Multi-robot Task Allocation Strategy based on Particle Swarm Optimization and Greedy Algorithm

Abstract

In order to improve the resource utilization efficiency of heterogeneous multi-robots, minimize the execution time of multi-type tasks, effectively maintain the load balancing of robot resources, solve the problem of multiple resources and difficult to find a near-optimal solution for multi-robot collaborative planning, a multi-robot task allocation strategy combining improved particle swarm optimization and greedy (IPSO-G) algorithm is proposed. The strategy is divided into two steps: First, the improved particle swarm optimization algorithm is used to search for the combination of tasks and robots; after that, the greedy algorithm is used to sort the task execution order in the task combination, and generate the overall cost of task execution plan. Through continuous iteration of the above process, the strategy finally finds the optimal solution. In the computer simulation environment, one TSP example is used to verify the feasibility and effectiveness of the proposed strategy.