Research On Task Allocation Optimization Method And Path Planning For Multiple Robots

With the complexity of social production movements and the dramatic changes in the demographic workforce,the multi-robot technology has gradually come to play a key role in industrial production processes.Both multi-robot task allocation(MRTA)and path planning are designed to achieve optimal task allocation and obstacle avoidance path calculation,and analysis in various industrial scenarios.The exploration of task planning algorithms has important theoretical research significance and engineering application value in the fields of warehouse logistics,equipment scheduling,and medical services.For the multiobjective requirement of optimal and balanced energy consumption of multi-robot,an energy penalty strategy is introduced into Genetic Algorithm(GA).An EP(Energy Penalty)-GA method for solving MRTA problem is proposed.The energy penalty EP-GA model is constructed by calculating and analyzing the total and average energy consumption of the robot.EP-GA method is combined with genetic manipulation.It constructs implementation scheme.The results of EP-GA method are verified and analyzed by simulation experiments.EP-GA and A*(AStar)algorithm are nested to realize the quadratic optimization of robot task sequence.An optimal obstacle avoidance path planning method is proposed in the multi-obstacle task scenario.According to the experimental data,a virtual scenario of multi-robot task planning is built on the Visual Components engine platform.The virtual scene realizes the visual simulation verification of task assignment and path planning.The main research contents of this paper are as follows.(1)The multi-objective task planning and task sequence optimization problems with optimal and balanced energy consumption of multiple robots are studied.The EP-GA method based on energy penalty strategy is proposed.An energy penalty model is established to clarify the calculation method of penalty energy and the penalty mechanism.In order to achieve energy consumption optimization and balance of robot in the barrier-free task scenario,the genetic operation of the EP-GA method includes to construct the fitness function of penalty energy,and to use two-segment chromosome coding,diverse crossover and variation.Finally,the MATLAB platform is used for simulation verification.(2)The optimal obstacle avoidance path problem in multi-obstacle task scenarios is studied.The nested method of EP-GA and A* algorithm is proposed.The nested A* algorithm is used to plan the optimal energy consumption path in complex task scenarios on the basis of the number of tasks does not change.The second optimization of path planning task sequence is realized according to energy penalty strategy.The nesting method of EP-GA and A* algorithm achieves energy consumption balance of the robot.Finally,the smoothing process of the planned trajectory is completed by both the path node redundancy reducing and the third-order B spline curve.(3)A visual demonstration and verification platform is built.It is based on Visual Components engine.According to the robot and task parameters in the algorithm instance,a multi-robot task planning three-dimensional scene is built.Visual demonstration and verification of multi-robot task planning path are realized by using this algorithm.