A Dynamic Area Approximation-Based Stochastic Multi-UAV Target Search with Noisy Measurements

This paper presents a novel approach to effectively search for a target using a multi-robot system. The proposed approach augments the conventional swarm-based stochastic search algorithms by dynamically refining the search space to locate the source. Compared to other search algorithms, including PSO, Cuckoo Search Algorithm, Bat Algorithm, Glowworm Swarm Optimization and Random Walk, our algorithm reduces the time taken, and the amount of exploration done is much more succinct. Similarly, the algorithm makes no concessions in terms of success rate. In extreme scenarios, when the number of particles is fewer than five, the search space is enormous, or the search space is unbounded or noise in the sensor readings, our method stands out and performs far better than other stochastic search methods.