Autonomous Cost-Effective Robotic Exploration and Mapping for Disaster Reconnaissance

Natural calamities like earthquake and tsunami wreak havoc on life of so many people and the infrastructure, thus saving people who are trapped in the affected region has been a very difficult task for rescuers. This work aims to develop a system which will ease this task for the rescuers by using behavior based multiple robots that will explore, map, localize, path plan and navigate in an unknown environment. Various incarnations of pre-existing meta-heuristic algorithms are used to develop a cost-effective solution in this work. A random walk exploration is used to explore the entire environment effectively which reduces the computational cost and time. To reduce the complexities of mapping, binary occupancy grid is used for the map layout, which is not as efficient as compared to its counterparts but better, where time is the crucial factor. To identify the efficient path to navigate from source to destination positions in the environment, Particle Swarm Optimization algorithm is used in this paper. The last and the most crucial step of the system is navigation towards the destination. By integrating INS and GPS algorithms, the robot can get an almost perfect algorithm for navigation which is not prone to errors. Autonomous robot exploration, mapping and navigation are achieved by integrating the proposed algorithms to work simultaneously. The effectiveness of the proposed algorithms is analyzed through simulations in Webots virtual simulator environment, and the results are presented. Adapting the proposed exploration technique for swarm of robots is a prospective future research direction of this paper.