CN115576332B - Task-level multi-robot collaborative motion planning system and method - Google Patents
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Abstract
The invention discloses a task-level multi-robot collaborative motion planning system and a method, wherein the method comprises the following steps: on the premise of giving each robot single body operation subtasks, collision-free path planning of the robot single bodies relative to the static environment obstacles is carried out, collision-free between the robot single bodies and the environment is achieved, under the condition that each robot single body subtask is guaranteed, collaborative planning among the multi-robot dynamic obstacles is achieved, the shortest path is used as a constraint condition, collision-free of multi-robot space resources is achieved, the problem of multi-robot collaborative processing of large-scale complex structural parts is effectively solved, and collaborative planning efficiency and task adaptability of a multi-robot system are improved.
Description
Technical Field
The invention relates to the technical field of robot motion planning, in particular to a task-level multi-robot collaborative motion planning system and method.
Background
Task-level multi-robot collaborative motion planning refers to the realization of collision-free and conflict-free collaborative motion planning of a multi-robot system on the premise of ensuring that the individual subtasks (operation paths) of each robot are not changed, and is one of the main problems in the robotized manufacturing of large-scale complex structural members. The traditional multi-robot system focuses on setting the working spaces of all robots to be independent from each other, namely, for each individual robot, the rest robots belong to static obstacles, and each robot follows a strict operation time sequence and plans an operation task according to offline programming, so that the individual robots lack effective information interaction, and the problems of extremely low planning efficiency, low task adaptability, large space resource occupation and the like exist. For the static motion planning method, it is only necessary to consider the motion planning of each robot individual in a fixed time tracking pre-planned trajectory or static structured environment, and the planning methods generally include a free-form space planning method, a cell decomposition method, a path map method, an artificial potential field method, a probability map, a fast expansion random tree and the like.
However, the static motion planning method cannot be applied to a task-level multi-robot system with a shared working space, and firstly, the multi-robot system belongs to a super-redundancy dimension system, and the planning variables are many; the second is that the system includes static obstacles of the environment, and each robot is a dynamic obstacle of the other robots.
Disclosure of Invention
Aiming at the problems, the invention provides a task-level multi-robot collaborative motion planning system and a task-level multi-robot collaborative motion planning method, and aims to solve the problem that a task-level multi-robot system with a shared working space cannot apply a static motion planning method.
In order to solve the above technical problems, a first aspect of the present invention provides a task-level multi-robot collaborative movement planning method, including the following steps:
s1, giving a limited number of collision-free path points in each robot monomer configuration space in a multi-robot system, continuously sampling in the configuration space by adopting a tree generation strategy, carrying out iterative compromise collision detection on a connecting line between any two collision-free path points until the state interval of the connecting line meets given sampling precision, and planning a collision-free path of each robot monomer in the multi-robot system under the constraint conditions of a tree structure corresponding to the tree generation strategy and the collision-free path points;
s2, determining all motion sequences contained in the multi-robot system, taking the length of the collision-free path of each robot monomer as a sub-motion cost, determining the total motion cost of the motion sequences according to the sub-motion cost, screening out the motion sequence with the minimum total motion cost, defining the motion sequence as an optimal motion sequence, constructing a heuristic search algorithm, giving initial nodes, termination nodes, search directions and direction costs of the heuristic search algorithm on the basis of the optimal motion sequence, and obtaining the collision-free optimal motion path of each robot monomer by setting a shortest path constraint condition.
In some embodiments, S1 is preceded by S0: and performing collision detection on a random state of the configuration space by using a sampling method, if the random state contains collision or intersection, defining the random state as a state of a collision configuration space, if the random state does not contain collision or intersection, defining the random state as a state of a free configuration space, and extracting the collision-free path point from the state of the free configuration space.
In some embodiments, the collision detection comprises: and the directed hierarchy bounding box approaches the environmental static obstacles in the multi-robot system and judges whether collision or intersection exists between any two different objects in the multi-robot system.
In some embodiments, the sampling method has a random sampling number of 10000 or more.
In some embodiments, after the collision-free path is planned, a speed constraint and an acceleration constraint are introduced to smooth the collision-free path.
In some embodiments, the search direction has and has only "forward" or "stop" for any of the initial nodes.
In some embodiments, the directional cost corresponding to the search direction is: the sum of the motion costs of all the individual robots currently performing the "forward" operation.
In a second aspect, a task-level multi-robot collaborative motion planning system includes:
the collision-free path planning unit is used for giving a limited collision-free path point in each robot monomer configuration space in the multi-robot system, continuously sampling in the configuration space by adopting a tree generation strategy, carrying out iterative compromise collision detection on a connecting line between any two collision-free path points until the state interval of the connecting line meets the given sampling precision, and planning a collision-free path of each robot monomer in the multi-robot system under the constraint conditions of a tree structure corresponding to the tree generation strategy and the collision-free path points;
and the optimal motion path unit is used for determining all motion sequences contained in the multi-robot system, determining the total motion cost of the motion sequences according to the sub-motion cost by taking the length of the collision-free path of each robot monomer as the sub-motion cost, screening out the motion sequence with the minimum total motion cost, defining the motion sequence as the optimal motion sequence, constructing a heuristic search algorithm, giving initial nodes, termination nodes, search directions and direction costs of the heuristic search algorithm on the basis of the optimal motion sequence, and obtaining the optimal motion path without collision of each robot monomer by setting a shortest path constraint condition.
In a third aspect, the present invention provides a task-level multi-robot collaborative movement planning apparatus, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any of the methods described above when executing the computer program.
In a fourth aspect, the invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, carries out the steps of the method according to any one of the above.
The beneficial effects of the invention are as follows: under the premise of giving each robot single body operation subtasks, collision-free path planning of the robot single bodies relative to the static environment obstacles is carried out, collision-free between the robot single bodies and the environment is achieved, under the condition that each robot single body subtask is guaranteed, collaborative planning is carried out among the multi-robot dynamic obstacles, the shortest path is used as a constraint condition, collision-free of multi-robot space resources is achieved, the problem of collaborative machining of multiple robots of a large-scale complex structural member is effectively solved, and collaborative planning efficiency and task adaptability of a multi-robot system are improved.
Drawings
Fig. 1 is a flowchart of a task-level multi-robot collaborative movement planning method according to an embodiment of the present invention;
fig. 2 is an architecture diagram of a task-level multi-robot collaborative motion planning method according to an embodiment of the present invention;
fig. 3 is a schematic composition diagram of a task-level multi-robot collaborative movement planning apparatus according to a third embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer and clearer, the following detailed description of the present invention is provided with reference to the accompanying drawings and detailed description. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some but not all of the relevant aspects of the present invention are shown in the drawings.
Example one
The embodiment provides a task-level multi-robot collaborative motion planning method, which is used for decoupling a collaborative motion planning problem of a super-redundancy multi-robot system, converting the problem into two sub-problems of collision-free motion planning of a robot monomer high-dimensional configuration space and conflict-free continuous path searching of a state space of the multi-robot system, and solving the two sub-problems.
As shown in fig. 1, the method comprises the following steps:
s0, performing collision detection on the random state of the single robot configuration space in the multi-robot system by using a sampling method, if the random state contains collision or intersection, defining the random state as the state of the collision configuration space, if the random state does not contain collision or intersection, defining the random state as the state of the free configuration space, and extracting collision-free path points from the state of the free configuration space.
In step S0, the robot monomer non-collision motion planning method based on the high-speed probability path map assumes that a multi-robot system consists of one robotNThe dimensional configuration space (the dimension is equal to the sum of all degrees of freedom in the system) is expressed. In general, assume a multi-robot system
ComprisesnThe individual robot, the multi-robot system is represented as:
wherein the content of the first and second substances,
is shown as
The robot single body has a corresponding configuration space:
wherein, the first and the second end of the pipe are connected with each other,
is shown as
The number of degrees of freedom of the individual robots.
In step S0, the collision detection described above includes: the directed hierarchical bounding box approaches each robot monomer and the static environment obstacle in the multi-robot system, and then the collision/intersection detection algorithm of the hierarchical bounding box is combined to judge whether collision or intersection exists between two different objects (including the robot monomers) in any state in the multi-robot system, so that the collision/intersection detection between different objects in the system is realized. In this embodiment, the random sampling frequency of the sampling method is greater than or equal to 10000, and the larger the sampling number is, the higher the collaborative planning precision is.
After collision detection, the configuration space of each robot monomer in the multi-robot system is determined by the collision configuration space
And free configuration space
Composition, expressed as:
。
s1, giving a limited number of collision-free path points in each robot monomer configuration space in a multi-robot system, continuously sampling in the configuration space by adopting a tree generation strategy, carrying out iterative compromise collision detection on a connecting line between any two collision-free path points until the state interval of the connecting line meets the given sampling precision, and planning the collision-free path of each robot monomer in the multi-robot system under the constraint conditions of a tree structure corresponding to the tree generation strategy and the collision-free path points.
In step S1, the collision-free path points include at least 2. After the collision-free path is planned, the speed constraint and the acceleration constraint are introduced to carry out smoothing treatment on the collision-free path, so that the motion of the robot monomer is more stable.
In summary, S0 and S1 perform collision-free path planning of the robot cell relative to the environmental static obstacle on the premise of giving each robot cell operation subtasks, so as to achieve "collision-free" between the robot cell and the environment.
S2, determining all motion sequences contained in the multi-robot system, determining the total motion cost of the motion sequences according to the sub-motion cost by taking the length of the collision-free path of each robot monomer as the sub-motion cost, screening out the motion sequence with the minimum total motion cost, defining the motion sequence as the optimal motion sequence, constructing a heuristic search algorithm, giving initial nodes, termination nodes, search directions and direction costs of the heuristic search algorithm on the basis of the optimal motion sequence, and obtaining the collision-free optimal motion path of each robot monomer by setting a shortest path constraint condition.
In step S2, a multi-robot motion optimization level strategy taking motion cost as a quantization index is combined with a heuristic shortest path direct search algorithm to solve the problem of multi-robot motion conflict. The method aims at the problem of motion optimization level of each robot monomer in a multi-robot system, namely, whether each robot monomer advances or waits at a space resource conflict position is determined, and the length of a collision-free path of each robot monomer is used as motion cost for measuring the motion optimization level by minimizing the task time of the multi-robot system.
Assume multi-robot system
ComprisesnThe single body of the individual robot is provided with a plurality of robots,
wherein
Is shown as
Individual robots, all motion sequences of the system being combined into
Groups, wherein the motion cost of each group of motion sequencesThe motion cost of the non-prioritized combination is subtracted from the motion cost of the prioritized combination. Generally, the lower the motion cost of the motion sequence as a whole indicates that the sequence has a higher motion optimization level, thereby determining the motion optimization level of each robot cell. For example, a multi-robot system with 3 robot cells has 6 sets of motion sequences
Wherein, in a sequence of movements
For example, it corresponds to a motion cost:
wherein the content of the first and second substances,
and representing the length of the collision-free path, and by analogy, the motion cost of each motion sequence can be obtained respectively.
If motion sequence
The motion cost is minimum, and the highest motion optimization level is the single robot
And secondly is
And finally is
. In a sequence of movements
For an optimal motion sequence, construct one
Dimensions (each dimension representing a single robot cell,
) The node of the heuristic shortest path direct search algorithm represents the sequence number of the motion path of the robot monomer. For example, a multi-robot system comprises 3 robot units, and the number of the motion path points of each robot is
Then the initial node of the algorithm is
(indicating that each robot is in a starting state) and the termination node is
(indicating that each robot is in a stopped state). Thus, the goal of the algorithm is to search for the shortest path from the initial node to the terminating node.
In order to avoid redundant movement of the robot, in the embodiment, the algorithm can be provided with only 2 search directions in each node, namely, for any initial node, the search directions have only "advance" or "stop". For a 3 robot system, the number of search directions for each node is
Where directions for all stopped states are ignored. In addition, the directional cost corresponding to each search direction is the sum of the motion costs of all the robot cells performing the "forward" operation at present.
The specific architecture is shown in fig. 2. In conclusion, S2 is that under the condition that the individual subtasks of each robot are ensured, the cooperative planning among the multiple robots is realized, the shortest path is taken as a constraint condition, the conflict-free space resources of the multiple robots are realized, the problem of multi-robot cooperative processing of large-scale complex structural members is effectively solved, and the cooperative planning efficiency and the task adaptability of the multiple robot system are improved.
Example two
The embodiment provides a task-level multi-robot collaborative movement planning system, which comprises:
the system comprises a configuration space detection unit, a configuration space detection unit and a configuration space detection unit, wherein the configuration space detection unit is used for carrying out collision detection on a random state of a configuration space of a multi-robot system by using a sampling method, if the random state contains collision or intersection, the random state is defined as a state of a collision configuration space, if the random state does not contain collision or intersection, the random state is defined as a state of a free configuration space, and collision-free path points are extracted from the state of the free configuration space;
the collision-free path planning unit is used for giving a limited collision-free path point in each robot monomer configuration space in the multi-robot system, continuously sampling in the configuration space by adopting a tree generation strategy, carrying out iterative compromise collision detection on a connecting line between any two collision-free path points until the state interval of the connecting line meets the given sampling precision, and planning a collision-free path of each robot monomer in the multi-robot system under the constraint conditions of a tree structure corresponding to the tree generation strategy and the collision-free path points;
and the optimal motion path unit is used for determining all motion sequences contained in the multi-robot system, determining the total motion cost of the motion sequences according to the sub-motion cost by taking the length of a collision-free path of each robot monomer as the sub-motion cost, screening out the motion sequence with the minimum total motion cost, defining the motion sequence as the optimal motion sequence, constructing a heuristic search algorithm, giving initial nodes, termination nodes, search directions and direction costs of the heuristic search algorithm on the basis of the optimal motion sequence, and obtaining the optimal motion path without collision of each robot monomer by setting a shortest path constraint condition.
The specific calculation method refers to the method described in the first embodiment.
EXAMPLE III
Referring to fig. 3, the task-level multi-robot collaborative motion planning apparatus provided in this embodiment includes a processor, a memory, and a computer program, such as a task-level multi-robot collaborative motion planning program, stored in the memory and executable on the processor. The processor, when executing the computer program, implements one of the steps of the above embodiments, such as the steps shown in fig. 1.
Illustratively, the computer program may be partitioned into one or more modules/units that are stored in the memory and executed by the processor to implement the invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program in the task-level multi-robot collaborative motion planning apparatus.
The task-level multi-robot cooperative motion planning device can be computing equipment such as a desktop computer, a notebook computer, a palm computer and a cloud server. The task-level multi-robot collaborative motion planning device may include, but is not limited to, a processor, and a memory. Those skilled in the art will appreciate that fig. 3 is merely an example of a task-level multi-robot collaborative motion planner and does not constitute a limitation of a task-level multi-robot collaborative motion planner, and may include more or fewer components than shown, or combine certain components, or different components, e.g., the task-level multi-robot collaborative motion planner may also include input-output devices, network access devices, buses, etc.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable Gate Array (FPGA) or other programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may be an internal memory element of the task-level multi-robot collaborative motion planning apparatus, such as a hard disk or a memory of the task-level multi-robot collaborative motion planning apparatus. The memory may also be an external storage device of the task-level multi-robot collaborative motion planning apparatus, such as a plug-in hard disk, a Smart Memory Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the task-level multi-robot collaborative motion planning apparatus. Further, the memory may also include both an internal storage unit and an external storage device of the task-level multi-robot collaborative motion planning apparatus. The memory is used for storing the computer program and other programs and data required by the task-level multi-robot collaborative motion planning device. The memory may also be used to temporarily store data that has been output or is to be output.
Example four
The present embodiments provide a computer-readable storage medium, which stores a computer program that, when executed by a processor, performs the steps of a method of one of the embodiments.
The computer-readable medium can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Further, the computer readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
The above embodiments are only for illustrating the technical idea and features of the present invention, and the purpose of the present invention is to enable those skilled in the art to understand the content of the present invention and implement the present invention, and not to limit the protection scope of the present invention by this. All equivalent changes or modifications made in accordance with the spirit of the present disclosure are intended to be covered by the scope of the present disclosure.
Claims (10)
1. A task-level multi-robot collaborative motion planning method is characterized by comprising the following steps:
s1, giving a limited number of collision-free path points in each robot monomer configuration space in a multi-robot system, continuously sampling in the configuration space by adopting a tree generation strategy, carrying out iterative compromise collision detection on a connecting line between any two collision-free path points until the state interval of the connecting line meets given sampling precision, and planning a collision-free path of each robot monomer in the multi-robot system under the constraint conditions of a tree structure corresponding to the tree generation strategy and the collision-free path points;
s2, determining all motion sequences contained in the multi-robot system, taking the length of the collision-free path of each robot monomer as a sub-motion cost, determining the total motion cost of the motion sequences according to the sub-motion cost, screening out the motion sequence with the minimum total motion cost, defining the motion sequence as an optimal motion sequence, constructing a heuristic search algorithm, giving initial nodes, termination nodes, search directions and direction costs of the heuristic search algorithm on the basis of the optimal motion sequence, and obtaining the collision-free optimal motion path of each robot monomer by setting a shortest path constraint condition.
2. The task-level multi-robot collaborative motion planning method according to claim 1, wherein S1 is preceded by S0: and performing collision detection on a random state of the configuration space by using a sampling method, if the random state contains collision or intersection, defining the random state as a state of a collision configuration space, if the random state does not contain collision or intersection, defining the random state as a state of a free configuration space, and extracting the collision-free path point from the state of the free configuration space.
3. The task-level multi-robot collaborative motion planning method of claim 2, wherein the collision detection includes: and the directed hierarchy bounding box approaches the environmental static obstacles in the multi-robot system and judges whether any two different objects in the multi-robot system collide or intersect.
4. The task-level multi-robot collaborative motion planning method according to claim 2, wherein the random sampling number of the sampling method is greater than or equal to 10000.
5. The task-level multi-robot collaborative motion planning method according to claim 1, wherein after the collision-free path is planned, a speed constraint and an acceleration constraint are introduced to smooth the collision-free path.
6. The task-level multi-robot collaborative motion planning method of claim 1, wherein for any of the initial nodes, the search direction has and has only "go" or "stop".
7. The task-level multi-robot collaborative motion planning method according to claim 6, wherein the directional cost corresponding to the search direction is: the sum of the motion costs of all the individual robots currently performing the "forward" operation.
8. A task-level multi-robot collaborative motion planning system is characterized by comprising:
the collision-free path planning unit is used for giving a limited collision-free path point in each robot monomer configuration space in the multi-robot system, continuously sampling in the configuration space by adopting a tree generation strategy, carrying out iterative compromise collision detection on a connecting line between any two collision-free path points until the state interval of the connecting line meets the given sampling precision, and planning a collision-free path of each robot monomer in the multi-robot system under the constraint conditions of a tree structure corresponding to the tree generation strategy and the collision-free path points;
and the optimal motion path unit is used for determining all motion sequences contained in the multi-robot system, determining the total motion cost of the motion sequences according to the sub-motion cost by taking the length of the collision-free path of each robot monomer as the sub-motion cost, screening out the motion sequence with the minimum total motion cost, defining the motion sequence as the optimal motion sequence, constructing a heuristic search algorithm, giving initial nodes, termination nodes, search directions and direction costs of the heuristic search algorithm on the basis of the optimal motion sequence, and obtaining the collision-free optimal motion path of each robot monomer by setting a shortest path constraint condition.
9. A task-level multi-robot collaborative movement planning apparatus comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor when executing the computer program implements the steps of the method according to any of claims 1 to 7.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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