A grouping method based on improved PSO for task allocation in rescue environment

Abstract

The environment after disaster is complicated and it is difficult to rescue the survivors timely and effectively. Robots can complete the rescue work instead of rescuers with high-efficiency and without limits of the disaster area. Based on this, a novel task allocation method for multi-robot in an environment after disaster is presented. Firstly, according to the locations and time constraints of the tasks, a new grouping method of the tasks is proposed to reduce the computational complexity. Following that, a new initial solution generation method is used to speed up the evolution. Finally, an improved particle swarm algorithm with the adaptive inertia weight and velocity update is developed to solve the grouping-based task allocation. The experimental results indicate that the proposed method can increase the success rate of rescue and speed up the rescue effectively.