Obstacle avoidance for a swarm of unmanned aerial vehicles operating on particle swarm optimization: a swarm intelligence approach for search and rescue missions

An approach, based on a multi-plane system, is conceptualized in this work to solve the problem of collision avoidance for a swarm of unmanned aerial vehicles, being used for search and rescue to minimize affecting the searching algorithm. Relevant chronological advancements in the last two decades of the parent algorithm, particle swarm optimization, are summarized. As each optimization algorithm for search and rescue has its own niche area of application, various well-established algorithms such as particle swarm optimization and novel algorithms like layered search and rescue, spiral search and fish-inspired task allocation are compared with each other qualitatively. Simulations with 100 different cases were used to compare the original particle swarm optimization with the additional novel collision avoidance algorithm. The statistical z test was run based on which it was found that the proposed algorithm significantly reduces the number of collisions and does not put a toll on the iterations to convergence. Standardized residuals of all cases indicate minimal error difference in the optimum average fitness value calculated by the particle swarm optimization, with and without the conceptualized anti-collision algorithm.