.png){kind=icon}
返回
Coverage Path Planning Using Actor-Critic Deep Reinforcement Learning
.png){kind=icon}
OA
AI
DOI：10.3390/s25051592.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
One of the main capabilities a mobile robot must demonstrate is the ability to explore its environment. The core challenge in exploration lies in planning the route to fully cover the environment. Despite recent advances, this problem remains unsolved. This study proposes an approach to address the coverage path planning problem, where the mobile robot is tasked with exploring and completely covering a terrain using a deep reinforcement learning framework. The environment is divided into cells, with obstacles designated as prohibited areas. The robot is trained using two state-of-the-art reinforcement learning algorithms based on actor-critic methods: Advantage Actor-Critic (A2C) and Proximal Policy Optimization (PPO). By defining a set of observations, states, and a reward function tailored to characteristics of the environment and the desired behavior of the robot, the training process is conducted, resulting in optimized policies for each algorithm. Then, these policies are evaluated to determine the most effective approach to accomplish the proposed task. Our findings demonstrate that actor-critic methods can produce policies capable of guiding a robot to efficiently explore and cover new environments.
Keyword:
coverage path planning
deep reinforcement learning
proximal policy optimization
advantage actor-critic
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
