CN110497403A - A kind of manipulator motion planning method improving two-way RRT algorithm - Google Patents
A kind of manipulator motion planning method improving two-way RRT algorithm Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1656—Programme controls characterised by programming, planning systems for manipulators
- B25J9/1664—Programme controls characterised by programming, planning systems for manipulators characterised by motion, path, trajectory planning
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Abstract
The invention discloses a kind of manipulator motion planning methods for improving two-way RRT algorithm, the two-way RRT algorithm of this method fusion tradition, introduce the searching mode of optimal father node and the connection type of more new node, greatly reduce the distance of robotic arm path planning, it is ensured that the search speed of two-way RRT algorithm and cook up preferably path；The target position of given mechanical arm simultaneously obtains obstacle information in environment, one collisionless path is gone out using improved two-way RRT algorithmic rule, mechanical arm moves to target position according to the path of planning, to complete mechanical arm based on the motion planning for improving two-way RRT algorithm.The present invention uses improved two-way RRT algorithm, can greatly reduce the path cost of planning, the path of an optimization is cooked up for mechanical arm, it is optimal for solving the problems, such as motion path not.This method is applicable not only to the motion planning research of mechanical arm under higher dimensional space, and can be applied to mobile robot field, has broad application prospects.
Description
Technical field
The present invention relates to manipulator motion planning field more particularly to a kind of manipulator motions for improving two-way RRT algorithm
Planing method.
Background technique
Robot motion planning is the basic problem of robot research field, i.e., in given initial position and target position
Between find the path for meeting constraint condition for robot.The robot motion planning method of early stage directly makees robot
Treat for particle, and mechanical arm be it is irregular, can not be therefore most of for moving directly as particle in state space
The motion planning method of robot can not be directly extended to mechanical arm.
The avoidance object of planning of mechanical arm is to cook up the optimal path of a meet demand, for this problem, has been mentioned
Artificial Potential Field Method, A* search method, genetic algorithm, C space law, ant group algorithm etc. are gone out, but these algorithms all have limitation.People
The problem of work potential field method is can not to avoid local minimum problem, and A* algorithm is theoretically time optimal, but the disadvantage is that it
Space increase be it is exponential other, ant group algorithm is substantially parallel algorithm, it problem space multiple spot start simultaneously at into
The multi-thread independent solution search of row, but the algorithm search time is longer, easily falls into stagnation problem.
Based on the path planning algorithm of Quick Extended random tree (RRT/rapidly exploring random tree),
By carrying out collision detection to the sampled point in state space, the modeling to space is avoided, mechanical arm can be efficiently solved
Motion planning problem under higher dimensional space and Complex Constraints.
Traditional two-way RRT algorithm grows two Quick Extended random trees simultaneously from initial position and target position to search for
State space, generating a paths enables the effective avoiding obstacles of mechanical arm, is efficient rule in a kind of higher dimensional space
The method of drawing.But traditional two-way RRT algorithm lays particular emphasis on convergent rapidity, the path of planning be not it is optimal, do not account for
The cost cost of planning path length, searching route strategy are all based on the search of stochastical sampling, exist much to white space
Futile searches, waste search time.
Summary of the invention
In view of the deficiencies of the prior art, the present invention proposes a kind of manipulator motion planning sides for improving two-way RRT algorithm
Method.This method uses improved two-way RRT algorithm, can greatly reduce the path cost of planning, cooks up one for mechanical arm
The path of item optimization, it is optimal for solving the problems, such as motion path not.
In order to achieve the above objectives, the present invention adopts the following technical scheme:
A kind of manipulator motion planning method improving two-way RRT algorithm, specifically includes the following steps:
Step 1: given mechanical arm target position obtains the inverse solution of target position by the method for inverse kinematics；
Step 2: the size of all barriers, location information in environment are obtained；
Step 3: the two-way RRT algorithm of fusion tradition introduces the connection of the selection mode and more new node of optimal father node
Mode improves two-way RRT algorithm, and the path planning of mechanical arm is then carried out using improved two-way RRT algorithm, generates one
Collisionless path from initial position to target position；
Step 4: mechanical arm is moved according to the path planned, then judges whether mechanical arm reaches target position,
The stop motion if reaching, otherwise mechanical arm continues movement until reaching target position.
Compared with prior art, the beneficial effect comprise that
The present invention improves the mode of father node selection on the basis of traditional two-way RRT algorithm, using cost function come
The node for choosing minimum cost in expanding node field is father node；Meanwhile each iteration all can more new node connection type,
To guaranteeing lower computation complexity, the path of an optimization can be rapidly cooked up for mechanical arm, then mechanical arm according to
The path planned is moved, and it is optimal for solving the problems, such as motion path not.
Detailed description of the invention
Fig. 1 is the flow chart for improving two-way RRT algorithm and being used for manipulator motion planning method.
Fig. 2 is the point spread figure improved in two-way RRT algorithm.
Fig. 3 is the selection mode figure for improving optimal father node in two-way RRT algorithm.
Fig. 4 is the connection type figure for improving more new node in two-way RRT algorithm.
Fig. 5 is sixdegree-of-freedom simulation illustraton of model.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, below in conjunction with attached drawing, to the present invention into
Row is further described.It should be appreciated that specific implementation example described herein is not used to limit only to explain the present invention
The fixed present invention.
As shown in Figure 1, a kind of manipulator motion planning method for improving two-way RRT algorithm, the workflow of this method are retouched
It states are as follows: the emulation platform of sixdegree-of-freedom simulation is built first with open source robot operating system ROS；The mesh of given mechanical arm
Cursor position obtains the inverse solution of target position by the method for mechanical arm inverse kinematics；Obtain size, the position letter of all barriers
Breath；Improved two-way RRT algorithm is used to cook up a collisionless path for mechanical arm；Mechanical arm according to planning path into
Row movement, until reaching target position.Specifically includes the following steps:
Step 1: given mechanical arm target position obtains the inverse solution of target position by the method for inverse kinematics；Specifically such as
Under: sixdegree-of-freedom simulation model is put up, as shown in figure 5, given target position, solution finds out its target position by inverting
Each joint angle judges that inverse solution whether there is, and if it exists, be set as dbjective state xgoal；If it does not exist, target point is reset
It sets, until finding out reachable dbjective state.
Step 2: the size of all barriers, location information in environment are obtained；It is specific as follows: all barriers in detection environment
Hinder object, obtain the size of all barriers, location information in environment, provides collision letter to generate collisionless path in step 3
Breath.
Step 3: the two-way RRT algorithm of fusion tradition introduces the connection of the selection mode and more new node of optimal father node
Mode improves two-way RRT algorithm, and the path planning of mechanical arm is then carried out using improved two-way RRT algorithm, generates one
Collisionless path from initial position to target position；
Improved two-way RRT algorithm extension principle is as shown in Figure 2, Figure 3 and Figure 4: respectively from initial position and target position
Set two path tree T of buildingaAnd Tb, two-way RRT algorithm obtains sampled point x in state space stochastical samplingrand, then in TaOn look for
To distance xrandNearest node xnearest, from xnearestTo xrandSide extend up a certain distance and obtain new node xnew, with
xnewCentered on point, r is radius, choose xnewNeighbouring neighbor node region, found in neighbor node region root node from
Line cost function cost (xnew) the minimum and line not node x with barrier collisionnearAs xnewFather node obtains new branch
(xnear, xnew), it is added on tree；The connection type of more new node, to obtain optimal path；With xnewAs path tree TbExpand
The direction of exhibition, path tree TbTowards xnewDirection continue to extend branch, until new node and xnewIt is overlapped or falls into barrier
Space；If the T after the above iterationaWith TbIt does not connect, then next iteration is from TbStart to repeat above-mentioned expansion process；So hand over
For iteration until TaWith TbConnect and find a path from initial position to target position；Specific step is as follows:
(1) to random tree TaIt is extended, random tree TaBy initial position xinitAs starting point, i.e. tree TaRoot node,
By xgoalAs target point, and by xinitIt as the father node of this extension, extends, utilizes since the father node currently defined
Stochastical sampling function randomly chooses a sampled point x in free spacerand；Stochastical sampling mode is as follows: each in random tree
Growth course in, come the direction of growth of decision tree be target point or random point according to random chance；It is set in sampling function
Determine parameter Prob, obtain one 0 to 1.0 random value p every time, when 0 < p < Prob, random tree is grown towards target point；
As Prob < p < 1, random tree is grown towards a random direction.
(2) distance x is found in current random treerandNearest node xnearest, in xrandAnd xnearestLine on look for
One point xnew, i.e., newborn node, xnew=xnearest+Δd*|xrand-xnearest|, wherein xnearestFor distance x in random treerand
Nearest node, Δ d are customized step-length.
(3) to xnewCollision detection is done, if do not collided, with xnewFor the center of circle, r is radius, according to node formula
Near (V, xnew)={ x ∈ V:| | xnew-x||≤γ((logn)/n)1/d, choose xnewNeighbouring neighbor node region, i.e.,
xnearSet Xnear(all nodes in border circular areas on tree), wherein d is Spatial Dimension, and γ is the constant of selection, V be with
The set of machine tree node, x are certain nodes in region of search, and n is current iteration number.
(4) as shown in figure 3, selecting the mode of optimal father node (to xnewFind optimal father node): by xnearestIt fixes tentatively and is
xmin, then from root node to xminAnd xminTo xnewPath distance fix tentatively for minimal path spend cmin, then loop through
xnewNeighbor node set XnearInterior all nodes, from new node xnewTo set XnearInterior child node xnearGenerate respective road
Diameter σ, if cost (xnear)+cost (σ) < cminThat is root node is to xnearPath spend with newly connect path cost the sum of
Less than cminIf xnearAnd xnewLine collision detection it is qualified, then by the xnearAs the smallest xmin, minimal path, which is spent, is then
Root node is to the xnearWith the xnearTo xnewSummation, finally by the xnearWith xnewIt connects, is added on random tree.
(5) as shown in figure 4, the connection type of more new node, to obtain optimal path；Loop through neighbor node set
XnearIn other remaining xnear, judge a certain xnearTo xnewPath spend plus root node to xnewPath spend whether
Less than the xnearTo the path distance of root node, i.e. cost (xnew)+cost (σ) < cost (xnear) whether true, if so, that
Collision detection is carried out to its σ and defines the x if not detecting barriernearIt fixes tentatively as father node, then rejects all
Other all x being connected with the father nodenearWith remaining xnearEdge, that is, E ← E { xparent, xnear, E, which is represented, to be constituted
Side set, and by new node xnewWith father node xnearSide be re-added to tree in i.e. E ← E ∪ { xnew, xnear}。
(6) random tree TaTrial and TbConnection, algorithm terminates if connection；Otherwise to random tree TbIt is extended, random tree
TbThe mode of extension is slightly different, and has extended the new node x of one treenewAfterwards, it is set using this new target point as second
The direction of extension；Random tree TbBy xgoalAs starting point (root node), with xnewAs random tree TbThe direction of extension, i.e. handle
xnewAs the sampled point of present tree, it can persistently extend new branch in such a way that step (2) is identical in (5), until new section
Point and xnewIt is overlapped or falls into Obstacles.
(7) if after extending above TaWith TbDo not connect, then it is next from random tree TbStart iteration, repeats step
(1) new branch is extended to (6) and attempt to connect with another path tree；Such alternating iteration is until TaWith TbIt connects and finds one
Item is from xinitTo xgoalPath P ATH (Ta, Tb)。
Step 4: mechanical arm is moved according to the path planned, then judges whether mechanical arm reaches target position,
The stop motion if reaching, otherwise mechanical arm continues movement until reaching target position.Specifically: by step 3 by changing
Into the path that goes out of two-way RRT algorithmic rule be sent to mechanical arm, then mechanical arm is moved according to received path, mechanical
Arm during the motion can Real-time Feedback current location information judge whether mechanical arm reaches target position, if do not reach mesh
Cursor position mechanical arm will continue to move, until reaching target position.
The two-way RRT algorithm of the method for the present invention fusion tradition, introduces the searching mode and more new node of optimal father node
Connection type, improve two-way RRT algorithm, grow two Quick Extended random trees simultaneously from initial position and target position and come
A collisionless path is finally planned in search condition space.Path generation can be found for new node by finding optimal father node mode
The smaller father node of valence, the connection type of each iteration all more new nodes is by the reconnect mode between node come more new root section
Point arrives the path cost of the node, by improving two-way RRT algorithm, can reduce the path cost of planning, greatly for machinery
Arm cooks up the path of an optimization.
Claims (5)
1. a kind of manipulator motion planning method for improving two-way RRT algorithm, which is characterized in that specifically includes the following steps:
Step 1: given mechanical arm target position obtains the inverse solution of target position by the method for inverse kinematics；
Step 2: the size of all barriers, location information in environment are obtained；
Step 3: the two-way RRT algorithm of fusion tradition introduces the selection mode of optimal father node and the connection side of more new node
Formula improves two-way RRT algorithm, and the path planning of mechanical arm is then carried out using improved two-way RRT algorithm, generate one from
Collisionless path of the initial position to target position；
Step 4: mechanical arm is moved according to the path planned, then judges whether mechanical arm reaches target position, if arriving
Up to then stop motion, otherwise mechanical arm continues movement until reaching target position.
2. the manipulator motion planning method according to claim 1 for improving two-way RRT algorithm, which is characterized in that described
Step 1 is specific as follows: putting up sixdegree-of-freedom simulation model, gives target position, find out its target position by solution of inverting
Each joint angle, judge that inverse solution whether there is, and if it exists, be set as dbjective state xgoal；If it does not exist, target point is reset
Position, until finding out reachable dbjective state.
3. the manipulator motion planning method according to claim 1 for improving two-way RRT algorithm, which is characterized in that described
Step 2 is specific as follows: all barriers in detection environment obtain the size of all barriers, location information in environment, for step
Collisionless path is generated in rapid three, and collision information is provided.
4. the manipulator motion planning method according to claim 1 for improving two-way RRT algorithm, which is characterized in that described
Step 3, improved two-way RRT algorithm extension principle are as follows: constructing two path trees from initial position and target position respectively
TaAnd Tb, two-way RRT algorithm obtains sampled point x in state space stochastical samplingrand, then in TaOn find distance xrandNearest section
Point xnearest, from xnearestTo xrandSide extend up a certain distance and obtain new node xnew, with xnewCentered on point, r is half
Diameter chooses xnewThe offline cost function cost of root node is found in neighbouring neighbor node region in neighbor node region
(xnew) the minimum and line not node x with barrier collisionnearAs xnewFather node, obtain new branch (xnear, xnew), add
It is added on tree；The connection type of more new node, to obtain optimal path；With xnewAs path tree TbThe direction of extension, path
Set TbTowards xnewDirection continue to extend branch in a similar way, until new node and xnewIt is overlapped or falls into barrier sky
Between；If the T after the above iterationaWith TbIt does not connect, then next iteration is from TbStart to repeat above-mentioned expansion process；So alternately
Iteration is until TaWith TbConnect and find a path from initial position to target position；Specific step is as follows:
(1) to random tree TaIt is extended, random tree TaBy initial position xinitAs starting point, i.e. tree TaRoot node, will
xgoalAs target point, and by xinitAs this extension father node, extended since the father node currently defined, using with
Machine sampling function randomly chooses a sampled point x in free spacerand；Stochastical sampling mode is as follows: each in random tree
It come the direction of growth of decision tree is target point or random point according to random chance in growth course；
(2) distance x is found in current random treerandNearest node xnearest, in xrandAnd xnearestLine on look for a bit
xnew, i.e., newborn node, xnew=xnearest+Δd*|xrand-xnearest|, wherein xnearestFor distance x in random treerandRecently
Node, Δ d are customized step-length；
(3) to xnewCollision detection is done, if do not collided, with xnewFor the center of circle, r is radius, according to node formula Near (V,
xnew)={ x ∈ V:| | xnew-x||≤γ((logn)/n)1/d, choose xnewNeighbouring neighbor node region, i.e. xnearSet
Xnear, all nodes that border circular areas is interior to be set, wherein d is Spatial Dimension, and γ is the constant of selection, and V is random tree node
Set, x is certain node in region of search, and n is current iteration number；
(4) mode for selecting optimal father node, i.e., to xnewFind optimal father node: by xnearestIt fixes tentatively as xmin, then from root section
Point arrives xminAnd xminTo xnewPath distance fix tentatively for minimal path spend cmin, then loop through xnewNeighbor node collection
Close XnearInterior all nodes, from new node xnewTo set XnearInterior child node xnearRespective path σ is generated, if cost
(xnear)+cost (σ) < cminThat is root node is to xnearPath spend and the sum of spend with the path that newly connect less than cminIf
xnearAnd xnewLine collision detection it is qualified, then by the xnearAs the smallest xmin, minimal path cost be then root node to this
xnearWith the xnearTo xnewSummation, finally by the xnearWith xnewIt connects, is added on random tree；
(5) connection type of more new node, to obtain optimal path；Loop through neighbor node set XnearIn it is remaining its
He is xnear, judge a certain xnearTo xnewPath spend plus root node to xnewPath spend whether be less than the xnearTo root
The path distance of node, i.e. cost (xnew)+cost (σ) < cost (xnear) whether true, if so, so its σ is touched
Detection is hit, if not detecting barrier, defines the xnearIt fixes tentatively as father node, then rejects and all be connected with the father node
Other all xnearWith remaining xnearEdge, that is, E ← E { xparent, xnear, E represents the set on constituted side, and will
New node xnewWith father node xnearSide be re-added to tree in i.e. E ← E ∪ { xnew, xnear}；
(6) random tree TaTrial and TbConnection, algorithm terminates if connection；Otherwise to random tree TbIt is extended, random tree TbExpand
The mode of exhibition is slightly different, and has extended the new node x of one treenewAfterwards, it is extended using this new target point as second tree
Direction；Random tree TbBy xgoalAs starting point (root node), with xnewAs random tree TbThe direction of extension, i.e., xnewMake
For the sampled point of present tree, it can persistently extend new branch in such a way that step (2) is identical in (5), until new node with
xnewIt is overlapped or falls into Obstacles；
(7) if after extending above TaWith TbDo not connect, then it is next from random tree TbStart iteration, repeats step (1) and arrive
(6) it extends new branch and attempts to connect with another path tree；Such alternating iteration is until TaWith TbConnect and find one from
xinitTo xgoalPath P ATH (Ta, Tb)。
5. the manipulator motion planning method according to claim 1 for improving two-way RRT algorithm, which is characterized in that described
Step 4 specifically: mechanical arm will be sent to by the path that improved two-way RRT algorithmic rule goes out in step 3, then mechanical arm
Moved according to received path, mechanical arm during the motion can Real-time Feedback current location information to judge mechanical arm be
No arrival target position, if not reaching target position mechanical arm will continue to move, until reaching target position.
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