Research On Task Allocation And Path Planning Algorithm For Mobile Charging

Energy is the material basis for human survival and development.Since human entered the industrial age,traditional energy sources such as coal and oil have been consumed in large quantities,causing energy shortage,air pollution and other problems that seriously threaten human living environment and sustainable development.Therefore,it is imperative to find and develop"new energy"which can replace traditional energy.Autonomous mobile robots（AMR）and Mobile Charging（MC）using"green electricity"have received strong support and attention from governments around the world because they can achieve"zero emission"of CO₂.As an important part of logistics warehousing,AMR plays a vital role in various factories,logistics warehousing and other environments,improving the production and operation efficiency of the whole factory and greatly reducing labor costs.However,at present,AMR still has problems such as short driving range,fixed charging pile location and high construction cost.In particular,"driving range"has to be considered in the process of building AMR warehousing and logistics system.From the perspective of the current technology development direction.On the one hand,starting from the AMR to increase the battery capacity,there are problems of equipment volume and input cost increase;On the other hand,MC is used to solve the"range anxiety"problem of AMR and improve the endurance without changing the AMR.In the logistics storage system,MC can provide charging service for AMR with insufficient energy anytime and anywhere,which can improve the working efficiency and flexibility of AMR.Firstly,AMR consumes energy in the working process.If it can be charged in time,the running time of the robot can be extended and the working efficiency can be improved.Secondly,MC can provide charging service for AMR anytime and anywhere,increasing the flexibility of the robot.In the charging process,MC mainly involves two aspects:charging task assignment and path planning.Reasonable charging task allocation can effectively improve the service efficiency of MC,and improve the work efficiency of AMR,so as to optimize the overall resource allocation of the system.When the MC receives the charging task assignment instruction,the MC first needs to plan the path from the starting point to the target point,avoid collision in the process,and realize the intellectualization and automation of the logistics storage system.Therefore,it is of great value to study the task allocation and path planning of MC.In view of the research on task allocation and path planning of MC,this paper mainly does the following work:（1）Build AMR charging model system.Firstly,the algorithm is used to generate random positions and tasks of AMR,and then the travel salesman algorithm is used to assign task points to AMR,so that the path distance is shortest,and the charging convergence point and separation point of MC are generated.（2）Design and implement MC task assignment algorithm.Firstly,the fireworks algorithm（FWA）was extended to discrete domain.The specific operations of discrete fireworks algorithm（DFWA）include:coding the charging task sequence;a new explosion operator is designed based on switching operation;the mutation operator is redesigned by combining the insert operation and the reverse operation.Finally,the DFWA algorithm and particle swarm optimization（PSO）were integrated,and the optimal moving path was solved on the Eil51 test set and the AMR charging model to verify the feasibility and effectiveness of the algorithm.（3）The MC path planning fusion algorithm is designed and implemented.Firstly,the A*and RRT-Connect algorithms are analyzed.Secondly,an improved A*algorithm is proposed to introduce the weight of obstacle information into the evaluation function to optimize the search node and path node.Finally,aiming at the problem that A*cannot deal with sudden obstacles in static environment,a fusion algorithm combining improved A*and improved RRT-Connect is proposed by taking advantage of the characteristics of RRT-Connect random sampling and fast planning,so as to improve the ability of the improved A*algorithm to avoid obstacles.（4）The greedy algorithm is used to extend A*algorithm to multi-objective point programming.Compared with the path planning that traverses all intermediate targets,the proposed algorithm can quickly plan the feasible path.The simulation results show that it is feasible to apply greedy thinking to multi-objective programming.