Reinforcement Learning-Based Coverage Path Planning with Implicit Cellular Decomposition

2021-10-18 · Javad Heydari, Olimpiya Saha, Viswanath Ganapathy

Coverage path planning in a generic known environment is shown to be NP-hard. When the environment is unknown, it becomes more challenging as the robot is required to rely on its online map information built during coverage for planning its path. A significant research effort focuses on designing heuristic or approximate algorithms that achieve reasonable performance. Such algorithms have sub-optimal performance in terms of covering the area or the cost of coverage, e.g., coverage time or energy consumption. In this paper, we provide a systematic analysis of the coverage problem and formulate it as an optimal stopping time problem, where the trade-off between coverage performance and its cost is explicitly accounted for. Next, we demonstrate that reinforcement learning (RL) techniques can be leveraged to solve the problem computationally. To this end, we provide some technical and practical considerations to facilitate the application of the RL algorithms and improve the efficiency of the solutions. Finally, through experiments in grid world environments and Gazebo simulator, we show that reinforcement learning-based algorithms efficiently cover realistic unknown indoor environments, and outperform the current state of the art.

📄 PDF Abstract BibTeX arXiv:2110.09018

Code (0)

등록된 구현이 없습니다.

Tasks

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Maximizing UAV Cellular Connectivity with Reinforcement Learning for BVLoS Path Planning

2025-09-11 · Mehran Behjati, Rosdiadee Nordin, Nor Fadzilah Abdullah arxiv

This paper presents a reinforcement learning (RL) based approach for path planning of cellular connected unmanned aerial vehicles (UAVs) operating beyond visual line of sight (BVLoS). The objective is to minimize travel …

Reinforcement Learning

End-to-End Framework for Robot Lawnmower Coverage Path Planning using Cellular Decomposition

2025-06-06 · Nikunj Shah, Utsav Dey, Kenji Nishimiya

Efficient Coverage Path Planning (CPP) is necessary for autonomous robotic lawnmowers to effectively navigate and maintain lawns with diverse and irregular shapes. This paper introduces a comprehensive end-to-end pipelin…

Navigate

Path Design for Cellular-Connected UAV with Reinforcement Learning

2019-05-09 · Yong Zeng, Xiaoli Xu

This paper studies the path design problem for cellular-connected unmanned aerial vehicle (UAV), which aims to minimize its mission completion time while maintaining good connectivity with the cellular network. We first …

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Learning Coverage Paths in Unknown Environments with Deep Reinforcement Learning

2023-06-29 · Arvi Jonnarth, Jie Zhao, Michael Felsberg

Coverage path planning (CPP) is the problem of finding a path that covers the entire free space of a confined area, with applications ranging from robotic lawn mowing to search-and-rescue. When the environment is unknown…

Deep Reinforcement Learning reinforcement-learning

Simulating Coverage Path Planning with Roomba

2022-10-10 · Robert Chuchro

Coverage Path Planning involves visiting every unoccupied state in an environment with obstacles. In this paper, we explore this problem in environments which are initially unknown to the agent, for purposes of simulatin…

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning (RL)