Robotics & Machine Learning Daily News2024,Issue(Nov.20) :36-37.

Reports Outline Robotics Study Findings from Chongqing Technology and Business U niversity (An Indoor Blind Area-oriented Autonomous Robotic Path Planning Approa ch Using Deep Reinforcement Learning)

重庆工商大学机器人学研究成果概要(一种基于深度强化学习的面向室内盲区自主机器人路径规划方法)

Robotics & Machine Learning Daily News2024,Issue(Nov.20) :36-37.

Reports Outline Robotics Study Findings from Chongqing Technology and Business U niversity (An Indoor Blind Area-oriented Autonomous Robotic Path Planning Approa ch Using Deep Reinforcement Learning)

重庆工商大学机器人学研究成果概要(一种基于深度强化学习的面向室内盲区自主机器人路径规划方法)

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摘要

由一名新闻记者-机器人与机器学习日报的工作人员新闻编辑每日新闻-一项关于机器人的新研究现在已经开始。根据新闻报道来自中华人民共和国重庆,由NewsRx记者报道,研究称,“深度加固”学习(DRL)为机器人自主路径规划提供了新的解决方案环境。以往的研究主要集中在机器人路径优化上,而忽略了机器人路径的盲区室内勘探,自然导致覆盖率低,勘探效率低。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - A new study on Robotics is now availab le. According to news reporting originatingfrom Chongqing, People’s Republic of China, by NewsRx correspondents, research stated, “Deep reinforcementlearning (DRL) provides a new solution for autonomous robotic path planning in a known in doorenvironment. Previous studies mainly focused on robot path optimization but ignored blind areas in theindoor exploration, naturally result in low coverage rate and low exploration efficiency.”

Key words

Chongqing/People’s Republic of China/A sia/Autonomous Robot/Emerging Technologies/Machine Learning/Reinforcement Le arning/Robot/Robotics/Robots/Chongqing Technology and Business University

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
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