CN109115226A - The paths planning method of multirobot conflict avoidance based on jump point search - Google Patents
The paths planning method of multirobot conflict avoidance based on jump point search Download PDFInfo
- Publication number
- CN109115226A CN109115226A CN201811017251.5A CN201811017251A CN109115226A CN 109115226 A CN109115226 A CN 109115226A CN 201811017251 A CN201811017251 A CN 201811017251A CN 109115226 A CN109115226 A CN 109115226A
- Authority
- CN
- China
- Prior art keywords
- node
- jump point
- point
- path
- conflict
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 88
- 238000001514 detection method Methods 0.000 claims abstract description 14
- 230000004888 barrier function Effects 0.000 claims description 13
- 238000004080 punching Methods 0.000 claims description 6
- 238000010845 search algorithm Methods 0.000 claims description 6
- 239000011159 matrix material Substances 0.000 claims description 3
- 230000000644 propagated effect Effects 0.000 claims description 3
- 238000009966 trimming Methods 0.000 claims description 3
- 235000013399 edible fruits Nutrition 0.000 claims 1
- 230000008520 organization Effects 0.000 abstract description 8
- 238000013473 artificial intelligence Methods 0.000 abstract description 2
- 230000002123 temporal effect Effects 0.000 description 5
- 241000238876 Acari Species 0.000 description 1
- 101001095088 Homo sapiens Melanoma antigen preferentially expressed in tumors Proteins 0.000 description 1
- 102100037020 Melanoma antigen preferentially expressed in tumors Human genes 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010411 cooking Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0287—Control of position or course in two dimensions specially adapted to land vehicles involving a plurality of land vehicles, e.g. fleet or convoy travelling
- G05D1/0289—Control of position or course in two dimensions specially adapted to land vehicles involving a plurality of land vehicles, e.g. fleet or convoy travelling with means for avoiding collisions between vehicles
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Automation & Control Theory (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Aviation & Aerospace Engineering (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention belongs to artificial intelligence fields, and in particular to a kind of paths planning method of the multirobot conflict avoidance based on jump point search；It specifically comprises the following steps: 1, pre-processes map for data map；2, path planning is carried out for all agencies using the search framework of CBS, that is, Conflict-based Search and in conjunction with without constraint jump point searching algorithm；3, traversal multiway tree carries out collision detection, increases constraint and carries out constrained path planning to all agencies.The path planning searching algorithm of conflict avoidance proposed by the present invention based on jump point search, by the quantity for largely reducing expanding node during planning path, it improves systems organization and goes out all search efficiencies for acting on behalf of collision-free path sequence algorithm, the actual distance that current node is reached home is directly searched out by EfftiveG table when processing conflict searches again for path, it again reduces systems organization and goes out all times for acting on behalf of collision-free path sequence, and make the shortest path of search closer to true path.
Description
Technical field
The invention belongs to artificial intelligence fields, and in particular to a kind of road of the multirobot conflict avoidance based on jump point search
Diameter planing method.
Background technique
Multirobot is widely used in multiple fields at present, such as: logistics storage, search and rescue, mine exploration, land mine
Remove and other various dangerous or uninteresting tasks, wherein robot not only needed during execution task around
Some barrier regions are crossed, and need to guarantee to collide between robot.By existing literature it is found that each robot can
To be considered as an agency, then the optimal path for quickly cooking up Lothrus apterus for multiple robots is that the planning of proxy path more than one is asked
Topic.
When only existing one and acting on behalf of, problem can usually use A* algorithm, dijkstra's algorithm, Floyed algorithm scheduling algorithm
Quickly cook up optimal path.But when there are multiple act on behalf of, can there are problems that more proxy resources conflicts.More agency associations
Making pathfinding i.e. MAPE is the non conflicting path gone out from current state to dbjective state for multiple agent plans.At present, more agency associations
Make pathfinding problem and obtains extensive research in multiple fields.
For MAPF problem, basic thought is the path for going out Lothrus apterus for all agent plans, for the path of conflict
Certain Conflict solving method is taken to solve to conflict.Wherein, Cooperative A* algorithm utilizes " reservation table " and time to constitute
A kind of storage table store the unit path in more deputy environments, by constantly searching the reservation table of each unit avoid rushing
It is prominent.Wherein, " reservation table " refers to the container either set for storing all proxy path sequences.But works as and existed
When acting on behalf of, generating deadlock situation, (i.e. system can not return to all path sequences for acting on behalf of Lothrus apterus as a result, at this time may more
Significantly slow down search speed.In order to avoid deadlock situation, Sharon et al. was proposed a kind of based on the more of conflict tree in 2015
The optimal pathfinding algorithm of proxy collaboration, the conflict in the way of multiway tree processing between effective solution agency, as all agencies
It when the path sequence set avoided there is no Lothrus apterus, returns without solution, when there are collision-free path sequence results, returns optimal
Collision-free path sequence results.But the A* algorithm that this method is utilized in underlying search generates during searching route
Expanding node it is more, reduce the search efficiency that systems organization goes out the collision-free path sequence of all agencies, wherein extension section
It puts and refers to during searching for single proxy path sequence, the subsequent node that will be traversed since starting point.Through retrieving,
The patent of the existing paths planning method about conflict avoidance is not directed to jump point searching algorithm, and the method used with the present invention is not
Together.
Summary of the invention
The purpose of the present invention is to provide it is a kind of based on jump point search multirobot conflict avoidance paths planning method,
It is able to solve more agent conflicts and avoids searching algorithm time complexity is higher, systems organization collision-free path efficiency is lower from asking
Topic.
A kind of paths planning method of the multirobot conflict avoidance based on jump point search, specifically comprises the following steps:
Step 1 pre-processes map for data map；
Step 2 is calculated using the search framework and combination of CBS, that is, Conflict-based Search without constraint jump point search
Method is that all agencies carry out path planning；
Step 3, traversal multiway tree carry out collision detection, increase constraint and carry out constrained path rule to all agencies
It draws.
It is described it is a kind of based on jump point search multirobot conflict avoidance paths planning method, step 1 specifically include as
Lower step:
Step 1.1, input map scene information, map scene information are comprising the barrier letter in map actual scene
The 0-1 two-dimensional matrix dimension of breath is M*N, and the value of each node is 0 or 1, and wherein barrier is indicated with 1, non-barrier
Region is indicated with 0；
All main jump points in step 1.2, label map；For all nodes in map, according to from left to right, from upper
The method of searching loop under judges whether each node has in basic orientation and forces neighbours, forces neighbour if had
It occupies and then this node is arranged as main jump point, and be added in main jump point set Primary；Otherwise, continue to traverse other nodes；Wherein
The forced neighbours refer to calculate current node to some point p apart from when, work as if the neighbor node of p will affect for barrier
For preceding node to the travel distance of node p, then current node, which has, forces neighbours；
Step 1.3, on the basis of step 1.2, the basic direction i.e. four corners of the world direction of all nodes is traversed, if worked as
Preceding node has main jump point in its basic orientation, then this node is direct jump point, and is added to direct jump point set
In Straight；Otherwise, continue to traverse other nodes；
Step 1.4, on the basis of step 1.3, the diagonal of all nodes is traversed, if in pair of current node
On linea angulata direction or the both horizontally and vertically upper of diagonally adjacent node has main jump point or direct jump point, then sets
Setting this point is diagonal line jump point, and is added in the set Diagonal of diagonal line jump point；Otherwise, continue to traverse other nodes；
Step 1.5, all nodes of traversal, record each node its basic orientation and diagonal apart from main jump point or
The distance of the direct jump point of person, for not comprising range information, i.e., being jumped apart from main jump point, directly in 8 directions of current node
The information of point or diagonal line jump point, then the range information apart from wall is added in these directions of jump point thus；
Step 1.6, output data map map, wherein each node in data map includes basic orientation and diagonal line
The range information of the main jump point of the distance in direction, direct jump point and diagonal line jump point.
A kind of paths planning method of multirobot conflict avoidance based on jump point search, is obtaining data map
After map, first with jump point searching algorithm all agencies are carried out with all extension sections in reverse search and store path sequence
Distance of the point apart from original equipment manufacturer, then for all agencies, using jump point searching algorithm, forward direction cooks up optimal path again, and
By in the path storage to multi-fork root vertex of all agencies, step 2 is then carried out.
It is described it is a kind of based on jump point search multirobot conflict avoidance paths planning method, step 2 specifically include as
Lower step:
Starting point is added in open table first for step 2.1, and open table is used to store the currently section that was not traversed also
Point；
Step 2.2 judges whether open table is empty, if being not sky, continues step 2.3, otherwise continues step 2.7；
Step 2.3 selects the smallest node of F value as current node from open table, and it is removed from open table,
It is added in close table；Above-mentioned F=G+H, wherein G represents minimum range of the start node apart from current node, and H represents current
Distance of the node apart from terminal；And open table is to store the node not extended also, close table has been propagated through for storing
Node；Above-mentioned close table refers to that the expanding node being traversed, subsequent step will not be extended again；Wherein extension section
It puts and refers to during searching for single proxy path sequence, all neighbor nodes that will be traversed since starting point；
Step 2.4, according to obtained data map map, obtain the descendant node set S of current node, it is mentioned above
Descendant node refers to the jump point set that can be reached for current node in its basic orientation and diagonal；
Step 2.5, descendant node information update are updated the information such as G value and father node to all descendant nodes, and will
The G value of each node is stored into EffectiveG table；
Step 2.6 judges terminal whether in open table, if entering step 2.7, otherwise return step 2.1；
The path sequence of the agency cooked up is stored in multi-fork root vertex by step 2.7.
A kind of paths planning method of multirobot conflict avoidance based on jump point search, subsequent section in step 2.5
The method of point information update specifically comprises the following steps:
Step 2.5.1, it determines the descendant node in current node basic orientation, and judges the pass of current node and terminal
System, if terminal not in its basic orientation or diagonally adjacent, and not in the centre of crucial jump point and present node, then by it
Otherwise terminal is added as descendant node in all key jump points, the father node of final updating descendant node is current node, and more
New G value；
Step 2.5.2, the diagonally adjacent descendant node of current node is determined, if terminal is in the straight line of diagonal
Above and not between its current node and crucial jump point, then open is added using next jump point of current node as descendant node
Otherwise open table is added in terminal by table, the father node of final updating descendant node is current node, and updates G value；
Step 2.5.3, the descendant node between current node basic orientation and diagonal is determined, if terminal is in base
When among this direction and diagonal, judges whether diagonally adjacent descendant node has in its basic orientation and force
Node, if there is open table then is added using its jump point as descendant node, the father node of final updating descendant node is current knot
Point, and update G value.
It is described it is a kind of based on jump point search multirobot conflict avoidance paths planning method, step 3 specifically include as
Lower step:
Step 3.1, traversal multi-fork root vertex to proxy path sequence carry out collision detection, when detecting path conflict with
Every two agency is one group, carries out vertex conflict using timing node to the path between one group of agency respectively and side conflict is examined
It surveys, then adds point constraint if there is conflict for it and side constrains, point constraint form is (ai,(vx,vy), t), represent generation
Manage aiPosition (v cannot be in time tx,vy) at；And side conflict represents two agencies and cannot go in the same direction, that is, acts on behalf of ai?
T moment acts on behalf of a from the n of the position in-position mjFrom the m of the position in-position n, two agencies are conflicted at this time, then
Adding side constraint for two agencies is (ai, n, t+1) and (aj, m, t+1), after detecting conflict and generating constraint, to there is punching
The agency in prominent path carries out planning path again using constrained jump point searching algorithm, and algorithm is specifically such as following steps；
Step 3.2, for generate conflict agency, first will agency starting point be added open table in；
Step 3.3 judges whether open table is sky, if it is sky, carries out step 3.10, otherwise, continues step 3.4；
Step 3.4 selects the smallest node of F value as current node from open table, and it is removed from open table,
It is added in close table, wherein H value is obtained by the method for directly traversing EffectiveG table, reduces systems organization in this way
All times for acting on behalf of collision-free path sequence out, and make the shortest path of search closer to true path；
Step 3.5, according to data map map, obtain the successor set S of current node；
Step 3.6, constraint condition judgement, judge whether each descendant node the constraint condition for meeting this agency,
If it is satisfied, then being added without open table and continuing to judge the latter descendant node；If conditions are not met, then continuing step 3.7；
Step 3.7 is updated the information such as G value and father node to all descendant nodes, in specific method and step 2.5
Method is identical；
Step 3.8 adds wait state to the current node with descendant node, between the agency for preventing conflict
Collision-free path must can be just cooked up by waiting, can not only effectively solve the conflict between agency in this way, additionally it is possible to be
Agency provides the selection of a variety of collision-free paths, if priority is different between client, then return when the conflict occurs priority compared with
High agency preferentially passes through the path sequence set of conflict point；Such as: if act on behalf of A ratio act on behalf of B priority it is high, i.e., ought act on behalf of A
It is first passed through with A needs when acting on behalf of B and clashing, are acted on behalf of, then acts on behalf of B and need to wait, finally then selected one to act on behalf of A and first pass through punching
The collision-free path arrangement set of bump；On the contrary, if act on behalf of B ratio act on behalf of A priority it is high, final choice one act on behalf of B elder generation
Pass through the collision-free path arrangement set of conflict point；The present invention does not consider issue of priority between agency, what final result returned
It is the smallest path sequence set of path sequence temporal summation of all agencies；
Step 3.9 judges terminal whether in open table, if entering step 3.10, otherwise return step 3.4；
The path sequence of agency is stored in multiway tree present node by step 3.10, is at this time multiway tree leaf node；
Step 3.11 after completing above-mentioned steps 3.2-3.10, that is, traverses and completes all multi-fork tree nodes, at this time multiway tree
Each branch node be leaf node, then compare the path time summation of all agencies of all leaf nodes, finally return
Return the smallest path sequence set of temporal summation.
A kind of paths planning method of multirobot conflict avoidance based on jump point search, constrains item in step 3.6
Part judgment method specifically comprises the following steps:
Step 3.6.1, compare tIt is subsequentWith tConstraintIf tIt is subsequent< tConstraint, then false is returned；That is tIt is subsequentRepresent descendant node when
Between, tConstraintRepresent the time of obligatory point；
If step 3.6.2, tIt is subsequent=tConstraint, continue the Agent ID and coordinate information that judge descendant node and obligatory point, if
It is all identical, true is returned, false is otherwise returned；
If step 3.6.3, tIt is subsequent=tConstraint, calculated according to the time of obligatory point be in current knot at the same moment first
The coordinate information of coordinate points ConstrainT between point and successor node, is then further continued for judging ConstrainT and obligatory point
Coordinate information, act on behalf of id information, if all it is identical, return to true, otherwise return to false.
A kind of paths planning method of multirobot conflict avoidance based on jump point search, the search based on CBS are calculated
Method is broadly divided into two layers of searching algorithm, including underlying search algorithm and top layer searching algorithm, and wherein underlying search algorithm refers to rule
The path sequence individually acted on behalf of is drawn as a result, it is possible to effectively reduce the interstitial content extended in search process；Top layer searching algorithm energy
It enough avoids detection from conflicting and generates corresponding constraint, finally cook up the path sequence of Lothrus apterus.
A kind of paths planning method of multirobot conflict avoidance based on jump point search, the search based on CBS are calculated
The detection conflict of top layer searching algorithm includes the vertex conflict and side conflict between detection proxy path sequence in method, wherein vertex
Conflict refers to that two agencies cannot be in same position in synchronization, and the point constraint of addition refers to that constraint type is (ai,
(vx,vy), t), act on behalf of aiPosition plane coordinate (v cannot be in time tx,vy) at；And side conflict represents two generations
Reason cannot go in the same direction, that is, act on behalf of aiIn t moment at the n of the position in-position m, and act on behalf of ajFrom the m of the position in-position n, this
When two agencies conflicted, then be that add sides constraint be (a by two agenciesi, n, t+1) and (aj,m,t+1)。
A kind of paths planning method of multirobot conflict avoidance based on jump point search, the search based on CBS are calculated
Process comprising an achievement in method creates the root node of a multiway tree first, and first time is utilized jump point searching algorithm
The path sequence for all agencies being planned for all is stored into root node, and the present node of multiway tree is root node at this time；Then
When detecting the conflict between agency, if detecting conflict, a multi-fork tree node is newly created, and as current tree node
Child nodes, current tree node at this time is child nodes, and the proxy path sequence results planned again are then stored in this
When current tree node in, the circulation above process is until traverse path Lothrus apterus between all agencies, i.e. present tree at this time
Node is leaf node, and traversal is completed to mean that the achievement process of multiway tree terminates.
A kind of paths planning method of multirobot conflict avoidance based on jump point search, jump point searching algorithm is one
The paths planning method by trimming the redundancy expanding node of partial invalidity is planted, and above-mentioned jump point refers to working as from map
The effective expanding node of the extension of preceding point selectivity.
The beneficial effects of the present invention are:
The path planning searching algorithm of conflict avoidance proposed by the present invention based on jump point search, by largely reducing planning
The quantity of expanding node in path process improves systems organization and goes out all search effects for acting on behalf of collision-free path sequence algorithm
Rate, one kind proposed by the present invention are whole using present node distance in reverse search record expansion process when to act on behalf of search for the first time
The method of the actual distance of point directly searches out current node by EfftiveG table when processing conflict searches again for path and arrives
It up to the actual distance of terminal, again reduces systems organization and goes out all times for acting on behalf of collision-free path sequence, and to search
The shortest path of rope is closer to true path.It is proposed by the present invention a kind of to judge whether descendant node meets about according to timing node
The method of beam, and the method to add wait state with the current node of expansible descendant node, can not only effectively solve
The certainly conflict between agency, and the selection of a variety of collision-free path arrangement sets can be provided for agency, so that agency can root
A collision-free path is selected according to different demands.
Detailed description of the invention
Fig. 1 is the path planning algorithm flow chart for the more agent conflicts searched for based on jump point；
Fig. 2 is the pretreatment map data information process of the paths planning method for the more agent conflicts searched for based on jump point
Figure；
Fig. 3 is the example of the pretreatment map data information of the paths planning method for the more agent conflicts searched for based on jump point
Figure；
Fig. 4 is the path planning algorithm flow chart based on unconfined jump point searching algorithm；
Fig. 5 is the descendant node information updating method flow chart based on unconfined jump point searching algorithm；
Fig. 6 is the instance graph of the path planning algorithm based on unconfined jump point searching algorithm；
Fig. 7 is based on constrained jump point searching algorithm path planning process figure；
Fig. 8 is the judgment method the flow chart whether constraint condition based on constrained jump point searching algorithm meets；
Fig. 9 is the instance graph based on constrained jump point searching algorithm path planning.
Specific embodiment
The present invention is described further with reference to the accompanying drawing:
More agent conflicts based on jump point search JPS (Jump point search) avoid paths planning method, it is intended to solve
Certainly more agent conflicts avoid the problem that searching algorithm time complexity is higher, systems organization collision-free path efficiency is lower.It is led
Wanting thought is the map scene information according to input, search framework and combination using CBS (Conflict-based Search)
Bottom jump point searching algorithm goes out the path of collision-free path and all agencies of final output accurately quickly for more agent plans
The smallest optimal path of temporal summation.
The above-mentioned searching algorithm based on CBS being previously mentioned is broadly divided into two layers of searching algorithm, including underlying search algorithm and
Top layer searching algorithm, wherein underlying search algorithm, which refers to, plans the path sequence individually acted on behalf of as a result, it is possible to effectively reduce search
The interstitial content extended in the process；And the purpose of top layer searching algorithm is to avoid detection from conflicting and generate corresponding constraint, is finally advised
Mark the path sequence of Lothrus apterus.
Wherein top layer searching algorithm above-mentioned detection conflict be further characterized in that: it include detection proxy path sequence it
Between vertex conflict and side conflict.Wherein vertex conflict above-mentioned refers to that two agencies cannot be in same position in synchronization
It sets, the point constraint of addition refers to that constraint type is (ai,(vx,vy), t), act on behalf of aiPosition cannot be in time t
Horizontalization areal coordinate (vx,vy) at.And side conflict above-mentioned represents two agencies and cannot go in the same direction, that is, acts on behalf of aiIn t moment from position
It sets at the n of the in-position m, and acts on behalf of ajFrom the m of the position in-position n, two agencies are conflicted at this time, then are acted on behalf of for two
Adding side constraint is (ai, n, t+1) and (aj,m,t+1)。
The feature of searching algorithm above-mentioned based on CBS further include: include an achievement in the searching algorithm based on CBS
Process, create the root node of a multiway tree, and all agencies that first time is planned for using jump point searching algorithm first
Path sequence all store into root node, at this time the present node of multiway tree be root node；Then between detection agency
When conflict, if detecting conflict, a multi-fork tree node, and the child nodes as current tree node are newly created, at this time
Current tree node is child nodes, and the proxy path sequence results planned again are then stored in current tree node at this time
In.The circulation above process is until traverse the path Lothrus apterus between all agencies, i.e., current tree node is leaf node at this time,
Traversal is completed to mean that the achievement process of multiway tree terminates.
Jump point searching algorithm above-mentioned is a kind of path planning side by trimming the redundancy expanding node of partial invalidity
Method.And above-mentioned jump point refers to the effective expanding node of the extension of the selectivity of the current point from map.
Fig. 1 is the whole implementation process of the paths planning method of the conflict avoidance based on jump point searching algorithm.Firstly, processing
Map scene is data map map, i.e., each node processing in map goes out in its basic orientation and diagonal respectively
On can directly and key point jump point, that is to say, that in its basic orientation and diagonal line side when each node is as current node
To the jump point that can be reached.Secondly, being cooked up according to the demand utilization jump point searching algorithm of the starting point of each agency and terminal
Optimal path, and in planning path for the first time, the node first by Origin And Destination reverse search, in record path sequence process
Actual distance apart from terminal, i.e. EffectiveG table, so as to can be with when other nodes of multiway tree have carried out Constrain Searching
EffectiveG table is directly consulted, further reduces the time of planning path, and path sequence is stored in multi-fork tree root section
Point in.Then, multi-fork root vertex is traversed, detects whether all proxy path sequences in root node include vertex conflict and side punching
It is prominent, if having conflict path sequence, generates new multi-fork tree node and add corresponding constraint to each agency, finally
It is that agent plan goes out optimal path using constrained jump point searching algorithm.Until traversing leaf node, i.e., there is no conflicts
Until, and finally return that the time series and shortest path sequence result of all agencies.
Fig. 2 illustrates the flow chart of preprocessed data map.Specifically comprise the following steps:
Step 1.1, input map scene information, map scene information are comprising the barrier letter in map actual scene
The 0-1 two-dimensional matrix dimension of breath is M*N, and the value of each node is 0 or 1, and wherein barrier is indicated with 1, non-barrier
Region is indicated with 0；
All main jump points in step 1.2, label map；For all nodes in map, according to from left to right, from upper
The method of searching loop under judges whether each node has in basic orientation and forces neighbours, forces neighbour if had
It occupies and then this node is arranged as main jump point, and be added in main jump point set Primary；Otherwise, continue to traverse other nodes；Wherein
Forced neighbours above-mentioned refer to calculate current node to some point p apart from when, work as if the neighbor node of p will affect for barrier
For preceding node to the travel distance of node p, then current node, which has, forces neighbours；
Step 1.3, on the basis of step 1.2, the basic direction i.e. four corners of the world direction of all nodes is traversed, if worked as
Preceding node has main jump point in its basic orientation, then this node is direct jump point, and is added to direct jump point set
In Straight；Otherwise, continue to traverse other nodes；
Step 1.4, on the basis of step 1.3, the diagonal of all nodes is traversed, if in pair of current node
On linea angulata direction or the both horizontally and vertically upper of diagonally adjacent node has main jump point or direct jump point, then sets
Setting this point is diagonal line jump point, and is added in the set Diagonal of diagonal line jump point；Otherwise, continue to traverse other nodes；
Step 1.5, all nodes of traversal, record each node its basic orientation and diagonal apart from main jump point or
The distance of the direct jump point of person, for not comprising range information, i.e., being jumped apart from main jump point, directly in 8 directions of current node
The information of point or diagonal line jump point, then the range information apart from wall is added in these directions of jump point thus；
Step 1.6, output data map map, wherein each node in data map includes basic orientation and diagonal line
The range information of the main jump point of the distance in direction, direct jump point and diagonal line jump point.
The map scene of a 6*6 is illustrated in Fig. 3, includes certain barrier, is every according to above-mentioned steps
Its distance value of basic direction and diagonal apart from its key point of a vertex ticks, is data map map as shown in the figure
All data informations.
Fig. 4 illustrates the flow chart for going out optimal path for all agent plans using jump point searching algorithm.Specifically include as
Lower step:
Starting point is added in open table first for step 2.1, and open table is used to store the currently section that was not traversed also
Point, and in the first step using jump point search by enabling to planning for the process of starting point and terminal reverse search
Path is more acurrate；
Step 2.2 judges whether open table is empty, if being not sky, continues step 2.3, otherwise continues step 2.7；
Step 2.3 selects the smallest node of F value as current node from open table, and it is removed from open table,
It is added in close table；Above-mentioned F=G+H, wherein G represents minimum range of the start node apart from current node, and H represents current
Distance of the node apart from terminal；And open table is to store the node not extended also, close table has been propagated through for storing
Node；Above-mentioned close table refers to that the expanding node being traversed, subsequent step will not be extended again；Wherein extension section
It puts and refers to during searching for single proxy path sequence, all neighbor nodes that will be traversed since starting point；
Step 2.4, according to obtained data map map, obtain the descendant node set S of current node, it is mentioned above
Descendant node refers to the jump point set that can be reached for current node in its basic orientation and diagonal；
Step 2.5, descendant node information update are updated the information such as G value and father node to all descendant nodes, and will
The G value of each node is stored into EffectiveG table；
Step 2.6 judges terminal whether in open table, if entering step 2.7, otherwise return step 2.1；
The path sequence of the agency cooked up is stored in multi-fork root vertex by step 2.7.
Fig. 5 is the descendant node information updating method for including in step 2.5 in Fig. 4, mainly from three kinds of Directional Extensions
Descendant node, in its basic orientation for, its descendant node is determined according to the basic orientation of current node, and judge current knot
The relationship of point and terminal, if terminal is not in the centre of its basic orientation or diagonally adjacent crucial jump point and present node
Then using its all crucial jump point as descendant node, terminal is otherwise added, father node and G value final and that update descendant node.
For diagonally adjacent, if terminal on the straight line of diagonal and not its current node and crucial jump point it
Between, then open table is added using next jump point of current node as descendant node, open table otherwise is added in terminal.If terminal
When among basic orientation and diagonal, judge whether diagonally adjacent descendant node has in its basic orientation
Node is forced, if there is open table then is added using its jump point as descendant node, and updates the father node and G value of descendant node.
In Fig. 6, simulation is map scene in a 6*6, acts on behalf of A and acts on behalf of B with respective starting point and end
Point according to the data map obtained, and combines unconfined jump point searching algorithm, the optimal path sequence cooked up for it
Column.
Fig. 7 is illustrated, and when detecting between proxy path sequence comprising conflict, jump point is utilized under the premise of constrained
The flow chart of collision-free path is cooked up in search.Specifically comprise the following steps:
Step 3.1, traversal multi-fork root vertex to proxy path sequence carry out collision detection, when detecting path conflict with
Every two agency is one group, carries out vertex conflict using timing node to the path between one group of agency respectively and side conflict is examined
It surveys, then adds point constraint if there is conflict for it and side constrains, point constraint form is (ai,(vx,vy), t), represent generation
Manage aiPosition (v cannot be in time tx,vy) at；And side conflict represents two agencies and cannot go in the same direction, that is, acts on behalf of ai?
T moment acts on behalf of a from the n of the position in-position mjFrom the m of the position in-position n, two agencies are conflicted at this time, then
Adding side constraint for two agencies is (ai, n, t+1) and (aj, m, t+1), after detecting conflict and generating constraint, to there is punching
The agency in prominent path carries out planning path again using constrained jump point searching algorithm, and algorithm is specifically such as following steps；
Step 3.2, for generate conflict agency, first will agency starting point be added open table in；
Step 3.3 judges whether open table is sky, if it is sky, carries out step 3.10, otherwise, continues step 3.4；
Step 3.4 selects the smallest node of F value as current node from open table, and it is removed from open table,
It is added in close table, wherein H value can be obtained by the method for directly traversing EffectiveG table, reduce system in this way
All times for acting on behalf of collision-free path sequence are cooked up, and make the shortest path of search closer to true path；
Step 3.5, according to data map map, obtain the successor set S of current node；
Step 3.6, constraint condition judgement, judge whether each descendant node the constraint condition for meeting this agency,
If it is satisfied, then being added without open table and continuing to judge the latter descendant node；If conditions are not met, then continuing step 3.7；
Step 3.7 is updated the information such as G value and father node to all descendant nodes, in specific method and step 2.5
Method is identical；
Step 3.8 adds wait state to the current node with descendant node, between the agency for preventing conflict
Collision-free path must can be just cooked up by waiting, can not only effectively solve the conflict between agency in this way, additionally it is possible to be
Agency provides the selection of a variety of collision-free paths, if priority is different between client, then return when the conflict occurs priority compared with
High agency preferentially passes through the path sequence set of conflict point；Such as: if act on behalf of A ratio act on behalf of B priority it is high, i.e., ought act on behalf of A
It is first passed through with A needs when acting on behalf of B and clashing, are acted on behalf of, then acts on behalf of B and need to wait, finally then selected one to act on behalf of A and first pass through punching
The collision-free path arrangement set of bump；On the contrary, if act on behalf of B ratio act on behalf of A priority it is high, final choice one act on behalf of B elder generation
Pass through the collision-free path arrangement set of conflict point；The present invention does not consider issue of priority between agency, what final result returned
It is the smallest path sequence set of path sequence temporal summation of all agencies；
Step 3.9 judges terminal whether in open table, if entering step 3.10, otherwise return step 3.4；
The path sequence of agency is stored in multiway tree present node by step 3.10, is at this time multiway tree leaf node；
Step 3.11 after completing above-mentioned steps 3.2-3.10, that is, traverses and completes all multi-fork tree nodes, at this time multiway tree
Each branch node be leaf node, then compare the path time summation of all agencies of all leaf nodes, finally return
Return the smallest path sequence set of temporal summation.
Fig. 8 is the constraint item for the more agent conflict avoiding methods based on jump point searching algorithm for including in the step 3.6 of Fig. 7
Part judgment method flow chart, mainly according to timing node to determine whether meeting constraint condition.If the time of current jump point < about
The time of beam spot returns to false；If time=obligatory point time of current jump point, continuing to judge Agent ID and coordinate, if
It is all identical, true is returned, false is otherwise returned；If time > obligatory point time of current jump point, first according to obligatory point
Time calculate the coordinate of current node when at the same time, be then further continued for judging coordinate, intelligent body ID, if all
It is identical, then true is returned, false is otherwise returned.In Fig. 9, A is acted on behalf of with B is acted on behalf of, conflict (t=in vertex has occurred at node D
3 moment acted on behalf of A and acted on behalf of B while present in the D of position), then B is acted on behalf of in the time of E point one grid of waiting, then by allowing
Acting on behalf of A and acting on behalf of B would not clash at node D, and path is more excellent.
Claims (10)
1. it is a kind of based on jump point search multirobot conflict avoidance paths planning method, which is characterized in that specifically include as
Lower step:
Step 1 pre-processes map for data map；
Step 2, using CBS, that is, Conflict-based Search search framework and combine without constraint jump point searching algorithm be
All agencies carry out path planning；
Step 3, traversal multiway tree carry out collision detection, increase constraint and carry out constrained path planning to all agencies.
2. a kind of paths planning method of the multirobot conflict avoidance based on jump point search according to claim 1, special
Sign is that the step 1 specifically comprises the following steps:
Step 1.1, input map scene information, map scene information are comprising the obstacle information in map actual scene
0-1 two-dimensional matrix dimension is M*N, and the value of each node is 0 or 1, and wherein barrier is indicated with 1, non-barrier region
It is indicated with 0；
All main jump points in step 1.2, label map；For all nodes in map, according to from left to right, from top to bottom
Searching loop method, judge each node in basic orientation whether have force neighbours, if having force neighbours if
This node is arranged as main jump point, and is added in main jump point set Primary；Otherwise, continue to traverse other nodes；Wherein force
Neighbours refer to calculate current node to some point p apart from when, if the neighbor node of p will affect current node to saving for barrier
The travel distance of point p, then current node, which has, forces neighbours；
Step 1.3, on the basis of step 1.2, the basic direction i.e. four corners of the world direction of all nodes is traversed, if current knot
Point has main jump point in its basic orientation, then this node is direct jump point, and is added in direct jump point set Straight；
Otherwise, continue to traverse other nodes；
Step 1.4, on the basis of step 1.3, the diagonal of all nodes is traversed, if in the diagonal line of current node
On direction or the both horizontally and vertically upper of diagonally adjacent node has main jump point or direct jump point, then this is arranged
Point is diagonal line jump point, and is added in the set Diagonal of diagonal line jump point；Otherwise, continue to traverse other nodes；
Step 1.5, all nodes of traversal record each node in its basic orientation and diagonal apart from main jump point or directly
The distance for connecing jump point, in 8 directions of current node not comprising range information, i.e., apart from main jump point, direct jump point or
The information of person's diagonal line jump point, then the range information apart from wall is added in these directions of jump point thus；
Step 1.6, output data map map, wherein each node in data map includes basic orientation and diagonal
The main jump point of distance, direct jump point and diagonal line jump point range information.
3. a kind of paths planning method of the multirobot conflict avoidance based on jump point search according to claim 1, special
Sign is, after obtaining the data map map, carries out reverse search to all agencies first with jump point searching algorithm and stores
Then distance of all expanding nodes apart from original equipment manufacturer in path sequence utilizes jump point searching algorithm again for all agencies
Forward direction cooks up optimal path, and by the storage to multi-fork root vertex of the path of all agencies, then carries out step 2.
4. a kind of paths planning method of the multirobot conflict avoidance based on jump point search according to claim 1, special
Sign is that the step 2 specifically comprises the following steps:
Starting point is added in open table first for step 2.1, and open table is used to store the currently node that was not traversed also；
Step 2.2 judges whether open table is empty, if being not sky, continues step 2.3, otherwise continues step 2.7；
Step 2.3 selects the smallest node of F value as current node from open table, and it is removed from open table, is added
Into close table；Above-mentioned F=G+H, wherein G represents minimum range of the start node apart from current node, and H represents current node
Distance apart from terminal；And open table is to store the node not extended also, close table is for storing the section propagated through
Point；Above-mentioned close table refers to that the expanding node being traversed, subsequent step will not be extended again；Wherein expanding node
Refer to all neighbor nodes that will traverse since starting point during searching for single proxy path sequence；
The data map map that step 2.4, basis obtain, obtains the descendant node set S of current node, mentioned above is subsequent
Node refers to the jump point set that can be reached for current node in its basic orientation and diagonal；
Step 2.5, descendant node information update are updated the information such as G value and father node to all descendant nodes, and will be each
The G value of node is stored into EffectiveG table；
Step 2.6 judges terminal whether in open table, if entering step 2.7, otherwise return step 2.1；
The path sequence of the agency cooked up is stored in multi-fork root vertex by step 2.7.
5. a kind of path rule of the multirobot conflict avoidance based on jump point search according to claim 1 or described in claim 4
The method of drawing, which is characterized in that the method for descendant node information update described in step 2.5 specifically comprises the following steps:
Step 2.5.1, it determines the descendant node in current node basic orientation, and judges the relationship of current node and terminal, if
Terminal is not owned then not in its basic orientation or diagonally adjacent in the centre of crucial jump point and present node
Otherwise as descendant node terminal is added, the father node of final updating descendant node is current node, and updates G in crucial jump point
Value；
Step 2.5.2, determine the diagonally adjacent descendant node of current node, if terminal on the straight line of diagonal and
Not between its current node and crucial jump point, then open table is added using next jump point of current node as descendant node, it is no
Open table then is added in terminal, the father node of final updating descendant node is current node, and updates G value；
Step 2.5.3, the descendant node between current node basic orientation and diagonal is determined, if terminal is substantially square
When to among diagonal, judges whether diagonally adjacent descendant node has in its basic orientation and force section
Point, if there is open table then is added using its jump point as descendant node, the father node of final updating descendant node is current node,
And update G value.
6. a kind of paths planning method of the multirobot conflict avoidance based on jump point search according to claim 1, special
Sign is that the step 3 specifically comprises the following steps:
Step 3.1, traversal multi-fork root vertex carry out collision detection to proxy path sequence, when detecting path conflict with every two
A agency is one group, carries out vertex conflict and side collision detection using timing node to the path between one group of agency respectively, such as
There is conflict and then add point constraint and side constraint for it in fruit, point constraint form is (ai,(vx,vy), t), act on behalf of ai?
Position (v cannot be in when time tx,vy) at；And side conflict represents two agencies and cannot go in the same direction, that is, acts on behalf of aiIn t moment
From the n of the position in-position m, and act on behalf of ajFrom the m of the position in-position n, two agencies are conflicted at this time, then are two
Agency's addition side constraint is (ai, n, t+1) and (aj, m, t+1), after detecting conflict and generating constraint, to there is conflict path
Agency utilize constrained jump point searching algorithm to carry out planning path again, algorithm is specifically such as following steps；
Step 3.2, for generate conflict agency, first will agency starting point be added open table in；
Step 3.3 judges whether open table is sky, if it is sky, carries out step 3.10, otherwise, continues step 3.4；
Step 3.4 selects the smallest node of F value as current node from open table, and it is removed from open table, is added
Into close table, wherein H value is obtained by the method for directly traversing EffectiveG table；
Step 3.5, according to data map map, obtain the successor set S of current node；
Step 3.6, constraint condition judgement, judge whether each descendant node the constraint condition for meeting this agency, if
Meet, is then added without open table and continues to judge the latter descendant node；If conditions are not met, then continuing step 3.7；
Step 3.7 is updated the information such as G value and father node, method in specific method and step 2.5 to all descendant nodes
It is identical；
Step 3.8 adds wait state to the current node with descendant node；
Step 3.9 judges terminal whether in open table, if entering step 3.10, otherwise return step 3.4；
The path sequence of agency is stored in multiway tree present node by step 3.10, is at this time multiway tree leaf node；
Step 3.11 after completing above-mentioned steps 3.2-3.10, that is, traverses and completes all multi-fork tree nodes, multiway tree is every at this time
A branch node is all leaf node, then the path time summation of all agencies of all leaf nodes is compared, when finally returning that
Between the smallest path sequence set of summation.
7. a kind of path rule of the multirobot conflict avoidance based on jump point search according to claim 1 or described in claim 6
The method of drawing, which is characterized in that constraint condition judgment method described in step 3.6 specifically comprises the following steps:
Step 3.6.1, compare tIt is subsequentWith tConstraintIf tIt is subsequent< tConstraint, then false is returned；That is tIt is subsequentRepresent the time of descendant node, tConstraint
Represent the time of obligatory point；
If step 3.6.2, tIt is subsequent=tConstraint, continue the Agent ID and coordinate information that judge descendant node and obligatory point, if all
It is identical, true is returned, false is otherwise returned；
If step 3.6.3, tIt is subsequent=tConstraint, first according to the time of obligatory point calculate the same moment be in current node and
Then the coordinate information of coordinate points ConstrainT between successor node is further continued for judging the seat of ConstrainT and obligatory point
Mark information acts on behalf of id information, if all identical, return to true, otherwise returns to false.
8. a kind of paths planning method of the multirobot conflict avoidance based on jump point search according to claim 1, special
Sign is: the searching algorithm based on CBS is broadly divided into two layers of searching algorithm, including underlying search algorithm and top layer search are calculated
Method, wherein underlying search algorithm refers to that the path sequence that planning is individually acted on behalf of detects punching as a result, top layer searching algorithm can be avoided
It dashes forward and generates corresponding constraint, finally cook up the path sequence of Lothrus apterus.
9. a kind of paths planning method of the multirobot conflict avoidance based on jump point search according to claim 1, special
Sign is that the process comprising an achievement in the searching algorithm based on CBS creates the root node of a multiway tree first,
And the path sequence for all agencies that first time is planned for using jump point searching algorithm is all stored into root node, multi-fork at this time
The present node of tree is root node；Then when detecting the conflict between agency, if detecting conflict, a multi-fork is newly created
Tree node, and the child nodes as current tree node, current tree node at this time be child nodes, then will be planned again
Proxy path sequence results are stored in current tree node at this time, and the circulation above process is until traverse between all agencies
Path Lothrus apterus, i.e., current tree node is leaf node at this time, and traversal is completed to mean that the achievement process of multiway tree terminates.
10. a kind of paths planning method of the multirobot conflict avoidance based on jump point search according to claim 1, special
Sign is that the jump point searching algorithm is a kind of paths planning method by trimming the redundancy expanding node of partial invalidity,
And above-mentioned jump point refers to the effective expanding node of the extension of the selectivity of the current point from map.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811017251.5A CN109115226B (en) | 2018-09-01 | 2018-09-01 | Route planning method for avoiding multi-robot conflict based on jumping point search |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811017251.5A CN109115226B (en) | 2018-09-01 | 2018-09-01 | Route planning method for avoiding multi-robot conflict based on jumping point search |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109115226A true CN109115226A (en) | 2019-01-01 |
| CN109115226B CN109115226B (en) | 2021-11-19 |
Family
ID=64861717
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811017251.5A Active CN109115226B (en) | 2018-09-01 | 2018-09-01 | Route planning method for avoiding multi-robot conflict based on jumping point search |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109115226B (en) |
Cited By (33)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110006429A (en) * | 2019-03-20 | 2019-07-12 | 智慧航海（青岛）科技有限公司 | A kind of unmanned boat path planning method based on depth optimization |
| CN110006430A (en) * | 2019-03-26 | 2019-07-12 | 智慧航海（青岛）科技有限公司 | A kind of optimization method of Path Planning |
| CN110428688A (en) * | 2019-08-09 | 2019-11-08 | 郑州铁路职业技术学院 | A kind of rail traffic vehicles maintenance simulation training system based on virtual scene |
| CN110530390A (en) * | 2019-09-16 | 2019-12-03 | 哈尔滨工程大学 | A kind of non-particle vehicle path planning method under narrow environment |
| CN110849373A (en) * | 2019-11-28 | 2020-02-28 | 中国航空工业集团公司沈阳飞机设计研究所 | Man-machine real-time airway re-planning method |
| CN110967015A (en) * | 2019-11-20 | 2020-04-07 | 中国人民解放军国防科技大学 | Path planning method and system |
| CN110975290A (en) * | 2019-11-20 | 2020-04-10 | 中国人民解放军国防科技大学 | Path planning method and system based on pattern database |
| CN110975291A (en) * | 2019-11-20 | 2020-04-10 | 中国人民解放军国防科技大学 | Path extraction method and system |
| CN110975288A (en) * | 2019-11-20 | 2020-04-10 | 中国人民解放军国防科技大学 | Geometric container data compression method and system based on jumping point path search |
| CN111104471A (en) * | 2019-11-20 | 2020-05-05 | 中国人民解放军国防科技大学 | Mode database information compression method and system based on jumping point path search |
| CN111238519A (en) * | 2020-01-06 | 2020-06-05 | 华侨大学 | Multi-unmanned vehicle road finding method based on topological map and conflict elimination strategy |
| CN111422741A (en) * | 2020-03-24 | 2020-07-17 | 苏州西弗智能科技有限公司 | Method for planning movement path of bridge crane |
| CN111510427A (en) * | 2020-03-06 | 2020-08-07 | 杜晓楠 | Method of mitigating path creation attacks in I2P networks, computer readable storage medium and I2P network |
| CN111641729A (en) * | 2019-05-23 | 2020-09-08 | 北京航空航天大学 | Inter-domain path identification prefix conflict detection and decomposition method based on prefix tree |
| CN111736524A (en) * | 2020-07-17 | 2020-10-02 | 北京布科思科技有限公司 | Multi-robot scheduling method, device and equipment based on time and space |
| CN111829526A (en) * | 2020-07-23 | 2020-10-27 | 中国人民解放军国防科技大学 | Distance map reconstruction and jumping point path planning method based on anti-collision radius |
| CN112229419A (en) * | 2020-09-30 | 2021-01-15 | 隶元科技发展(山东)有限公司 | Dynamic path planning navigation method and system |
| CN112731929A (en) * | 2020-12-23 | 2021-04-30 | 浙江大学 | Ackerman model-based mobile robot obstacle avoidance path planning method |
| CN112835364A (en) * | 2020-12-30 | 2021-05-25 | 浙江大学 | Multi-robot path planning method based on conflict detection |
| CN112915541A (en) * | 2021-04-07 | 2021-06-08 | 腾讯科技（深圳）有限公司 | Jumping point searching method, device, equipment and storage medium |
| CN112947475A (en) * | 2021-03-22 | 2021-06-11 | 山东大学 | Laser navigation forklift type AGV vehicle-mounted system and method |
| CN113074728A (en) * | 2021-03-05 | 2021-07-06 | 北京大学 | Multi-AGV path planning method based on jumping point routing and collaborative obstacle avoidance |
| CN113311829A (en) * | 2021-05-11 | 2021-08-27 | 北京理工大学 | Multi-robot path planning method based on dynamic time window conflict search |
| CN113505922A (en) * | 2021-07-07 | 2021-10-15 | 福州大学 | Truss assembly sequence planning method based on parallel tree search |
| CN113515129A (en) * | 2021-08-23 | 2021-10-19 | 哈尔滨理工大学 | Bidirectional skip point search unmanned vehicle path planning method based on boundary search |
| CN113568411A (en) * | 2021-07-30 | 2021-10-29 | 深圳市新盒科技有限公司 | Machine trolley dispatching system for track map |
| CN113804197A (en) * | 2021-07-30 | 2021-12-17 | 深圳市新盒科技有限公司 | Multi-machine trolley path planning method based on complex track map |
| CN113885567A (en) * | 2021-10-22 | 2022-01-04 | 北京理工大学 | Multi-unmanned aerial vehicle collaborative path planning method based on conflict search |
| CN114326621A (en) * | 2021-12-25 | 2022-04-12 | 长安大学 | Group intelligent airport trolley dispatching method and system based on layered architecture |
| CN114564023A (en) * | 2022-03-11 | 2022-05-31 | 哈尔滨理工大学 | Jumping point search path planning method under dynamic scene |
| CN114705196A (en) * | 2022-06-07 | 2022-07-05 | 湖南大学 | Self-adaptive heuristic global path planning method and system for robot |
| CN115507858A (en) * | 2022-11-24 | 2022-12-23 | 青岛中德智能技术研究院 | Single-robot and multi-robot driving path navigation method |
| CN116486907A (en) * | 2023-01-10 | 2023-07-25 | 湖南工商大学 | Protein sequence tag sequencing method based on A star algorithm |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103557867A (en) * | 2013-10-09 | 2014-02-05 | 哈尔滨工程大学 | Three-dimensional multi-UAV coordinated path planning method based on sparse A-star search (SAS) |
| CN105955280A (en) * | 2016-07-19 | 2016-09-21 | Tcl集团股份有限公司 | Mobile robot path planning and obstacle avoidance method and system |
| CN106774347A (en) * | 2017-02-24 | 2017-05-31 | 安科智慧城市技术（中国）有限公司 | Robot path planning method, device and robot under indoor dynamic environment |
| CN108268971A (en) * | 2017-12-06 | 2018-07-10 | 腾讯科技（深圳）有限公司 | Searching method, device, processor and the electronic device in path |
-
2018
- 2018-09-01 CN CN201811017251.5A patent/CN109115226B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103557867A (en) * | 2013-10-09 | 2014-02-05 | 哈尔滨工程大学 | Three-dimensional multi-UAV coordinated path planning method based on sparse A-star search (SAS) |
| CN105955280A (en) * | 2016-07-19 | 2016-09-21 | Tcl集团股份有限公司 | Mobile robot path planning and obstacle avoidance method and system |
| CN106774347A (en) * | 2017-02-24 | 2017-05-31 | 安科智慧城市技术（中国）有限公司 | Robot path planning method, device and robot under indoor dynamic environment |
| CN108268971A (en) * | 2017-12-06 | 2018-07-10 | 腾讯科技（深圳）有限公司 | Searching method, device, processor and the electronic device in path |
Non-Patent Citations (3)
| Title |
|---|
| ARIEL FELNER .ET AL: "Adding Heuristics to Conflict-Based Search for Multi-Agent Path Finding", 《PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON AUTOMATED PLANNING AND SCHEDULING》 * |
| ELI BOYARSKI .ET AL: "ICBS: Improved Conflict-Based Search Algorithm for Multi-Agent Pathfinding", 《PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI)》 * |
| JASON TRAISH .ET AL: "Optimization Using Boundary Lookup Jump Point Search", 《IEEE TRANSACTIONS ON COMPUTATIONAL INTELLIGENCE AND AI IN GAMES》 * |
Cited By (52)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110006429A (en) * | 2019-03-20 | 2019-07-12 | 智慧航海（青岛）科技有限公司 | A kind of unmanned boat path planning method based on depth optimization |
| CN110006430B (en) * | 2019-03-26 | 2021-05-04 | 智慧航海（青岛）科技有限公司 | Optimization method of track planning algorithm |
| CN110006430A (en) * | 2019-03-26 | 2019-07-12 | 智慧航海（青岛）科技有限公司 | A kind of optimization method of Path Planning |
| CN111641729A (en) * | 2019-05-23 | 2020-09-08 | 北京航空航天大学 | Inter-domain path identification prefix conflict detection and decomposition method based on prefix tree |
| CN110428688A (en) * | 2019-08-09 | 2019-11-08 | 郑州铁路职业技术学院 | A kind of rail traffic vehicles maintenance simulation training system based on virtual scene |
| CN110530390A (en) * | 2019-09-16 | 2019-12-03 | 哈尔滨工程大学 | A kind of non-particle vehicle path planning method under narrow environment |
| CN110975288A (en) * | 2019-11-20 | 2020-04-10 | 中国人民解放军国防科技大学 | Geometric container data compression method and system based on jumping point path search |
| CN110975291A (en) * | 2019-11-20 | 2020-04-10 | 中国人民解放军国防科技大学 | Path extraction method and system |
| CN110975290A (en) * | 2019-11-20 | 2020-04-10 | 中国人民解放军国防科技大学 | Path planning method and system based on pattern database |
| CN111104471A (en) * | 2019-11-20 | 2020-05-05 | 中国人民解放军国防科技大学 | Mode database information compression method and system based on jumping point path search |
| CN110967015A (en) * | 2019-11-20 | 2020-04-07 | 中国人民解放军国防科技大学 | Path planning method and system |
| CN110975290B (en) * | 2019-11-20 | 2023-09-05 | 中国人民解放军国防科技大学 | Path planning method and system based on pattern database |
| CN110975291B (en) * | 2019-11-20 | 2023-11-10 | 中国人民解放军国防科技大学 | Path extraction method and system |
| CN110975288B (en) * | 2019-11-20 | 2023-08-29 | 中国人民解放军国防科技大学 | Geometric container data compression method and system based on jump point path search |
| CN111104471B (en) * | 2019-11-20 | 2023-03-31 | 中国人民解放军国防科技大学 | Mode database information compression method and system based on jumping point path search |
| CN110849373A (en) * | 2019-11-28 | 2020-02-28 | 中国航空工业集团公司沈阳飞机设计研究所 | Man-machine real-time airway re-planning method |
| CN110849373B (en) * | 2019-11-28 | 2023-07-21 | 中国航空工业集团公司沈阳飞机设计研究所 | Real-time route re-planning method for man-machine |
| CN111238519B (en) * | 2020-01-06 | 2022-05-03 | 华侨大学 | Multi-unmanned vehicle road finding method based on topological map and conflict elimination strategy |
| CN111238519A (en) * | 2020-01-06 | 2020-06-05 | 华侨大学 | Multi-unmanned vehicle road finding method based on topological map and conflict elimination strategy |
| CN111510427B (en) * | 2020-03-06 | 2022-02-11 | 杜晓楠 | Method for mitigating path creation attack in I2P network system, computer-readable storage medium, and I2P network system |
| CN111510427A (en) * | 2020-03-06 | 2020-08-07 | 杜晓楠 | Method of mitigating path creation attacks in I2P networks, computer readable storage medium and I2P network |
| CN111422741B (en) * | 2020-03-24 | 2022-02-11 | 苏州西弗智能科技有限公司 | Method for planning movement path of bridge crane |
| CN111422741A (en) * | 2020-03-24 | 2020-07-17 | 苏州西弗智能科技有限公司 | Method for planning movement path of bridge crane |
| CN111736524A (en) * | 2020-07-17 | 2020-10-02 | 北京布科思科技有限公司 | Multi-robot scheduling method, device and equipment based on time and space |
| CN111829526B (en) * | 2020-07-23 | 2022-05-10 | 中国人民解放军国防科技大学 | Distance map reconstruction and jumping point path planning method based on anti-collision radius |
| CN111829526A (en) * | 2020-07-23 | 2020-10-27 | 中国人民解放军国防科技大学 | Distance map reconstruction and jumping point path planning method based on anti-collision radius |
| CN112229419A (en) * | 2020-09-30 | 2021-01-15 | 隶元科技发展(山东)有限公司 | Dynamic path planning navigation method and system |
| CN112731929A (en) * | 2020-12-23 | 2021-04-30 | 浙江大学 | Ackerman model-based mobile robot obstacle avoidance path planning method |
| CN112835364A (en) * | 2020-12-30 | 2021-05-25 | 浙江大学 | Multi-robot path planning method based on conflict detection |
| CN113074728A (en) * | 2021-03-05 | 2021-07-06 | 北京大学 | Multi-AGV path planning method based on jumping point routing and collaborative obstacle avoidance |
| CN113074728B (en) * | 2021-03-05 | 2022-07-22 | 北京大学 | Multi-AGV path planning method based on jumping point routing and collaborative obstacle avoidance |
| CN112947475A (en) * | 2021-03-22 | 2021-06-11 | 山东大学 | Laser navigation forklift type AGV vehicle-mounted system and method |
| CN112915541A (en) * | 2021-04-07 | 2021-06-08 | 腾讯科技（深圳）有限公司 | Jumping point searching method, device, equipment and storage medium |
| CN113311829B (en) * | 2021-05-11 | 2022-04-08 | 北京理工大学 | Multi-robot path planning method based on dynamic time window conflict search |
| CN113311829A (en) * | 2021-05-11 | 2021-08-27 | 北京理工大学 | Multi-robot path planning method based on dynamic time window conflict search |
| CN113505922B (en) * | 2021-07-07 | 2023-08-01 | 福州大学 | Truss assembly sequence planning method based on parallel tree search |
| CN113505922A (en) * | 2021-07-07 | 2021-10-15 | 福州大学 | Truss assembly sequence planning method based on parallel tree search |
| CN113804197A (en) * | 2021-07-30 | 2021-12-17 | 深圳市新盒科技有限公司 | Multi-machine trolley path planning method based on complex track map |
| CN113568411A (en) * | 2021-07-30 | 2021-10-29 | 深圳市新盒科技有限公司 | Machine trolley dispatching system for track map |
| CN113568411B (en) * | 2021-07-30 | 2023-10-27 | 深圳市新盒科技有限公司 | Machine trolley scheduling system for track map |
| CN113515129B (en) * | 2021-08-23 | 2022-02-11 | 哈尔滨理工大学 | Bidirectional skip point search unmanned vehicle path planning method based on boundary search |
| CN113515129A (en) * | 2021-08-23 | 2021-10-19 | 哈尔滨理工大学 | Bidirectional skip point search unmanned vehicle path planning method based on boundary search |
| CN113885567B (en) * | 2021-10-22 | 2023-08-04 | 北京理工大学 | Collaborative path planning method for multiple unmanned aerial vehicles based on conflict search |
| CN113885567A (en) * | 2021-10-22 | 2022-01-04 | 北京理工大学 | Multi-unmanned aerial vehicle collaborative path planning method based on conflict search |
| CN114326621A (en) * | 2021-12-25 | 2022-04-12 | 长安大学 | Group intelligent airport trolley dispatching method and system based on layered architecture |
| CN114326621B (en) * | 2021-12-25 | 2023-11-14 | 长安大学 | Group intelligent airport consignment car scheduling method and system based on layered architecture |
| CN114564023A (en) * | 2022-03-11 | 2022-05-31 | 哈尔滨理工大学 | Jumping point search path planning method under dynamic scene |
| CN114564023B (en) * | 2022-03-11 | 2022-11-08 | 哈尔滨理工大学 | Jumping point search path planning method under dynamic scene |
| CN114705196A (en) * | 2022-06-07 | 2022-07-05 | 湖南大学 | Self-adaptive heuristic global path planning method and system for robot |
| CN115507858A (en) * | 2022-11-24 | 2022-12-23 | 青岛中德智能技术研究院 | Single-robot and multi-robot driving path navigation method |
| CN116486907A (en) * | 2023-01-10 | 2023-07-25 | 湖南工商大学 | Protein sequence tag sequencing method based on A star algorithm |
| CN116486907B (en) * | 2023-01-10 | 2024-04-30 | 湖南工商大学 | Protein sequence tag sequencing method based on A star algorithm |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109115226B (en) | 2021-11-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109115226A (en) | The paths planning method of multirobot conflict avoidance based on jump point search | |
| CN110006429A (en) | A kind of unmanned boat path planning method based on depth optimization | |
| CN109947120B (en) | Path planning method in warehousing system | |
| CN108896052A (en) | A kind of mobile robot smooth paths planing method under the environment based on DYNAMIC COMPLEX | |
| CN109543872A (en) | Multirobot is without parking maneuver method and system | |
| CN109839110A (en) | A kind of multiple target point path planning method based on quick random search tree | |
| CN106500697B (en) | LTL-A*-A* optimum path planning method suitable for dynamic environment | |
| CN102506849B (en) | Method for optimizing shortest path with restraint | |
| CN105652838A (en) | Multi-robot path planning method based on time window | |
| CN110196061A (en) | Based on the mobile robot global path planning method for improving ant group algorithm | |
| CN109164815A (en) | A kind of Autonomous Underwater Vehicle paths planning method based on improvement ant group algorithm | |
| CN110296704A (en) | A kind of path planning method based on Visual Graph modeling | |
| CN110333659A (en) | A kind of pilotless automobile local paths planning method based on improvement A star search | |
| CN110319829A (en) | Unmanned aerial vehicle flight path planing method based on adaptive polymorphic fusion ant colony algorithm | |
| CN109934388A (en) | One kind sorting optimization system for intelligence | |
| Atzmon et al. | Multi-train path finding | |
| CN115237135A (en) | Mobile robot path planning method and system based on conflict | |
| CN112683275A (en) | Path planning method of grid map | |
| CN107544502A (en) | A kind of Planning of Mobile Robot under known environment | |
| CN110275528A (en) | For the method for optimizing route of RRT algorithm improvement | |
| CN113703452A (en) | AGV path planning method for large-scale storage environment | |
| CN111598332A (en) | Workshop double-resource integrated scheduling method and system in intelligent manufacturing environment | |
| CN108871344A (en) | Soccer robot GGRRT paths planning method | |
| CN107423810A (en) | Solving job shop scheduling problem scheduling method and system based on camel group's algorithm | |
| CN116414139A (en) | Mobile robot complex path planning method based on A-Star algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |