A Path Planning Algorithm for Collective Monitoring Using Autonomous Drones

This paper presents a novel mission-oriented path planning algorithm for a team of Unmanned Aerial Vehicles (UAVs). In the proposed algorithm, each UAV takes autonomous decisions to find its flight path towards a designated mission area while avoiding collisions to stationary and mobile obstacles. The main distinction with similar algorithms is that the target destination for each UAV is not apriori fixed and the UAVs locate themselves such that they collectively cover a potentially time-varying mission area. One potential application for this algorithm is deploying a team of autonomous drones to collectively cover an evolving forest wildfire and provide virtual reality for fire fighters. We formulated the algorithm based on Reinforcement Learning (RL) with a new method to accommodate continuous state space for adjacent locations. To consider more realistic scenario, we assess the impact of localization errors on the performance of the proposed algorithm. Simulation results show that success probability for this algorithm is about 80% when the observation error variance is as high as 100 (SNR:-6dB).