Multi quadrotors coverage optimization using reinforcement learning with negotiation - Bonaventura Wijaya, Glenn; Agustinus Tamba, Tua (IAES International Journal of Artificial Intelligence (IJ-AI), 2024)
This paper proposes an optimization scheme to maximize the area coverage of multiple quadrotor unmanned aerial vehicles that are deployed to monitor an operational area/space. Each quadrotor initially performs a single agent reinforcement learning to determine target points with optimal coverage area. Whenever each quadrotor encounters the others within a predetermined negotiation region that is defined by an inter-agent distance threshold, it will activate a multiagent reinforcement learning with action negotiation algorithm and coordinate its movement policies to maximize the total coverage area and avoids inter-agent coverage overlaps. Results of simulation evaluations are shown to illustrate the performance of the proposed learning-based coverage optimization method.