Autonomous Multiple Robots Exploration using Evolutionary Optimization Algorithm

Multiple robots exploration is an emerging research area in Robotics. Autonomous robotic exploration in natural or manmade disaster areas speeds up the search and rescue operations and it saves the life of victims and human rescuers. It helps to explore the places such as space, ocean, and forest etc., where humans are unable to physically visit. The important challenge existing in this autonomous multi-robots exploration problem is collaboration between the robots team to accomplish the entire mission. This research paper proposes an autonomous exploration strategy by means of a swarm intelligence algorithm. The entire area to be explored is partitioned into a number of smaller regions. Exploration of the entire smaller regions collectively brings the robots team to accomplish the overall mission. Hence the efficient allocation of robots to the smaller regions to be explored consumes optimal resources like battery energy and task completion time. The Particle Swarm Optimization algorithm is applied for allocating the smaller regions to the robots in an efficient manner. The simulation of the proposed environment partitioning and exploration strategy is implemented in the Robot Operating System Gazebo simulation tool. A flat and smooth terrain environment of known boundary conditions is considered in the simulation study. The environment contains a number of obstacles at random positions. The task allocation time, overall robots battery energy consumption is minimal by the proposed exploration strategy. The real time implementation of the proposed concept is the future scope of this research.