Integrated Ray-Tracing and Coverage Planning Control using Reinforcement Learning

Abstract

In this work we propose a coverage planning control approach which allows a mobile agent, equipped with a controllable sensor (i.e., a camera) with limited sensing domain (i.e., finite sensing range and angle of view), to cover the surface area of an object of interest. The proposed approach integrates ray-tracing into the coverage planning process, thus allowing the agent to identify which parts of the scene are visible at any point in time. The problem of integrated ray-tracing and coverage planning control is first formulated as a constrained optimal control problem (OCP), which aims at determining the agent's optimal control inputs over a finite planning horizon, that minimize the coverage time. Efficiently solving the resulting OCP is however very challenging due to non-convex and non-linear visibility constraints. To overcome this limitation, the problem is converted into a Markov decision process (MDP) which is then solved using reinforcement learning. In particular, we show that a controller which follows an optimal control law can be learned using off-policy temporal-difference control (i.e., Q-learning). Extensive numerical experiments demonstrate the effectiveness of the proposed approach for various configurations of the agent and the object of interest.

arXiv comment2022 IEEE 61st Conference on Decision and Control (CDC), 06-09 December 2022, Cancun, Mexico

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Cited by in corpus (4)

• Unscented Optimal Control for 3D Coverage Planning with an Autonomous UAV Agent
• Cooperative Receding Horizon 3D Coverage Control with a Team of Networked Aerial Agents
• Synergising Human-like Responses and Machine Intelligence for Planning in Disaster Response
• Jointly-optimized Trajectory Generation and Camera Control for 3D Coverage Planning