de Carvalho, Kevin Braathen (Universidade Federal de Viçosa), Oliveira, Iure Rosa (Federal University of Viçosa), Villa, Daniel Khede Dourado (Federal University of Espírito Santo), Gomes Caldeira, Alexandre (Universidade Federal de Viçosa), Sarcinelli-Filho, Mário (Federal University of Espirito Santo), Brandao, Alexandre Santos (Federal University of Vicosa)

An Offline Path Planning Method for UAVs Using Priority Shifting

Scheduled for presentation during the Regular Session "Path Planning I" (WeB4), Wednesday, June 22, 2022, 16:50−17:10, Divona-2

2022 International Conference on Unmanned Aircraft Systems (ICUAS), June 21-24, 2022, Dubrovnik, Croatia

This information is tentative and subject to change. Compiled on April 23, 2024

Abstract

Path planning is a crucial part of autonomous navigation when regarding autonomous aerial vehicles, often demanding different priorities such as the length, safety or energy consumption. Dynamic programming and geometric methods have been applied to solve this problem, but in recent years, more work has been developed using artificial intelligence approaches, such as reinforcement learning. In this paper we propose an offline path planning method for static environments using Q-learning. An optimal policy is found weighting three important factors: path length, safety and energy consumption. Due to a well balanced exploring/exploiting ratio, the proposed method can lead the agent to the desired destination starting from anywhere in the map. Simulations are done in different scenarios to address the performance of the proposed method and it showcased that the algorithm is able to find feasible paths in each scenario while regarding different set of priorities.