Study of formation control and obstacle avoidance of swarm robots using evolutionary algorithms

Swarm robots cooperating in a group offer plentiful benefits and can accomplish several jobs that could be otherwise challenging either for human beings or for a single robot. Here we have considered two evolutionary based control algorithms, namely Bacterial Foraging (BFOA) and Particle swarm optimization (PSO), for flocking of a swarm to a predefine objective along an optimum path while avoiding obstacles. During the movement of the swarm, attraction, repulsion and formation coefficients of all agents are evaluated using the above stated algorithms based on some fitness function as described. The evaluated coefficients can plan the path with obstacle avoidance efficiently throughout the journey. It is shown that PSO is responsible for proficient and fast path selection whereas BFOA maintains formation throughout the trajectory. Simulation results illustrate that two competing algorithms produce robust solution in different aspects.