Application Of Chaos Ant Colony Algorithm In Multi-Robots System Mission Planning

Mission Planning is essential for Multi-Robot Systems to improve the navigation performance of the robots and reduce their uncertainty when they are moving. In the areas of planet detection, intelligent transportation, and electronic assembly applications, Robot is a highly limited resource, and its navigation performance affects the efficiency of various application systems directly.Ant Colony Algorithm is a newly heuristic and cooperative simulation algorithm for evolution, inspired by the evolutionary ability of the real ants when they are searching food. It owns simple structure, strong robustness and better solution. Therefore, the Ant Colony Algorithm has been widely used in the field of traveller and so on.In the light of multi-robot task planning, this paper studies some algorithms to prevent prematurity and increase convergence speed of the algorithms. Analyzing all the methods by theory and application, we find they are effective. The main innovations include:1. Improving chaotic ant colony algorithm from following points: return optimization strategy, elite strategy and intersection removal strategy. And an Orthogonal-cluster Chaos Ant Colony Algorithm(OCACA) is presented for multi-robot mission planning. The idea of the algorithm is that first use orthogonal method to cluster the target points, then adopt chaos technology to optimize initial solution of the ant colony to improve individual quality and chaos perturbation is utilized to avoid the search being trapped in local optimum. It's the first time to apply OCACA in multi-robot mission planning, and the algorithm solve large-scale mission planning problem successfully. Simulation result show that the algorithm works well in improving the efficiency of executing tasks of multi-robots. Additionally, it's a novel ideal to solve multiple traveling salesmen problem.2. For the number of task could be added or reduced in a dynamic environment in robot tour construction problem. we used dynamic adaptability of the elastic net, proposed Elastic adapting Chaotic Ant Colony Algorithm(ECACA). Then, based on the characteristics of Multi-Robot System, we proposed Elastic adapting Chaotic Ant Colony Algorithm(MECACA) on the basis of OCACA. The simulation results demostrate the feasibility and efficiency of our algorithm.