CN115079701A - Unmanned vehicle and unmanned aerial vehicle cooperative path planning method - Google Patents
Unmanned vehicle and unmanned aerial vehicle cooperative path planning method Download PDFInfo
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Abstract
The invention discloses a path planning method for cooperation of an unmanned vehicle and an unmanned aerial vehicle. The method comprises the following steps: acquiring a target point set to be detected of the unmanned aerial vehicle and selecting an alternative stop point set of the unmanned aerial vehicle; and solving a planning solution of the vehicle-machine cooperative inspection path planning model by taking the set objective function as an optimization objective so as to determine a feasible target point and a feasible stop point, wherein in the process of solving the planning solution of the vehicle-machine cooperative inspection path planning model, the running time of the unmanned vehicle for completing an inspection task, the waiting time of the unmanned vehicle and the charging time required by the unmanned vehicle are considered. The unmanned vehicle and unmanned aerial vehicle cooperative planning method can realize cooperative planning of the unmanned vehicle and unmanned aerial vehicle, has high solving efficiency, and improves the capability of the unmanned vehicle and unmanned aerial vehicle to cooperatively and dynamically execute the routing inspection task.
Description
Technical Field
The invention relates to the technical field of path planning, in particular to a path planning method for cooperation of an unmanned vehicle and an unmanned aerial vehicle.
Background
With the rapid development of urbanization, manual inspection cannot meet the requirement of modern urban inspection. In recent years, along with the development of unmanned technologies, unmanned aerial vehicles play an increasingly important role in fields such as search and inspection. However, the unmanned aerial vehicle cannot execute the routing inspection task for a long time or a long distance due to the limited battery capacity, so that the requirement of a complex routing inspection task cannot be met. In order to solve the problems of short endurance time, weak long-distance maneuverability and the like of the unmanned aerial vehicle, the unmanned aerial vehicle and the unmanned vehicle can be used for executing a combined inspection task in a cooperative mode. For example, as a long-distance ground mobile platform with unmanned vehicle, carry unmanned aerial vehicle and remove together, can also change the battery or charge for unmanned aerial vehicle and come reuse unmanned aerial vehicle, can effectively improve adaptability and the efficiency that unmanned system carries out the target and patrols and examines like this.
In order to realize autonomous cooperative operation of the unmanned vehicle and the unmanned aerial vehicle, the operation tracks of the unmanned vehicle and the unmanned aerial vehicle need to be planned in a cooperative manner. For example, patent publication No. CN113723804A discloses a car-machine cooperative distribution method and system considering multiple drone stations. The method comprises the following steps: randomly generating a vehicle-machine cooperative distribution scheme to form a scheme to be optimized; taking the scheme to be optimized as a historical optimal scheme and a current optimal scheme; judging whether the iteration times are greater than or equal to a preset value; under the condition that the iteration times are judged to be less than the preset value, executing destruction operation and insertion operation on the scheme to be optimized to form a corresponding scheme to be updated; determining whether the scheme to be updated is superior to the historical optimal scheme; under the condition that the scheme to be updated is superior to the historical optimal scheme, replacing the historical optimal scheme and the current optimal scheme with the scheme to be updated; under the condition that the scheme to be updated is inferior to the historical optimal scheme, updating the historical optimal scheme and the current optimal scheme based on the simulated annealing criterion; and under the condition that the current iteration number is judged to be larger than or equal to the preset value, taking the historical optimal scheme as a final vehicle-machine cooperative distribution scheme. The method considers that vehicles are used for carrying the unmanned aerial vehicles to the positions near the client points, the unmanned aerial vehicles carry the unmanned aerial vehicles for delivery, the vehicles are not moved, the unmanned aerial vehicles wait for return, the real vehicle-machine cooperative motion is not achieved, the unmanned aerial vehicles only consider the problem of one client point, and the method can increase the energy consumption and time of routing inspection on the aspect of routing inspection.
Patent publication No. CN113705982A provides a scheduling decision method for in-vehicle machine cooperative power patrol. The method comprises the following steps: acquiring equipment parameters of a polling task and an executable task; dividing an operation area, clustering all towers to be inspected by adopting an improved K-means clustering algorithm, namely a K mean value clustering algorithm, wherein each cluster generated by clustering is an operation area; parking point address selection of each operation area; planning a driving path of the inspection vehicle, and determining the shortest driving path of the inspection vehicle; and guiding the calculated parking point and the calculated running path of the inspection vehicle into the electric power inspection scheduling system. In the method, the unmanned aerial vehicle starts from the parking point, and returns to the same parking point after routing inspection, and the problem that the vehicle machine moves simultaneously is not considered.
Patent publication No. CN113985912A provides a path planning method and system for cooperative inspection of vehicles and unmanned aerial vehicles. The method comprises the steps of firstly, acquiring relevant parameters in the vehicle-mounted cooperative inspection process; then presetting a vehicle-machine cooperative inspection constraint condition, and constructing a vehicle-machine cooperative inspection path planning model by taking the shortest total time when the unmanned aerial vehicle reaches a destination after finishing the inspection of all target points as a target function based on related parameters; and solving the vehicle-machine cooperative inspection path planning model by using a CPLEX solver to obtain an optimal planning scheme of the vehicle-machine cooperative inspection path. The alternative stop points of the method are given in advance, are not reasonable and flexible enough, the model is solved through the CPLEX solver, the solving efficiency is low, and the situation that the vehicle can only wait for the unmanned aerial vehicle at the stop points is specified, so that the application scene is limited.
Through analysis, the prior art mainly has the following defects:
1) when planning the unmanned aerial vehicle path, the unmanned vehicle stop point is not flexible and reasonable enough. The prior art generally adopts fixed unmanned vehicle stopping points or alternative unmanned vehicle stopping points which are determined in advance. For the method of fixing the unmanned vehicle stop point, if the unmanned vehicle plans the path of the unmanned vehicle by taking the fixed unmanned vehicle stop point as a return point, the situation that the unmanned vehicle needs to return to the stop point on the original road after detecting the front target point can be caused, and the inspection time is increased. For the method of using the candidate unmanned vehicle stop points determined in advance, the candidate unmanned vehicle stop points are not flexible and reasonable enough, so that the efficient dynamic cooperation of the unmanned vehicle and the unmanned vehicle cannot be realized.
2) In the prior art, the solution of the model is carried out through a CPLEX solver or a genetic algorithm and the like, and the solution forms of path planning and stop point planning are complicated, long in solution time and slow in speed.
3) The prior art only considers the situation that an unmanned vehicle waits in place or arrives at a stop point in advance to wait for the unmanned vehicle, and the application range is limited.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a path planning method for cooperation of an unmanned vehicle and an unmanned aerial vehicle, which comprises the following steps:
acquiring a target point set to be detected of the unmanned aerial vehicle and selecting an alternative stop point set of the unmanned aerial vehicle;
solving a planning solution of the vehicle-machine cooperative inspection path planning model by taking a set objective function as an optimization objective so as to determine a feasible target point and a feasible stopping point;
in the planning solution process of solving the vehicle-machine cooperative routing inspection path planning model, the running time of the unmanned vehicle for completing the routing inspection task, the waiting time of the unmanned vehicle and the charging time required by the unmanned vehicle are considered.
In one embodiment, the objective function is represented as:
min T＝t ugv +Δt+Δt c
t ugv ＝d ugv /v ugv
wherein, d ugv The method comprises the steps of representing a driving distance from a starting point to a terminal point of an unmanned vehicle; v. of ugv Representing the running speed of the unmanned vehicle; t is t ugv The driving time of the unmanned vehicle which starts from the starting point and reaches the end point after all the target points are patrolled is shown; Δ t represents the time for the unmanned vehicle to wait for the unmanned vehicle at the stop point; Δ t c Battery replacement time or charging time required by the unmanned aerial vehicle is represented; t represents the total consumption time of the unmanned aerial vehicle from the starting point and reaching the end point after finishing patrolling all the target points.
In one embodiment, the alternative stopping points for the unmanned vehicle are selected according to the following steps:
the method comprises the steps of dividing roads in a road network into road sections with the same interval by adopting an equidistant scattering method, obtaining a coordinate matrix set of discrete points and storing the coordinates of the discrete points in stop points, and forming a first alternative stop point set.
And adding the closest road point from each target point to the road network to a first candidate stop point set, and expanding the closest road point to the road network to a second candidate stop point set as a selected candidate stop point set.
In one embodiment, in the process of solving the planning solution of the vehicle-machine cooperative routing inspection path planning model, the target points are numbered by using positive serial numbers, the alternative stop points are numbered by using negative serial numbers, the planning solution is a group of sequences formed by the numbers of the target points and the stop points, and the unmanned aerial vehicle inspects the target points in ascending order of the serial numbers.
Compared with the prior art, the unmanned vehicle and the unmanned aerial vehicle have the advantages that when the unmanned vehicle and the unmanned aerial vehicle jointly execute tasks in a certain area (such as a certain number of target points need to be detected in a routing inspection task), the route of the unmanned aerial vehicle and the stop point of the unmanned vehicle are reasonably planned, so that the unmanned vehicle and the unmanned aerial vehicle can dynamically cooperate; by flexibly and reasonably setting alternative stop points, a consistent representation form of the planning solution of the path and the stop points is provided; the designed dynamic path planning and stop point combined planning method can realize the cooperative planning of the unmanned vehicle and the unmanned aerial vehicle without adopting a complex solver and an optimization algorithm, has high solving efficiency, and improves the capability of the unmanned vehicle and the unmanned aerial vehicle to cooperatively and dynamically execute the routing inspection task.
Other features of the present invention and advantages thereof will become apparent from the following detailed description of exemplary embodiments thereof, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description, serve to explain the principles of the invention.
Fig. 1 is an example of an application scenario for unmanned vehicle and drone collaboration according to one embodiment of the invention;
fig. 2 is a flow chart of a method for cooperative path planning of unmanned vehicles and unmanned aerial vehicles according to an embodiment of the present invention;
FIG. 3 is a schematic illustration of selecting an alternate stop point for an unmanned vehicle, in accordance with one embodiment of the present invention;
FIG. 4 is a schematic diagram of a target point and a docking point, according to one embodiment of the present invention;
fig. 5 is a schematic illustration of coordinated movement of an unmanned vehicle and an unmanned aerial vehicle, according to one embodiment of the invention;
fig. 6 is a schematic diagram of a solution process of a routing model for unmanned vehicles and unmanned aerial vehicles according to an embodiment of the invention;
fig. 7 is a collaborative planned route map for a drone and an unmanned vehicle, in accordance with one embodiment of the present invention.
Detailed Description
Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.
The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.
In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
The unmanned aerial vehicle and unmanned vehicle cooperative path planning method provided by the invention comprises an unmanned aerial vehicle and an unmanned vehicle, wherein the unmanned vehicle can carry the unmanned aerial vehicle to run on a road network. Unmanned aerial vehicle configures the task carrier according to specific task of patrolling and examining (like sanitation, illegal construction and disinfection operation etc.), like detection equipment, disinfecting equipment etc..
Referring to the scenario shown in fig. 1, an unmanned vehicle carries an unmanned aerial vehicle to visit all target points from a predefined starting point. The unmanned aerial vehicle and unmanned vehicle cooperative path planning aims at finding a proper path and a dynamic stop point of the unmanned aerial vehicle and the unmanned vehicle, so that the unmanned aerial vehicle can traverse all target points from a starting point, and in the process, when the cruising distance or time of the unmanned aerial vehicle is exhausted, the unmanned aerial vehicle can return to the unmanned vehicle for charging or battery replacement, and finally the unmanned aerial vehicle reaches a destination position. In the description herein, the charging operation or the battery replacement operation is also collectively referred to as charging.
Unmanned plane velocity v uav The flying vehicle flies freely in the air and can fly towards all directions. Speed v of unmanned vehicle ugv When the unmanned aerial vehicle runs on a road network, the unmanned aerial vehicle can continuously move for a long time (with or without the unmanned aerial vehicle), and can be charged or carry enough standby batteries to be used for the unmanned aerial vehicle to change the battery. At the starting point of the road network point, the unmanned aerial vehicle takes off from the unmanned vehicle, and according to the planned detection path, the unmanned aerial vehicle takes off to the target points on the two sides of the road network and inspects the target points, for example, the unmanned aerial vehicle is used as a shooting tool or a sensor to directly inspect the target points and collect data. At the moment, the unmanned vehicle continues to move to the dynamic stop point according to the planning result of the dynamic stop point. The planned path of unmanned aerial vehicle guarantees that unmanned aerial vehicle patrols and examines a plurality of target points as far as under the biggest journey constraint to can get back to the stop on the road network, join with unmanned vehicles, in order to change the battery or charge. The unmanned aerial vehicle changes the battery to take off again, continues to patrol and examine the rest target points, and returns to the unmanned vehicle and moves to the terminal point after all the target points in the patrol and examine area complete the patrol and examine task.
Specifically, referring to fig. 2, the method for planning the collaborative path of the unmanned vehicle and the unmanned vehicle includes the following steps:
step S210, determining a road network and a target point of the routing inspection task.
And acquiring a road network and target points of the inspection task, wherein the road network is a road on which the unmanned vehicle can run, and the target points are the target points positioned in a certain distance at two sides of the road. The certain distance refers to the maximum distance that the unmanned aerial vehicle can reach before taking off from a certain point on the road network and returning to the point. For example, if the farthest flying distance of the unmanned aerial vehicle is 2R, the certain distance is referred to as R.
In one embodiment, the drone energy constraint is modeled as the maximum distance R that a drone can travel to and from a full battery, because small rotor drones all have a few kilometers of flight radius, and cities have a denser network of roads, so all target points in an urban environment can be considered to be within the detectable range of the drone-drone.
Step S220: discretizing the path and selecting alternative road network stop points.
Road network in actual environment is complicated, not every road is a straight line, and some roads are bent and changeable, and the method is particularly important for selecting stop points of vehicle-machine cooperative work. Due to the complexity of road networks, the road networks are difficult to represent by continuous functions, the paths are discretized into points connected in sequence, the planning model is easy to solve quickly in the follow-up process, and the calculation result is convenient to store and easy to expand.
In one embodiment, the road is divided into segments with the same width (i.e. interval) by using an equidistant dispersion method, and the number k of the segments can be determined according to actual conditions. Assuming that the length of each road segment after discretization is d, acquiring a coordinate matrix set M 'in which the coordinates of each discrete point are stored in stop points' h′×2 Wherein h' is the number of discrete points.
Preferably, the road point with the shortest distance from each target point to the road is further used as a candidate road network stop point, and the candidate road network stop points are numbered to obtain a candidate stop point set. The unmanned aerial vehicle takes off and lands only at a stop point in a road network.
Specifically, a coordinate matrix M 'at the stop point' h′×2 Adding a group of stop points, namely taking the closest road point from the target point to the road network as a stop point, adding an alternative stop point set of the unmanned vehicle, namely, making the intersection point of the target point and the road as a vertical line, and expanding the intersection point into a coordinate matrix of the stop pointM h×2 Where h is the number of discrete points (including the extended anchor points). The advantage of this design is that when the individual target points v are present j When the distance of the selected stopping point set M' is very close to the flight radius R of the unmanned aerial vehicle, no stopping point which can be returned by the unmanned aerial vehicle in the limited endurance range exists, and the problem can be solved after a new alternative stopping point set M is formed.
As shown in FIG. 3, when the road network is curved, a tangent line l is drawn outside the road 1 Passing through the target point as a tangent line l 1 Perpendicular line l 2 ，l 1 And l 2 The intersection point of (a) is the closest point m from the target point to the unmanned vehicle road network. If the road network road corresponding to the target point is a straight line, a perpendicular line l of the road is made directly passing through the target point 2 Road and vertical line l 2 The intersection point of the point M is the closest point M from the target point to the unmanned vehicle road network, and the point M is added into the set of the alternative stop points of the unmanned vehicle to form M.
Step S230, the target point and the alternative stop point are sorted.
Target points on both sides of the road network are numbered, for example, the target points are numbered sequentially from a starting point along a forward direction of the road, and a sequence of the target points is obtained, for example, V ═ 1, 2.
And numbering the stop points in the candidate stop point set M, and multiplying the number by-1 to obtain M { -1, -2.
By the marking mode, the condition that the number is positive represents the target points on two sides of the road network, and the condition that the number is negative represents the stop points on the road, so that the target points and the stop points can be effectively distinguished. FIG. 4 is a schematic illustration of a target point and a docking point.
And S240, constructing a vehicle-machine cooperative inspection path planning model, and acquiring a planning solution of the vehicle-machine cooperative inspection path by using a heuristic search method.
In the step, a heuristic search method is used for obtaining a planning solution of the vehicle-machine cooperative inspection path according to actual constraints of the unmanned vehicle and the unmanned vehicle. The planning solution is in the form of a set of sequences consisting of the numbers of target points and stop points together.
In the prior art, the solution form is usually a matrix form in storage and representation modes, the method has large storage data volume, has great influence on the model solving speed, and also has increased requirements on model equipment and a solver. There are also methods that use the index of coordinates, i.e. the number of each point, but the target point and the stopping point are stored separately, which is not simple and efficient enough.
In one embodiment of the invention, a simple and efficient form of storage is used, i.e. the numbers of target and anchor points together form a set of sequences. Wherein, a positive sequence number is 1,2, 3... denotes an inspection target point, a negative sequence number is-1, -2, -3.. denotes a stop point, and the unmanned aerial vehicle cooperate to form a motion schematic diagram as shown in fig. 5, and the storage form can be expressed as [ -1, 1, -2, -3, 2, -3, -5, 3, 4, -7, 5, -8 ]. By adopting the solution form of the vehicle-machine collaborative planning, the planning result of the vehicle-machine collaborative operation can be simply and effectively represented by only one group of numbers, and the judgment and calculation of the time for charging or battery replacement of the unmanned aerial vehicle are facilitated.
In a set of sequences representing the planning solution, the first negative sequence number and the last negative sequence number are removed, and the time for charging or changing the battery of the unmanned aerial vehicle (or collectively referred to as charging time) is judged and calculated among other negative sequence numbers. This is because the first negative sequence number and the last negative sequence number respectively represent the first departure point and the last landing point, and since the unmanned aerial vehicle is in a full power state by default at the first departure point, and the unmanned aerial vehicle does not need to take off after the inspection task has been completed at the last landing point, the battery charging and replacing time does not need to be considered at this time.
The aim of path planning is to maximize the inspection task in the unmanned aerial vehicle range, so that the inspection task time is shorter.
For example, the routing inspection target points in all the target point set V are sequenced according to the corresponding road network sections, and the unmanned aerial vehicle is routed in ascending order according to the serial numbers during routing inspection, so that the unmanned aerial vehicle can be ensured to sequentially detect the target points from near to far, the appearance of head-walking is prevented, and the task time is reduced; in addition, can also prevent that the loop from appearing in unmanned aerial vehicle route, prevent that the condition of returning the loop from appearing in unmanned aerial vehicle's flight path.
The vehicle-machine cooperative inspection path planning model can be described as follows: the unmanned aerial vehicle battery replacement or charging time and the waiting time of the unmanned aerial vehicle waiting for the unmanned aerial vehicle or the unmanned aerial vehicle waiting for the unmanned vehicle are considered, and in a vehicle-machine cooperation mode, when the unmanned aerial vehicle starts from the starting point and finishes the inspection of all target points to be inspected and arrives at the end point, the total time is as short as possible.
In one embodiment, the objective function of the vehicle-machine cooperative inspection path planning model is represented as:
min T＝t ugv +Δt+Δt c (1)
t ugv ＝d ugv /v ugv (2)
wherein, d ugv The method comprises the steps of representing a driving distance from a starting point to a terminal point of an unmanned vehicle; v. of ugv Representing the running speed of the unmanned vehicle; t is t ugv The driving time of the unmanned vehicle which starts from the starting point and reaches the end point after all the target points are patrolled is shown; Δ t represents the time for the unmanned vehicle to wait for the unmanned vehicle at the stop point; Δ t c The battery replacement or charging time required by the unmanned aerial vehicle is represented; t represents the total consumption time of the unmanned aerial vehicle from the starting point and reaching the end point after finishing patrolling all the target points.
Fig. 6 is a schematic diagram of a solution process of unmanned vehicle-unmanned aerial vehicle collaborative planning, which specifically includes the following steps:
step S0, determine the set of stop points M and the set of target points V.
Step S1, from a stop point m i Taking off and judging whether the next unaccessed target point v can be reached j Namely:
wherein the content of the first and second substances,
indicates a stop point m i To the targetPoint v j The distance of (c).
If the judgment result of the step S1 is YES, a step S2 is executed:
step S2, the unmanned aerial vehicle is at the stop point m i Takeoff, visit target point v j And then judge that the unmanned aerial vehicle is at the stop point m i Takeoff, visit target point v j Then, whether or not the next unvisited destination point v can be reached j+1 Namely:
and repeating the step S2 until no more target points can be visited in the unmanned aerial vehicle range, and saving the target points which can be visited by the unmanned aerial vehicle.
If the judgment result of the step S1 is not satisfied, a step S3 is executed:
step S3, the unmanned vehicle carries the unmanned aerial vehicle to reach the next stop point m i+1 And returning to the step S1 until the unmanned aerial vehicle is at the stop point m i+l Taking off and reaching the next unvisited target point v j A stop point m i+l As flying spot m i Then, step S2 is executed.
Step S4, judging whether the unmanned aerial vehicle flies from the flying point m i And returning the take-off access target point to the unmanned vehicle, and judging whether the range of the unmanned vehicle meets the maximum flight distance 2R smaller than that of the unmanned vehicle.
Selecting the last target point v of the voyage j+k Corresponding stop point m of shortest distance of road network i+l As the landing point of the unmanned plane, namely:
if step S4 cannot be satisfied, then step S5 is performed:
step S5, the target point of unmanned aerial vehicle range visit is reduced by one, and the target point is returned to the previous target point v j+k-1 And returning to the step S4 as the last target point of the unmanned aerial vehicle range.
Until the unmanned aerial vehicle voyage does not have the target point that can visit, unmanned aerial vehicle carries on unmanned aerial vehicle and arrives next stop m i+1 Returning to step S1.
If step S4 is satisfied, then execute step S6:
step S6, selecting feasible stopping points;
the stop point returned by the unmanned aerial vehicle is selected to be one forward, namely the unmanned aerial vehicle returns to the next stop point m i+l+1 As the landing point of the unmanned plane voyage. And judging whether the range of the unmanned aerial vehicle meets the maximum flight distance 2R smaller than the unmanned aerial vehicle.
And repeating the steps until the judgment condition cannot be met.
The stop point returned by the unmanned aerial vehicle is selected to be one backward, namely the unmanned aerial vehicle returns to the previous stop point m i+l-1 As the landing point of the unmanned plane voyage. And judging whether the range of the unmanned aerial vehicle meets the maximum flight distance 2R smaller than the unmanned aerial vehicle.
And repeating the steps until the judgment condition cannot be met.
The feasible stops are sorted into a feasible stop set M' according to the size of the previous sorting in ascending order, and the top feasible stop is selected as the junction.
And step S7, judging whether the unmanned vehicle unmanned aerial vehicles can be converged.
Taking the maximum stop point as the landing point m of the unmanned aerial vehicle on the voyage i+l′ Judging that the unmanned vehicle reaches the landing point m i+l′ Time and unmanned aerial vehicle from stop point m i Taking off, accessing all target points of the voyage and returning to the stop point m i+l′ The time scale of (c).
t ugv ＝d ugv /v ugv (7)
t uav ＝d uav /v uav (8)
The unmanned plane is in two conditions, namely, the unmanned plane arrives first and waits for the unmanned plane; secondly, the unmanned aerial vehicle arrives first and waits for the unmanned vehicle.
(A) Unmanned vehicle first arrives and waits for unmanned aerial vehicle
M for judging whether the unmanned vehicle reaches the landing point i+l′ Whether the time is less than the unmanned aerial vehicle from the stop point m i Taking off, accessing all target points of the voyage and returning to the stop point m i+l′ Time of (d), i.e.;
t ugv ≤t uav (9)
Δt＝t uav -t ugv (10)
(B) unmanned aerial vehicle first arrives and waits for unmanned vehicle
The unmanned aerial vehicle first arrives at the situation, and whether the unmanned aerial vehicle can arrive at the stop point to be converged or not needs to be considered before the energy consumption of the unmanned aerial vehicle is exhausted.
M for judging whether the unmanned vehicle arrives at a stop point i+l′ Whether the time is less than the unmanned aerial vehicle from the stop point m i The time for the takeoff to access all target points of the flight and then return to the stop point mi + l' plus the hovering waiting time of the unmanned aerial vehicle, namely the maximum flight time t under the energy consumption of the unmanned aerial vehicle Consumption unit ：
t Consumption unit ＝2R/v uav (11)
t ugv ≤t Consumption unit (12)
Step S8, if step S7 neither (A) nor (B) is satisfied, that is, the unmanned aerial vehicles can not converge, then selecting a stop point M from the feasible stop point set M' to the back i+l′-1 And repeating the step S7 to select a stop point where the unmanned vehicle unmanned aerial vehicle can converge.
If no stop point that the unmanned vehicle unmanned aerial vehicle can converge exists in the feasible stop point set M', the target points visited by the unmanned aerial vehicle range are decreased by one, and the step S4 is returned.
If the unmanned aerial vehicle has no target point which can be accessed in the voyage, the unmanned aerial vehicle carries the unmanned aerial vehicle and then reaches the next stop point m i+1 The process returns to step S1.
If there is a case where (a) and (B) in step S7 are satisfied, the stop point is considered to satisfy the requirement that the unmanned vehicle unmanned aerial vehicles can meet.
In step S9, it is determined whether the area target point has been visited.
Namely: last target point v of unmanned aerial vehicle journey j Whether subscript j of (b) equals n.
If the inspection is not finished, the unmanned vehicle carries the unmanned vehicle to the next unmanned vehicle flying starting point, the unmanned vehicle visits the next stop point in the target area from the next stop point, then the steps are repeated, and the unmanned vehicle visits the target point which is not visited again from the step S1 until all the target points in the area are inspected.
I.e. the last target point v of the unmanned aerial vehicle journey j The index j of (a) is equal to n.
In addition, in the process of obtaining the planning solution, the judgment and calculation of the time for charging the unmanned aerial vehicle or replacing the battery are also involved.
As shown in fig. 5, unmanned vehicle unmanned aerial vehicle moves together schematically. The charging and battery replacing time between two continuous negative serial numbers and the charging and battery replacing time of a single negative serial number are divided into two conditions aiming at a group of numbers including the serial numbers of the target point and the stop point.
1) Two consecutive negative sequence numbers
As in the case of the solution between-2, -3 or-3, -5 in fig. 5, i + l represents the general form, and the case of two consecutive negative sequence numbers represents the drone at the stop point m i After the unmanned aerial vehicle falls back, the unmanned aerial vehicle carries the unmanned aerial vehicle to a stop point m i+l From stop m for unmanned plane i+l And (6) taking off.
Considering that the time for completing the operation of charging and battery replacing for the unmanned aerial vehicle is t c At this time, the unmanned vehicle is required to be compared with the parking point m i Travel to stop point m i+l Time of charging and battery replacement time t c Unmanned vehicle from stop point m i Travel to stop point m i+l Time of (2):
when t is i,i+l ≥t c When, the unmanned plane is atThe battery replacement or charging operation can be completed in the driving process of the unmanned vehicle;
when t is i,i+l ＜t c During the time, unmanned aerial vehicle just can't accomplish in unmanned vehicle driving process and trade the battery or the operation of charging, unmanned aerial vehicle need wait for a period of time to accomplish and trade the battery or the operation of charging this moment, the time that the battery was traded in the charging that unmanned aerial vehicle really needed promptly:
Δt c ＝t c -t i,i+l (14)
2) a single negative serial number;
as in the case of solution-7 in fig. 5, the single negative sequence number indicates that the drone is at a stop point m i After the unmanned aerial vehicle is landed back, the battery replacement or charging operation is finished, and the unmanned aerial vehicle still follows the stop point m i Taking off, the time that the battery was traded in the charging that unmanned aerial vehicle really needed this moment is:
Δt c ＝t c (15)
to further verify the effect of the present invention, simulation experiments were performed. Under the simulation condition, suppose that 1 patrol and examine unmanned vehicle carries an unmanned aerial vehicle to patrol and examine the operation to some region under jurisdiction of a certain city, all target points to be examined and road network coordinates are known in this region.
Fig. 7 is a collaborative planned route map of an unmanned aerial vehicle and an unmanned vehicle. Set up target point 5, unmanned aerial vehicle maximum flight distance sets up 2000 meters, and the highway section sets up 5000 meters, and unmanned aerial vehicle flying speed sets up 2 meters per second, and unmanned vehicle speed sets up 1 meter per second. The lines of the unmanned aerial vehicle and the unmanned vehicle are reasonable as can be seen from the collaborative planning route map.
In summary, the technical effects of the present invention are mainly reflected in the following aspects:
1) and selecting an alternative docking point set according to the actual environment and the inspection requirement. In order to prevent the distance between a specific target point and the flying radius of the unmanned aerial vehicle from being very close to the flying radius of the unmanned aerial vehicle, the closest point from the target point to the unmanned aerial vehicle road network is added into the candidate stop point set of the unmanned aerial vehicle, the road section is discretized into appropriate points by adopting an equidistant discretization method, and the stop points are reasonably and comprehensively planned.
2) And sorting the points corresponding to the shortest distance from the target points to the roads and sorting the stop points according to the position of the given target points and the road network of the routing inspection area and the traveling direction of the unmanned vehicle. Unmanned aerial vehicle patrols and examines according to the ascending order of sequence number when patrolling and examining, can prevent that the condition of loop from appearing in the unmanned aerial vehicle route, can prevent that unmanned vehicle from making a round trip to converge unmanned aerial vehicle and influence the traffic conditions of road on the road, reduces the energy consumption and the time of patrolling and examining, improves the efficiency that the car machine patrols and examines the task in coordination.
3) According to the actual constraint of the vehicle-mounted cooperative inspection task, a heuristic search method is used, two conditions that an unmanned vehicle waits for the unmanned vehicle and waits for the unmanned vehicle under the constraint of energy consumption of the unmanned vehicle are respectively considered, and the time for charging or replacing a battery of the unmanned vehicle is considered, so that the inspection task is maximized in the range of the unmanned vehicle, and the inspection task time is shortened. The mode enables the unmanned vehicle and the unmanned vehicle to cooperate dynamically, reduces the task execution time, realizes efficient vehicle-machine cooperative motion, and is closer to a real environment.
4) The method comprises the steps of constructing a simple and effective storage and solution form, for example, representing a routing inspection target point by using a positive sequence number, representing a stop point by using a negative sequence number, and calculating the time for charging or replacing a battery of the unmanned aerial vehicle in the storage and solution form, wherein the storage and solution form reduces the complexity of a model, is simpler and more efficient, has better calculation efficiency, and improves the efficiency of the vehicle-mounted machine cooperative routing inspection task.
The present invention may be a system, method and/or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied therewith for causing a processor to implement various aspects of the present invention.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be interpreted as a transitory signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or an electrical signal transmitted through an electrical wire.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device over a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present invention may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + +, Python, or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present invention are implemented by personalizing an electronic circuit, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA), with state information of computer-readable program instructions, which can execute the computer-readable program instructions.
Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, implementation by software, and implementation by a combination of software and hardware are equivalent.
Having described embodiments of the present invention, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the invention is defined by the appended claims.
Claims (10)
1. A path planning method for cooperation of an unmanned vehicle and an unmanned aerial vehicle comprises the following steps:
acquiring a target point set to be detected of the unmanned aerial vehicle and selecting an alternative stop point set of the unmanned aerial vehicle;
solving a planning solution of the vehicle-machine cooperative inspection path planning model by taking a set objective function as an optimization objective so as to determine a feasible target point and a feasible stopping point;
in the planning solution process of solving the vehicle-machine cooperative routing inspection path planning model, the running time of the unmanned vehicle for completing the routing inspection task, the waiting time of the unmanned vehicle and the charging time required by the unmanned vehicle are considered.
2. The method of claim 1, wherein the objective function is represented as:
min T＝t ugv +Δt+Δt c
t ugv ＝d ugv /v ugv
wherein d is ugv The method comprises the steps of representing a driving distance from a starting point to a terminal point of an unmanned vehicle; v. of ugv Representing the running speed of the unmanned vehicle; t is t ugv The driving time of the unmanned vehicle which starts from the starting point and reaches the end point after all the target points are patrolled is shown; Δ t represents the time for the unmanned vehicle to wait for the unmanned vehicle at the stop point; Δ t c Battery replacement time or charging time required by the unmanned aerial vehicle is represented; t represents the total consumption time of the unmanned aerial vehicle from the starting point and reaching the end point after finishing patrolling all the target points.
3. The method of claim 1, wherein the alternative docking points for the unmanned vehicle are selected according to the following steps:
the method comprises the steps of dividing roads in a road network into road sections with the same interval by adopting an equidistant scattering method, obtaining coordinates of all scattered points and storing the coordinates in a coordinate matrix set of stop points, and forming a first alternative stop point set.
And adding the closest road point from each target point to the road network to a first candidate stop point set, and expanding the closest road point to the road network to a second candidate stop point set as a selected candidate stop point set.
4. A method according to claim 3, characterized in that the closest road point to the road network of each target point is determined according to the following steps:
for the curved road condition, the road is made outside the roadTangent line l of road 1 Passing through the corresponding target point to make a tangent line l 1 Perpendicular line l 2 Is prepared by 1 And l 2 The intersection point of the target point and the road network is used as the nearest road point of the target point to the road network;
for the condition that the road is a straight line, a perpendicular line l of the road is made through the target point 2 The road is aligned with the vertical line l 2 The intersection point of (a) is used as the nearest road point from the target point to the road network.
5. The method according to claim 1, wherein in the process of solving the planning solution of the vehicle-machine cooperative routing inspection path planning model, the target points are numbered by using positive serial numbers, the alternative stop points are numbered by using negative serial numbers, the planning solution is a group of sequences formed by the numbers of the target points and the stop points, and the unmanned aerial vehicle inspects the target points in ascending order of the serial numbers.
6. The method according to claim 5, wherein in the process of solving the planning solution of the vehicle-machine cooperative inspection path planning model, a feasible stopping point is determined by judging whether the unmanned vehicle and the unmanned vehicle can converge, and the method comprises the following steps:
to the unmanned vehicle first arriving at the stop point, wait for the unmanned vehicle's condition:
judging whether the unmanned vehicle reaches a stop point m i+l′ Whether the time is less than the unmanned aerial vehicle from the stop point m i Taking off, visiting all target points and returning to a stop point m i+l′ The time of (d);
to the situation that the unmanned aerial vehicle arrives at the stop first, waits for the unmanned vehicle:
judging whether the unmanned vehicle reaches a stop point m i+l′ Whether the time is less than the unmanned aerial vehicle from the stop point m i During takeoff, the maximum flight time of the unmanned aerial vehicle under energy consumption;
wherein, i and i + l 'represent the index of waypoint, when neither satisfying two kinds of condition, represent unmanned vehicle and unmanned aerial vehicle can't converge, when satisfying one of two kinds of condition, represent unmanned vehicle and unmanned aerial vehicle can converge, will correspond the waypoint and regard as feasible waypoint.
7. The method of claim 5, wherein the charging time required for the drone is calculated according to the following steps:
for unmanned aerial vehicle at stop point m i After the unmanned aerial vehicle falls back, the unmanned aerial vehicle carries the unmanned aerial vehicle to a stop point m i+l Unmanned aerial vehicle from stop m i+l Takeoff conditions:
comparing the unmanned vehicle from the stop point m i Travel to stop point m i+l Time t of i,i+l And charging time t c When t is i,i+l ≥t c When the unmanned aerial vehicle is judged to be capable of finishing charging operation in the driving process of the unmanned aerial vehicle, and when t is reached i,i+l ＜t c Time, charging time delta t needed by unmanned aerial vehicle c Comprises the following steps:
Δt c ＝t c -t i,i+l
for unmanned aerial vehicle at stop point m i After descending back to the unmanned aerial vehicle, the charging operation is completed, and the unmanned aerial vehicle still follows the stop point m i Taking off, charging time Δ t required by the drone c Comprises the following steps:
Δt c ＝t c
where i and i + l represent indices.
8. The method of claim 6, wherein the maximum time of flight at the energy consumption of the drone is expressed as:
t consumption unit ＝2R/v uav
Wherein R represents the farthest flight distance of the unmanned aerial vehicle, v uav Representing the flight speed of the drone.
9. A computer-readable storage medium, on which a computer program is stored, wherein the computer program, when being executed by a processor, realizes the steps of the method according to any one of claims 1 to 8.
10. A computer device comprising a memory and a processor, on which memory a computer program is stored which is executable on the processor, characterized in that the processor realizes the steps of the method according to any one of claims 1 to 8 when executing the computer program.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115933750A (en) * | 2023-01-06 | 2023-04-07 | 国网浙江省电力有限公司嵊州市供电公司 | Data processing-based power inspection method and power inspection system |
| CN116300990A (en) * | 2022-11-18 | 2023-06-23 | 南京航空航天大学 | Time planning method for collaborative search and rescue of helicopter and unmanned aerial vehicle in low-altitude environment |
| CN116934029A (en) * | 2023-07-20 | 2023-10-24 | 南京海汇装备科技有限公司 | Ground-air cooperation management system and method based on artificial intelligence |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116300990A (en) * | 2022-11-18 | 2023-06-23 | 南京航空航天大学 | Time planning method for collaborative search and rescue of helicopter and unmanned aerial vehicle in low-altitude environment |
| CN116300990B (en) * | 2022-11-18 | 2023-11-07 | 南京航空航天大学 | Helicopter and unmanned aerial vehicle collaborative search and rescue time planning method for low-altitude environment |
| CN115933750A (en) * | 2023-01-06 | 2023-04-07 | 国网浙江省电力有限公司嵊州市供电公司 | Data processing-based power inspection method and power inspection system |
| CN116934029A (en) * | 2023-07-20 | 2023-10-24 | 南京海汇装备科技有限公司 | Ground-air cooperation management system and method based on artificial intelligence |
| CN116934029B (en) * | 2023-07-20 | 2024-06-04 | 南京海汇装备科技有限公司 | Ground-air cooperation management system and method based on artificial intelligence |
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