首页|Study Results from Shanghai Jiao Tong University Provide New Insights into Robot ics (An End-to-end Path Planner Combining Potential Field Method With Deep Reinf orcement Learning)
Study Results from Shanghai Jiao Tong University Provide New Insights into Robot ics (An End-to-end Path Planner Combining Potential Field Method With Deep Reinf orcement Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Robotics have been published. According to news reporting originating in Shanghai, People’s Republic of China, by NewsRx journalists, research stated, “This article presents an end-to-end path planning and motion control method based on deep reinforcement learning (DRL), aimed at enhancing the autonomous navigation capabilities of wheeled robots in mapless environments. By physically modeling the robot and opt imizing LiDAR inputs, our method precisely processes the relative distances between obstacles and the robot, thereby improving obstacle avoidance precision and adaptability.”
ShanghaiPeople’s Republic of ChinaAsiaEmerging TechnologiesMachine LearningReinforcement LearningRobotRobo ticsShanghai Jiao Tong University
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