DeepAI AI Chat
Log In Sign Up

Reinforcement Learning-Based Coverage Path Planning with Implicit Cellular Decomposition

by   Javad Heydari, et al.
University of Nebraska at Omaha

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.


Simulating Coverage Path Planning with Roomba

Coverage Path Planning involves visiting every unoccupied state in an en...

Cellular Decomposition for Non-repetitive Coverage Task with Minimum Discontinuities

A mechanism to derive non-repetitive coverage path solutions with a prov...

Path Planning of Cleaning Robot with Reinforcement Learning

Recently, as the demand for cleaning robots has steadily increased, ther...

Fast-Spanning Ant Colony Optimisation (FaSACO) for Mobile Robot Coverage Path Planning

Coverage Path Planning (CPP) aims at finding an optimal path that covers...

Decentralized Coverage Path Planning with Reinforcement Learning and Dual Guidance

Planning coverage path for multiple robots in a decentralized way enhanc...

CT-CPP: 3D Coverage Path Planning for Unknown Terrain Reconstruction using Coverage Trees

This letter addresses the 3D coverage path planning (CPP) problem for te...

Online search of unknown terrains using a dynamical system-based path planning approach

Surveillance and exploration of large environments is a tedious task. In...