Self-Organizing Map approach to determining compromised solutions for multi-objective UAV path planning

The demand for Unmanned Aerial Vehicle (UAV) extends to various civil and military missions. While the use of remotely controlled UAV reduces the rate of human casualties in hazardous environments, it is reported that most of UAV accidents are caused by human factor errors. Automated path planning is required and because of the multi-objective nature of UAV's missions, several heuristic approaches to path planning have been proposed in order to automate UAV's navigation. While solving multi-objective problems requires the search for a set of pareto-optimal points, it requires the involvement of the user to select the desired result from the solution space. In this paper, we propose a variant of Self-Organizing Map approach to finding a compromised solution for a multi-objective path planning problem that does not require user involvement. Preliminary tests conducted in virtual environments have shown the immunity of our algorithm to local minima, and its efficiency to respond to multiple objectives.