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Deep Reinforcement Learning for Spatial Motion Planning in 3D Environments

Authors:
Oren Gal
Yerach Doytsher

Keywords: Deep Reinforcement Learning; Visibility; 3D; Spatial analysis; Motion Planning.

Abstract:
In this paper, we present a spatial motion planner in 3D environments based on Deep Reinforcement Learning (DRL) algorithms. We tackle 3D motion planning problem by using Deep Reinforcement Learning (DRL) approach, which learns agent’s and environment constraints. Spatial analysis focus on visibility analysis in 3D setting an optimal motion primitive considering agent’s dynamic model based on fast and exact visibility analysis for each motion primitives. Based on optimized reward function, consisting of generated 3D visibility analysis and obstacle avoidance trajectories, we introduce DRL formulation which learns the value function of the planner and generates an optimal spatial visibility trajectory. We demonstrate our planner in simulations for Unmanned Aerial Vehicles (UAV) in 3D urban environments. Our spatial analysis is based on a fast and exact spatial visibility analysis of the 3D visibility problem from a viewpoint in 3D urban environments. We present DRL architecture generating the most visible trajectory in a known 3D urban environment model, as time-optimal one with obstacle avoidance capability.

Pages: 6 to 11

Copyright: Copyright (c) IARIA, 2021

Publication date: July 18, 2021

Published in: conference

ISSN: 2308-393X

ISBN: 978-1-61208-871-6

Location: Nice, France

Dates: from July 18, 2021 to July 22, 2021