Task Allocation of Rescue Robots based on Path optimization in Chemical Disaster Environment

Abstract

Chemical disaster events are characterized by suddenness, danger and variability, while robots are inefficient and unsafe in path planning and task allocation under special and blocked chemical environment. In order to improve rescue speed and safety, combining the characteristics of the environment, a task framework for rescue robots is proposed in the paper. The improved ant colony algorithm is used to obtain the shortest path plan. The result of the shortest path is used in the task allocation process. A task allocation model with the goal of minimum time is proposed and solved by genetic algorithm with elite strategy (EGA). The simulation results show that the method proposed in this paper can be well adapted to the chemical disaster environment, and provides a better or sub-optimal solution for the task allocation problem of rescue robots.