CN115016506A - Heterogeneous multi-agent networking cooperative scheduling planning method based on autonomous obstacle detouring - Google Patents
Heterogeneous multi-agent networking cooperative scheduling planning method based on autonomous obstacle detouring Download PDFInfo
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Abstract
The invention discloses a heterogeneous multi-agent networking cooperative scheduling planning method based on autonomous obstacle avoidance, which comprises the following steps: carrying out system configuration and transportation task management of multi-agent networking cooperative scheduling planning; each intelligent agent which is going to execute the transportation task in the system carries out collaborative path search to obtain a corresponding path list; the intelligent agent carries out motion planning to obtain an execution path; calculating to obtain a driving speed according to the execution path; the intelligent agent performs autonomous obstacle detouring in the operation process; and adjusting the time window. The invention realizes the cooperative solution of conflict between the intelligent agents and the autonomous obstacle avoidance by adopting a time window algorithm and introducing an autonomous obstacle avoidance technology of the intelligent agents, and compared with the prior art, the invention has the advantages of higher efficiency, flexibility and robustness, and more intelligent agents can be accommodated by the system.
Description
Technical Field
The invention belongs to the technical field of multi-agent scheduling and path planning, and particularly relates to an autonomous obstacle avoidance technology-based heterogeneous multi-agent networking cooperative scheduling planning method, which is a central and distributed fusion heterogeneous multi-agent networking cooperative scheduling planning method for introducing an intelligent agent autonomous obstacle avoidance technology.
Background
In recent years, due to the appearance of flexible manufacturing systems, the increase of the demand of customized robots and the increase of the demand of industrial automation of small and medium-sized enterprises, mobile intelligent bodies are rapidly developed in the fields of manufacturing industry, medical treatment, intelligent logistics and the like, and are widely applied.
Aiming at improving the production efficiency to the maximum extent, a user usually expects a plurality of intelligent agents to operate in a scene at the same time, which provides challenges for the safety of the intelligent agents in the scene and ensures that the plurality of intelligent agents execute transportation tasks efficiently without conflict in the scene, which is the first problem to be solved by a multi-agent system. The scheduling planning system is used for completing the task, the operation of a plurality of intelligent agents in a scene needs to be coordinated, and the operation efficiency of the system is improved as much as possible on the premise of ensuring the safety.
At present, in the prior art, two scheduling planning systems with different architectures are mainly adopted, namely a central architecture system and a distributed architecture system. Compared with a central architecture system, the distributed architecture system has the advantages of strong flexibility, strong expandability and the like, but in the system with the architecture, all the intelligent bodies are 'managed' respectively, hidden dangers are buried for the safety of the system, and unified management and control of users on the intelligent bodies in scenes are not facilitated, so that the system with the distributed architecture is rarely applied to real environments such as intelligent logistics, intelligent warehousing and the like at present. The central architecture system is widely applied at present, and has the advantages of convenient control, easy obtaining of an optimal solution and the like, but the intelligent capacity of the intelligent agents in the existing central architecture system is not fully utilized, the control center needs to complete all calculation tasks in the system, and all the intelligent agents in the system need to continuously exchange information with the control center, so that the control center of the existing central architecture system has large calculation pressure and large network communication burden, the improvement of the number of the intelligent agents which can be accommodated by the system is further limited, and the system has the defects of single point failure, and has poor flexibility and robustness.
Disclosure of Invention
The invention provides a heterogeneous multi-agent networking cooperative scheduling planning method based on autonomous obstacle-detouring technology and integrating a central type and a distributed type, which fully utilizes the autonomous intelligent capability of an agent, combines the advantages of a central type architecture and a distributed type architecture, and realizes a heterogeneous scheduling planning method which is more flexible, efficient and robust and can accommodate more agents.
The intelligent robot system comprises a control center, an intelligent agent and a data sharing end, wherein the intelligent agent is an intelligent robot for executing a transportation task in an application scene, an algorithm part of the control center is deployed on a computer, and the data sharing end is deployed on a server. The invention simultaneously considers the potential safety hazard brought by the intelligent agent driving of the repeated road sections and the opposite driving road sections in the paths, if the paths of the two intelligent agents comprise the same road section, the road section is the repeated road section of the two intelligent agents, and if the paths of the two intelligent agents comprise the same road section and the directions of the same road section passing through the road section are opposite, the road section is the repeated road section and the opposite driving road section of the two intelligent agents; the intelligent agent adopts the existing time window algorithm to carry out motion planning and tracking driving according to the result of the path search of the control center, when the intelligent agent encounters an obstacle, the intelligent agent utilizes the existing local path planning algorithm carried by the intelligent agent to carry out obstacle detouring, and after the obstacle detouring is finished, the time window checking and adjusting strategy designed by the invention is executed to ensure the cooperative relationship between the intelligent agents in the scene in the subsequent operation process; the data sharing end is used for storing a time window data structure required by collision avoidance between the intelligent agents, and each intelligent agent can independently access or modify data stored in the data sharing end.
The method mainly comprises five parts: the system comprises a task management part, a collaborative path searching part, a motion planning part, an autonomous obstacle detouring part and a time window adjusting part; the task management part and the collaborative path search part are realized in a control center, and the motion planning part, the autonomous obstacle avoidance part and the time window adjustment part are respectively realized in each intelligent terminal.
The task management part realizes dynamic addition of tasks and task allocation; the collaborative path searching part is realized based on dijkstra algorithm, and meanwhile, the potential safety hazard brought by the intelligent agent driving of the repeated road section and the opposite driving road section in the path is considered, so that the path planned for the intelligent agent by the method is safer and more reliable; in the motion planning part, each agent adopts a time window algorithm to avoid the conflict with other agents in the scene according to the search result of the control center cooperating with the path, and obtains an execution instruction specifically reaching a speed level; the invention innovatively introduces the autonomous planning of the intelligent agent into the multi-intelligent-agent scheduling and planning method and the multi-intelligent-agent cooperative application system, and when the intelligent agent encounters an obstacle in the driving process, the intelligent agent adopts a local path planning algorithm of an autonomous obstacle-avoiding part to perform autonomous obstacle avoidance; the autonomous obstacle avoidance breaks through the driving behavior planned by the motion planning part, so that the time window adjustment part needs to check and adjust the time window after the autonomous obstacle avoidance is finished.
The invention provides a heterogeneous multi-agent networking cooperative scheduling planning method based on an autonomous obstacle-circumventing technology, which comprises the following specific steps:
the system comprises a control center, an intelligent agent and a data sharing end; and the task management part is used for adding a transportation task to the system and distributing the task.
The intelligent agent is an intelligent robot for executing a transportation task in a scene; the control center is used for performing task allocation and collaborative path search; the data sharing end is used for storing a time window data structure required by collision avoidance between the intelligent agents; each agent can independently access or modify the data stored in the data sharing end.
The configuration of the multi-agent networking cooperative scheduling planning system specifically comprises the starting of a control center and each agent, the setting of scene maps in the control center and each agent and the initialization of a time window data structure of a data sharing end. The scene map comprises node information and road section information, the nodes need to be set at fixed intervals (such as 10% of the scene width) according to the length and the width of the scene, some key points (such as goods picking points, goods throwing points and the like) in the scene also need to be set as the nodes, and if an existing driving route exists in the scene, the positions of the nodes need to be set at fixed intervals on the existing driving route; the road sections are line sections formed by two adjacent nodes, and a plurality of road sections are connected to form a path.
The transportation task can take any node in the map as a task end point and take the position of a corresponding agent executing the task as a task starting point; in specific implementation, the tasks can be distributed according to the sequence of adding the tasks.
And 2, a cooperative path searching part, namely performing cooperative path searching on each agent to be subjected to task execution to obtain a corresponding path list.
All loop-free PATHs between a task starting point and a task ending point are obtained based on a dijkstra algorithm and are stored in a PATH list PATH, meanwhile, potential safety hazards brought by the fact that a repeated road section and an opposite-direction running road section are taken into consideration, if the PATHs of the current intelligent agent and other intelligent agents comprise the same road section, the road section is the repeated road section of the current intelligent agent, and if the PATHs of the current intelligent agent and other intelligent agents comprise the same road section and the directions of the same road section passing through the road section are opposite, the road section is the repeated road section and the opposite-direction running road section of the current intelligent agent; in specific implementation, each PATH in the PATH list PATH is checked, and a certain PATH in the PATH list is set as a PATH 0 If path is 0 The number of repeated sections and paths of the path being executed by any other agent 0 The ratio of the total number of the upper sections is larger than a set threshold (such as 0.4) or the number of the opposite driving sections of the path which is executed by any other intelligent agent and the path 0 Upper main roadThe ratio of the number of segments is greater than a set threshold (e.g., 0.3), then path 0 For unavailable path, will path 0 Is removed from the PATH list PATH.
the intelligent agent obtains the result of the collaborative path search performed by the control center, performs motion planning by adopting the existing time window algorithm, performs time window insertion operation on each path, and selects a path capable of completing the task earliest as an execution path.
and 3, calculating the running speed of the execution path obtained in the step 3 according to the length of each road section on the path and the time window insertion result, and issuing the running speed to a chassis of the intelligent agent, wherein the intelligent agent runs according to the speed.
And 5, an autonomous obstacle detouring part, namely in the running process of the intelligent agent, if the intelligent agent meets an obstacle, the intelligent agent executes the detouring operation of the obstacle according to a local path planning algorithm of the autonomous obstacle detouring part, and when the intelligent agent is specifically implemented, the local path planning algorithm adopts the existing Dynamic time Window algorithm (DWA).
And 6, a time window adjusting part, namely, after the autonomous obstacle avoidance of the intelligent agent is finished, a time window checking and adjusting strategy designed by the method needs to be adopted to ensure the cooperative relationship between the intelligent agents in the scene in the subsequent operation process because the autonomous obstacle avoidance time of the intelligent agent cannot be determined.
Through the steps, a plurality of intelligent agents can flexibly and efficiently execute tasks in a scene.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a heterogeneous multi-agent networking cooperative scheduling planning method based on the integration of a central type and a distributed type on the basis of an autonomous obstacle detouring technology, which realizes the cooperative solution of conflicts among agents and the autonomous obstacle detouring by adopting a time window algorithm and the autonomous obstacle detouring technology introduced into the agents.
The heterogeneous multi-agent networking cooperative scheduling planning method provided by the invention has the following advantages:
1) and autonomous local path planning of an intelligent agent is introduced, so that the calculation pressure of a control center is obviously reduced.
2) The intelligent agent has the autonomous obstacle-avoiding capability, and the control center does not need to monitor the running state of the intelligent agent all the time, so that the burden of network communication is remarkably reduced.
3) The reduction in the computational pressure and network communication burden of the control center further facilitates an increase in the number of agents that the system can accommodate.
4) The operation of the system does not depend on a control center any more, namely the defect of single point failure of the traditional central architecture system is overcome, and the system has stronger robustness.
5) The intelligent agent has the capability of autonomously dealing with the emergency and the abnormal condition, and has stronger flexibility compared with the strategy of dealing with the emergency by re-planning through the control center in the traditional central type framework system.
Drawings
Fig. 1 is a system architecture adopted by the method of the present invention, including a control center, an agent and a data sharing end.
FIG. 2 is a flow chart diagram of a task management part task allocation method of the present invention.
FIG. 3 is a schematic diagram of an exemplary scenario used in describing embodiments of the present invention.
Fig. 4 is an example of a time window data structure of the data sharing end of the present invention.
FIG. 5 is a block diagram of the algorithm flow for path checking in conjunction with the path search section of the present invention
Figure 6 shows an example of a time window insertion operation performed by the motion planning section of the present invention.
Fig. 7 is a partial velocity sampling space schematic of the local path planning of the present invention.
Fig. 8 is a schematic diagram of an autonomous obstacle detouring path planned autonomously by an autonomous obstacle detouring portion agent according to the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
The invention provides a heterogeneous multi-agent networking cooperative scheduling planning method based on the integration of a central mode and a distributed mode of an autonomous obstacle-detouring technology, which realizes that a plurality of agents execute tasks efficiently, flexibly and without conflict in scenes such as intelligent logistics, intelligent warehousing and the like. The method mainly comprises five parts: the system comprises a task management part, a collaborative path searching part, a motion planning part, an autonomous obstacle detouring part and a time window adjusting part; the collaborative path searching part fully considers the potential safety hazard brought by the intelligent agent driving of the repeated road section and the opposite driving road section, so that the path planned by the system for the intelligent agent is safer and more reliable; in addition, the method is a heterogeneous method with the integration of the central architecture and the distributed architecture, makes full use of the autonomous intelligence capability of the intelligent agent, combines the technical advantages of the central architecture and the distributed architecture, and greatly improves the flexibility, robustness and expandability compared with the traditional central architecture system.
The task management part realizes dynamic addition of tasks and task allocation. The policy flow for task assignment is illustrated with reference to fig. 2. The collaborative path searching part is realized based on dijkstra algorithm, and meanwhile, potential safety hazards brought by intelligent body driving of repeated road sections and opposite driving road sections in the path are considered, so that the path planned by the system for the intelligent body is safer and more reliable. The motion planning part realizes the avoidance of conflict among the intelligent agents, and each intelligent agent respectively accesses the data sharing end and carries out time window insertion operation to obtain an execution instruction specifically reaching a speed level. The autonomous obstacle detouring part realizes autonomous detouring of the intelligent body when meeting the obstacle, namely, a barrier detouring path is drawn by using a local path planning and calculating rule. The time window adjusting part realizes time window check and adjustment after the autonomous obstacle avoidance is finished so as to guarantee the cooperative relationship among the intelligent agents in the scene in the subsequent operation process.
For the convenience of understanding, when the embodiment of the method of the present invention is introduced, the scenario shown in fig. 3 is taken as an example, and it can be seen from the figure that there are 4 AGENTs in the scenario, respectively AGENT 1 、AGENT 2 、AGENT 3 And AGENT 4 Wherein only AGENT 3 Is in an idle state.
The method comprises the following steps:
The intelligent agent is an intelligent robot for executing a transportation task in a scene; the control center is used for performing task allocation and collaborative path search; the data sharing end is used for storing a time window data structure required by collision avoidance between the intelligent bodies; each agent can independently access or modify the data stored in the data sharing end.
The configuration of the multi-agent networking cooperative scheduling planning system specifically comprises the starting of a control center and each agent, the setting of scene maps in the control center and each agent and the initialization of a time window data structure of a data sharing end. The control center and all the agents are started, namely, the computer for controlling the deployment of the algorithm part of the control center and all the agents are started; the control center and the scene maps in the intelligent agents are set by pointing to the control center and inputting the same map files to the intelligent agents, so that the control center and the intelligent agents can use the same scene maps; the time window data structure maintains a time window vector for each road segment in the map, each element in the time window vector corresponds to a period of time that an agent occupies the road segment, the time window data structure is empty initially, and fig. 4 shows an example of the time window data structure in the system operation process.
Adding a task taking any node in the map as a terminal point into the system, and performing task allocation according to the sequence of task addition, as shown in fig. 2, a specific task allocation strategy is as follows:
1) if no idle agent exists in the current system, the task is allocated in a suspension mode;
2) and if the current system has idle agents, distributing the task to the agent with the current position closest to the task end point according to the principle of closeness.
Taking the scenario shown in fig. 3 as an example, if a task with an end point being the node 21 is added, the task will be allocated to AGENT according to the task allocation policy 3 And (6) executing.
And 2, a cooperative path searching part, namely performing cooperative path searching on each agent to be subjected to task execution to obtain a corresponding path list.
The basic dijkstra algorithm is executed as follows:
1) acquiring a task starting point s and a task end point e;
2) two sets are defined: s and U. S is used for recording all nodes with shortest paths obtained in the map, and U is used for recording nodes with shortest paths not obtained yet. Two vectors are defined: d and P, the shortest distance from the starting point s to other nodes in the map is stored in the vector D in the order of the node numbers, and the last node of the node z in the shortest route from the task starting point s to some other node z in the map is stored in the vector P in the order of the node numbers. Initially, only a task starting point S exists in S, and all nodes in a map except the task starting point S are contained in U; the value of each element in D is the distance from the task starting point s to each other node in the map (if a certain node v in U is not adjacent to the task starting point s, the corresponding element value in D is infinity);
3) selecting a node k closest to the task starting point S from the U, adding the node k into the S, and removing the node k from the U;
4) updating the distance from each node in the U to the starting point s (considering that the distance from the starting point s to a certain node v in the U is possibly reduced by taking the node k as an intermediate point), and updating the vector D and the vector P;
5) repeating the step 3) and the step 4) until all nodes in the map are traversed;
6) and (4) from the end point e, reversely searching the vector P, and acquiring the shortest path from the starting point s to the end point e.
A number of agents are shared in the system, except the current agentThe length of the path currently executed by the ith agent in the A-1 agents in vitro is l i (i.e., the path has l in common i Node), the path being executed by the ith agent among the other a-1 agents may be represented as
The invention carries out the steps of collaborative path search based on dijkstra algorithm as follows:
1) and (3) solving the shortest path between two points of a task starting point and a task end point by adopting a basic dijkstra algorithm: s → k 1 →k 2 →…→k n → e; wherein k is 1 ～k n All nodes are nodes on the path;
2) let k 0 ＝s，k n+1 E, for each node k on the path except the task end e m (m is 0,1,2 …, n) and k is m Total r neighboring nodes, traverse k m Each adjacent node v q (q ═ 1,2, …, r); if v is q ≠k m+1 In v with q Using e as task end point as task starting point, adopting basic dijkstra algorithm to obtain v q Shortest path to e: v. of q →n 1 →n 2 →…→n p →e，n 1 ～n p If all the nodes are nodes on the path, obtaining another path between s and e: k is a radical of 0 →…→k m →v q → … → e, add it to the PATH list PATH.
3) Step 2) after the execution is finished, all loop-free PATHs from the task starting point S to the task ending point e are stored in the PATH list PATH, S PATHs are set in the PATH list PATH in total, and for each PATH z (z is 1,2, …, S), and β is used respectively z And gamma z To record PATH z PATH for overlapping road section and opposite driving road section of PATH being executed by other agent z Maximum value of the ratio of the total number of stages. The algorithm flow is shown with reference to fig. 5.
Path setting PATH z Has a length of l (i.e. PATH) z Total of l nodes above) forh 1,2, …, l 1, a-1 agent paths N in the examination system in addition to the current agent i (i-1, 2, … a-1), then for w-1, 2, …, l i -1，l i Is path N i Length of (i.e. total l on the path) i A node); respectively searching the routing nodes from the map information
And node
The road section formed by the connection is marked arc h And a node
And node
The road section formed by the connection is marked arc w If arc h ＝arc w Then PATH z And path N i Number of repeating segments of 1, if arc h ＝arc w And is
Then PATH z And path N i The number of opposite travel stages of (1) is also added. Solving PATH for the set N of current execution PATHs of other agents z The maximum value of the repeated road section and the opposite driving road section of the A-1 paths is calculated to obtain beta z And gamma z If beta is z Not less than 0.4 or gamma z If the PATH is more than or equal to 0.3, the PATH is considered as PATH z If there is a greater risk of collision with other agents currently performing tasks in the system, the PATH will be followed z Removed from the path list.
In the scenario shown in FIG. 3, assume AGENT 1 、AGENT 2 And AGENT 4 The current execution paths are: 10 → 3 → 24 → 12 → 11 → 18 → 19, 19 → 15 → 13 → 14 and 16 → 21 → 26 → 23 → 24 → 25, AGENT 3 Planned Path List PATH E Comprises the following 3 stripsPath:
①22→24→3→2→9→16→21
②22→18→14→13→12→19→24→23→26→21
③22→24→12→11→18→23→26→21
after the routing check is performed on the 3 PATHs, the PATH list PATH E There is only one path available, i.e., path I, which is removed because it is associated with AGENT 4 The ratio of the opposite driving road section to the total road section of the path (c) is more than 0.3, and the reason why the path (c) is removed is that the path (c) is equal to the AGENT 1 The ratio of the number of repeated sections between the paths to the total number of sections of the paths is greater than 0.4.
And 3, a motion planning part, namely the intelligent agent obtains a result (the path list obtained in the step 2) of the collaborative path search of the control center, performs motion planning by adopting the existing time window algorithm, performs time window insertion operation on each path, and selects a path capable of completing the task earliest as an execution path.
After the control center sends the result of the collaborative path search to the intelligent agent, the intelligent agent firstly needs to access a data sharing end to obtain a current time window structure, the time window data structure maintains a time window vector for each road section in the map, each element in the time window vector corresponds to a period of time that the intelligent agent occupies the road section, and then for each path received by the intelligent agent, the following operations are executed:
for each segment of road section alpha on each path m (m＝1，2，…，n)：
1) With the shortest length of the time window (the shortest length of the time window being the length of the road section a) m The length of the agent to the maximum travel speed of the agent). Finding an insertion position satisfying the following two conditions:
(ii) on a road section a m A position may be used as an insertion position if the difference between the start time of a time period subsequent to the position and the end time of a time period previous to the position is sufficient to accommodate the length of the shortest length time window.
If m is not equal to 1, the time for entering the section of road section is after the time for releasing the section of road section on the path of the intelligent agent.
2) If m is not equal to 1, judging whether the time for releasing the last section of the road section on the path of the intelligent agent is equal to the time for entering the section of the road section. If not, the release time of the previous section of road section is prolonged to be equal to the entry time of the section of road section.
3) It is checked whether there is an overlap between the time windows. If not, the insertion is successful, and the next section of road section is continuously operated. If the two paths are overlapped, the previous path segment on the path is returned, and the step 1) is returned.
The time window insertion operation is carried out on a path, namely, the path comprises a plurality of road sections a m And after all the road sections on the path find the insertion positions meeting the conditions in the time window vector, the time window insertion operation of the path is successful.
After the time window insertion operation is successfully carried out on one path, the time when the intelligent agent releases the last section of road section on the path is the time when the intelligent agent adopts the path to execute the task and is expected to finish the task. And performing time window insertion operation on each path received by the intelligent agent, and then selecting one path which is expected to complete the task earliest as a final path for the intelligent agent to execute the task.
By performing time window insertion operation on each path received by the intelligent agent, after the final execution path is selected, the intelligent agent needs to send a message to the data sharing end, and the time window insertion result of the selected execution path is synchronously updated to the data sharing end.
Still taking the scenario shown in fig. 3 as an example, the AGENT is found in the collaborative path search section in step 2 3 There is only one candidate route, i.e. 22 → 24 → 3 → 2 → 9 → 16 → 21, the links on the route are a 9 →a 16 →α 7 →α 1 →a 17 →a 22 Let take AGENT 3 After receiving the candidate path sent by the control center, and before performing the motion planning (i.e. performing the time window insertion operation), if the time window data structure of the road segment is shown as (1) in fig. 6, performing the motion planningThereafter, the time window data structure of the above-described link is shown in (2) in fig. 6.
And 4, a motion planning part, namely calculating the running speed of the execution path obtained in the step 3 according to the length of each road section on the path and the time window insertion result, and issuing the running speed to a chassis of the intelligent agent, wherein the intelligent agent runs according to the speed.
After the intelligent agent completes the time window inserting operation according to the current time window structure, the length of each section of road on the execution path and the running time of the intelligent agent on the section can be obtained, the speed of the intelligent agent on each section of road in the execution path to run is further calculated, and the speed is issued to the chassis execution mechanism of the intelligent agent, so that the tracking running of the intelligent agent can be realized.
And 5, an autonomous obstacle detouring part, namely when the intelligent body meets an obstacle, the intelligent body conducts autonomous obstacle detouring by using a local path planning algorithm.
When the intelligent body encounters an obstacle in front of the intelligent body in the driving process, different from a strategy of only depending on a control center to conduct re-planning in a traditional central type architecture system, the intelligent body in the method adopts a local path planning algorithm to conduct autonomous obstacle detouring, a target point of the autonomous obstacle detouring is a next node of a node where the intelligent body is located on a running path, and particularly, when the next node on the running path is a last node of the path, the target point of the autonomous obstacle detouring is a task terminal point. In implementation, the method specifically adopts the existing Dynamic time Window algorithm (DWA), which is a Dynamic decision algorithm based on a limited observation range, and the algorithm comprises three steps:
1) velocity sampling
The speed sampling space of an agent is mainly limited by the following factors: maximum and minimum speed limits of the agent itself; the limitation of the maximum acceleration, maximum deceleration, which the agent is capable of achieving, as shown with reference to fig. 7, results in the speed that the agent can achieve in the next forward simulation cycle being limited to a dynamic time window.
2) Trajectory scoring
In the sampling velocity group, several groups of trajectories are possible, so that an evaluation function is adopted for evaluating each trajectory, and the evaluation function is as follows:
H(u，ω)＝α·goal(v，ω)+β·dist(v，ω)+γ·path(v，ω) (4.5)
wherein, the goal (v, ω) is a target evaluation function for evaluating the difference between the position of the robot reaching the end of the simulated trajectory and the position of the target point and the difference between the orientation angle of the robot reaching the end of the simulated trajectory and the target azimuth angle at the currently set sampling speed, and the score is lower when the difference is larger; dist (v, ω) is a distance evaluation function for evaluating a distance between the trajectory and the nearest obstacle, and the smaller the distance between the trajectory and the nearest obstacle, the larger the value; path (v, ω) is a path evaluation function for evaluating the shortest distance between a point on the sampling trajectory and the global path, and the evaluation function is mainly used for guiding the agent to travel according to the result of the global path search as much as possible so as to reach the target point earlier.
It should be noted that, because parameters of 3 evaluation functions such as good (v, ω), dist (v, ω), and path (v, ω) are different, before calculating a final evaluation value H (v, ω), 3 parts of evaluation function values need to be normalized, and then multiplied by corresponding coefficients α, β, and γ, and summed, so as to obtain a score of a trajectory of the group of velocities in a next forward simulation period, and a group of velocities with the highest score is selected from all sampling trajectories as an execution velocity of the agent in the next period.
3) Speed distribution
Sampling multiple groups of speeds in a speed space, simulating tracks of the multiple groups of speeds in the next forward simulation period, scoring all tracks, selecting the track with the highest score, issuing the speed corresponding to the track with the highest score to the intelligent body, driving the intelligent body in the next period according to the speed, and repeating the process until the intelligent body reaches a target point.
And 6, a time window adjusting part, namely after the obstacle detouring of the intelligent bodies is finished, adopting a time window checking and adjusting strategy designed by the method of the invention to ensure the cooperative relationship among the intelligent bodies in the scene in the subsequent operation process. .
Because the autonomous obstacle avoidance of the intelligent agent is a time-consuming operation and the required time cannot be estimated in advance, if the autonomous obstacle avoidance of the intelligent agent does not reach the task end point after the obstacle avoidance is finished, the time consumed by obstacle avoidance needs to be checked after the obstacle avoidance is finished, and if the consumed time exceeds half of the time window protection time, the time window needs to be adjusted. If the time consumed by autonomous obstacle avoidance is δ and the time window protection time is w (the time window protection time refers to the time which should be the least difference between the time windows of two agents if the two agents need to pass through a road segment), the specific strategy is as follows:
1)
in this case, the time consumed by the autonomous obstacle avoidance is less than or equal to half of the time window protection time, and the intelligent agent can compensate the time consumption by adjusting the driving speed in the subsequent driving, so that the time window does not need to be adjusted.
2)
In this case, the time consumed by autonomous obstacle avoidance exceeds half of the time window protection time, and if the intelligent agent only needs to adjust the speed to make up for the time error in the subsequent driving, there is a great risk of conflict, so that the time window needs to be adjusted and the motion planning needs to be performed again.
The specific method for adjusting the time window and performing the motion planning again comprises the following steps:
1) and accessing the data sharing end, and clearing the time window data of the current agent.
2) And obtaining the time window structure after the current intelligent agent time window data is cleared.
3) And (4) according to the current position of the intelligent agent, turning to the step 3 to determine a subsequent driving path of the intelligent agent and performing motion planning again.
Still taking the scenario shown in FIG. 3 as an example, when AGENT 3 When the vehicle travels to the position of the node 24, it is detected that an obstacle exists on the front travel path, the intelligent AGENT immediately performs autonomous obstacle avoidance by using the local path planning algorithm in step 5, and fig. 8 shows an AGENT 3 A detour path that may be planned; AGENT 3 When the obstacle detouring is finished and the position of the node 3 is reached, if the obstacle detouring consumes 5.2s, namely delta is 5.2, the time window protection time is set to 8s, namely w is 8, because the obstacle detouring is finished and the position of the node 3 is reached
So that AGENT follows 3 The current time window data structure is cleared of its own time window, and the subsequent travel path (3 → 2 → 9 → 16 → 21) is then subjected to the movement planning again.
It is noted that the disclosed embodiments are intended to aid in further understanding of the invention, but those skilled in the art will appreciate that: various substitutions and modifications are possible without departing from the spirit and scope of the invention and appended claims. Therefore, the invention should not be limited to the embodiments disclosed, but the scope of the invention is defined by the appended claims.
Claims (5)
1. A heterogeneous multi-agent networking cooperative scheduling planning method based on autonomous obstacle avoidance is characterized by comprising the following steps:
step 1: carrying out system configuration and transportation task management of multi-agent networking cooperative scheduling planning;
the system comprises a control center, an intelligent agent and a data sharing end; the intelligent agent is an intelligent robot for executing a transportation task in a scene; the control center is used for performing task allocation and collaborative path search; the data sharing end is used for storing a time window data structure required by collision avoidance between the intelligent agents; each agent can independently access or modify the data stored in the data sharing end;
the system configuration of the multi-agent networking cooperative scheduling planning comprises the following steps: starting a control center and each intelligent agent, setting a scene map in the control center and each intelligent agent and initializing a time window data structure of a data sharing end;
the scene map comprises nodes and road sections; setting the key points of the scene as nodes; the key points of the scene comprise a goods picking point and a goods throwing point; in addition, the positions of the nodes are set at fixed intervals according to the length and the width of the scene; if the existing driving route exists in the scene, setting the positions of the nodes at fixed intervals on the existing driving route; the road sections of the scene are line sections formed by two adjacent nodes, and a plurality of road sections are connected to form a path;
the transportation task management is to point to a system to add a transportation task and distribute the task; the transportation task takes any node in the scene map as a task end point and takes the position of a corresponding agent executing the task as a task starting point;
step 2: each intelligent agent which is about to execute the transportation task in the system scene carries out collaborative path search to obtain a corresponding path list; the method comprises the following steps:
21) and (3) solving the shortest path between a task starting point and a task end point, and recording as: s → k 1 →k 2 →…→k n → e; wherein s is a task starting point; e is a task end point; k is a radical of 1 ～k n All nodes are nodes on the path;
22) let k 0 ＝s，k n+1 E, for each point k on the path except the task end point e m Where m is 0,1,2 …, n, k m Total r neighboring nodes, traverse k m Each adjacent node v q Q is 1,2, …, r; if v is q ≠k m+1 In v with q As a task starting point, and e as a task end point, v is obtained q Shortest path to e: v. of q →n 1 →n 2 →…→n p →e，n 1 ～n p If all the nodes are nodes on the path, obtaining another path between s and e: k is a radical of 0 →…→k m →v q → … → e, add it to the path list;
23) step 22), after the execution is finished, all loop-free paths from the task starting point s to the task ending point e are stored in the path list; for each path, calculating and recording the maximum value of the ratio of repeated road sections and opposite driving road sections of the path and the paths being executed by other agents to the total number of the road sections of the path;
the repeated section means that: if the paths of the current agent and other agents contain the same section of road, the road is the repeated section of the current agent;
the repeated section and the opposite direction driving section refer to: if the paths of the current agent and other agents comprise the same section of road and the direction of the road is opposite, the road is the repeated road and the opposite driving road of the current agent;
the method comprises the following steps:
231) let the PATH list PATH have S PATHs in total, and for each PATH z 1,2, …, S, respectively, beta z And gamma z To record the PATH z PATH for overlapping road section and opposite driving road section of PATH being executed by other agent z The maximum value of the ratio of the total number of stages;
232) path setting PATH z Has a length of l, i.e. the PATH z The number of the nodes is l; for h-1, 2, …, l-1, a-1 agent paths N in the examination system in addition to the current agent i I 1,2, … a-1, then w 1,2, …, l i -1，l i Is path N i Respectively finding the route nodes from the map
And node
The link formed by the connection is denoted arc h And a routing node
And node
The road section formed by the connection is denoted as arc w ；
233) If arc h ＝arc w Then PATH z And path N i 1 is accumulated in the number of the repeated sections; if arc h ＝arc w And is provided with
Then PATH z And path N i The number of opposite driving sections is also accumulated to be 1;
234) solving PATH for the set N of current execution PATHs of other agents z Calculating beta according to the maximum value of the repeated road section of the A-1 paths and the maximum value of the opposite driving road section z And gamma z (ii) a For beta is z And gamma z Respectively setting threshold values; if beta is z Exceeding the corresponding threshold value or gamma z If the corresponding threshold value is exceeded, the PATH PATH is set z Removing from the path list;
and step 3: the intelligent agent carries out motion planning to obtain an execution path;
according to a path list result obtained by collaborative path search, performing motion planning by adopting a time window algorithm, performing time window insertion operation on each path, and selecting a path capable of completing a task earliest as an execution path;
and 4, step 4: calculating to obtain a driving speed according to the execution path;
calculating the running speed on each road section according to the length of each road section on the execution path obtained in the step 3 and the time window insertion result, and sending the running speed to a chassis of the intelligent agent, wherein the intelligent agent runs according to the speed;
and 5: the intelligent agent carries out autonomous obstacle avoidance in the operation process: if the intelligent agent meets the obstacle, the intelligent agent bypasses the obstacle according to a local path planning algorithm and reaches an autonomous obstacle-bypassing target point; the target point of the autonomous obstacle detouring is the next node of the node where the intelligent agent is located on the operation path;
step 6, time window adjustment: after the autonomous obstacle avoidance of the intelligent agent is finished, if the intelligent agent does not reach a task end point, a time window checking and adjusting strategy is designed and adopted to ensure the cooperative scheduling among the intelligent agents in a scene in the subsequent operation process of the intelligent agent; the method comprises the following steps:
6A) when the time consumed by the intelligent agent autonomous obstacle avoidance is less than or equal to half of the time window protection time, the time window does not need to be adjusted; in the subsequent running process, the intelligent agent can compensate the consumption of time by adjusting the running speed;
the time window protection time refers to the time which is required to be separated from the time windows of two intelligent agents at least if the two intelligent agents need to pass through a road section;
6B) when the time consumed by the autonomous obstacle avoidance exceeds half of the time window protection time, the time window is adjusted according to the following method:
accessing a data sharing end, and clearing time window data of the current agent;
obtaining a time window structure after the current agent time window data is cleared;
turning to the step 3 to determine a subsequent driving path of the intelligent agent and carrying out motion planning again according to the current position of the intelligent agent;
through the steps, the heterogeneous multi-agent networking cooperative scheduling planning based on autonomous obstacle avoidance can be realized.
2. The heterogeneous multi-agent networking cooperative scheduling planning method based on autonomous obstacle avoidance as claimed in claim 1, wherein in step 234), β is set z 0.4 of the threshold value; setting up gamma z The threshold value of (2) is 0.3.
3. The heterogeneous multi-agent networking cooperative scheduling planning method based on autonomous obstacle avoidance as claimed in claim 1, wherein step 21) specifically uses dijkstra algorithm to find a shortest path between two points.
4. The heterogeneous multi-agent networking cooperative scheduling planning method based on autonomous obstacle avoidance as claimed in claim 1, wherein step 3 is to perform motion planning by using a time window algorithm and calculate to obtain an execution path; the method comprises the following steps:
firstly, the intelligent agent accesses a data sharing end to obtain a current time window data structure; the time window data structure maintains a time window vector for each road section in the map, and each element in the time window vector corresponds to a period of time for which an agent occupies the road section; then, for each path received by the agent, the following operations are executed:
for each segment of road a on each path m ，m＝1,2,…,n：
31) Inserting with a time window of shortest length; the shortest length of the time window is the road section a m The length of the intelligent agent and the maximum driving speed of the intelligent agent; finding an insertion position satisfying the following two conditions:
(ii) on a road section a m If the difference between the starting time of the time period after a certain position and the ending time of the time period before the position is enough to accommodate the length of the time window with the shortest length, the position can be used as an insertion position;
if m is not equal to 1, the time for entering the section of road section is after the time for releasing the section of road section on the path of the intelligent agent;
32) if m is not equal to 1, judging whether the time for releasing the last section of road section on the path of the intelligent agent is equal to the time for entering the section of road section; if not, prolonging the release time of the previous section of road section to be equal to the entry time of the section of road section;
33) checking whether there is an overlap between the time windows; if the sections are not overlapped, the insertion is successful, and the next section of road section is continuously operated; if the road sections are overlapped, returning to the previous section of the road section on the path and returning to the step 31);
and after selecting the final execution path by performing time window insertion operation on each path received by the intelligent agent, the intelligent agent sends a message to the data sharing end, and the time window insertion result of the selected execution path is synchronously updated to the data sharing end.
5. The heterogeneous multi-agent networking cooperative scheduling planning method based on autonomous obstacle avoidance of claim 1, wherein in step 5, the agent performs autonomous obstacle avoidance in the operation process according to a local path planning algorithm; specifically, a dynamic time window algorithm DWA is adopted, and the method comprises the following steps:
51) sampling speed;
52) and (3) track scoring:
scoring each trajectory by using an evaluation function; the evaluation indexes include: the distance between the position of the tail end of the track and the position of the target point, the difference between the orientation angle when the tail end of the track is reached and the target azimuth angle, the distance between the track and the nearest obstacle, and the nearest distance between a point on the sampling track and the global path;
53) and (3) speed release:
scoring all tracks and selecting the track with the highest score;
then sending the speed corresponding to the track with the highest score to the intelligent agent;
the intelligent agent runs at the speed in the next period;
and repeating the process until the intelligent agent reaches the target point of autonomous obstacle avoidance.
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