Cooperative Control For Swarm Robots Based On Bio-inspired Intelligent Algorithms

As the development of the robots technology, the robot using mode is changing, from component type unit application to the system application. The use of robots has gradually entered the people’s life. At the same time, swarm robot system got more and more attentions and researches because of its better to meet people’s needs than single ones. Related research and applications have sprung up endlessly in recent years. The history, developments and applications for swarm robots are introduced. Some major subjects in proceed are analyzed and discussed. Firstly, we propose a decentralized control algorithm of swarm robot for target search and trapping inspired by bacteria chemotaxis. First, a local coordinate system is established according to the initial positions of the robots in the target area. Then the target area is divided into Voronoi cells. After the initialization, swarm robots start performing target search and trapping missions driven by the proposed bacteria chemotaxis algorithm under the guidance of the gradient information defined by the target. Simulation results demonstrate the effectiveness of the algorithm and its robustness to unexpected robot failure. Compared with other commonly used methods for distributed control of swarm robots, our simulation results indicate that the bacteria chemotaxis algorithm exhibits less vulnerability to local optimum, and high computational efficiency. Secondly, a dynamic Voronoi-based algorithm to solve the area coverage searching problem in decentralized control of sensors-based swarm robots. In the beginning, local coordinate system is established by initial position and the target area of the swarm robots by BC algorithm. Then the target area is divided into Voronoi cells dynamically by the robots moving. The robots move following the concentration gradient of area by the BC algorithm. Simulation results proved the effectiveness of dynamic Voronoi-based algorithm. At last, a bio-inspired filtering via SGRN is proposed to improve estimation accuracy and robustness for the multi-robot localization system which lacks sufficient information of complete models or the process and with varying measurement noise. The proposed bio-inspired filtering is evolved using a multi-objective optimization algorithm subject to minimizing the absolute value of the error between the desired overall performance index and the actual one, and shorten the settling time. From the dynamics of the robot, the solution existence of the proposed filter is shown while with a low computational complexity to obtain a solution from the proposed filter. The simulation results demonstrate that there is a decrease in scrap and eventually an improvement of the multi-robot localization by using the bio-inspired filtering. Some conclusions and future discussion are given at the end of this paper.