Multi robot area exploration using nature inspired algorithm

Exploration is a process where the objective is to cover an area that is used for subsequent navigation. It is an important criteria for problem-solving in many unknown search space and is an important aspect of automation and information gathering. Here we have proposed multi-robot area exploration method for unknown search areas. In the proposed work, exploration is mainly guided by combined effect of probabilistic and deterministic movement. It uses Clustering Based Distribution Factor (CBDF) for deterministic movement and nature inspired algorithms (NIA) like Particle swarm optimization (PSO), Bacteria foraging optimization (BFO), and Bat algorithm (BA) for random guidance for exploration. The environment partitioning avoids repeated area coverage, and robots may be allocated to any partition to explore the map in a random manner. Robots move in the direction provided by CBDF and explore the area using nature inspired algorithm. The proposed approaches have been implemented and evaluated in several simulated environments and with varying team sizes and detection ranges. Simulation results show that, on increasing the number of robots and detection range, performance also increases and that best results are achieved for PSO.