首页|Recent Findings in Robotics Described by a Researcher from Henan University of S cience and Technology (Active Collision Avoidance for Robotic Arm Based on Artif icial Potential Field and Deep Reinforcement Learning)

Recent Findings in Robotics Described by a Researcher from Henan University of S cience and Technology (Active Collision Avoidance for Robotic Arm Based on Artif icial Potential Field and Deep Reinforcement Learning)

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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on robotics is the subjec t of a new report. According to news reporting out of Luoyang, People's Republic of China, by NewsRx editors, research stated, "To address the local minimum iss ue commonly encountered in active collision avoidance using artificial potential field (APF), this paper presents a novel algorithm that integrates APF with dee p reinforcement learning (DRL) for robotic arms." The news journalists obtained a quote from the research from Henan University of Science and Technology: "Firstly, to improve the training efficiency of DRL for the collision avoidance problem, Hindsight Experience Replay (HER) was enhanced by adjusting the positions of obstacles, resulting in Hindsight Experience Repl ay for Collision Avoidance (HER-CA). Subsequently, A robotic arm collision avoid ance action network model was trained based on the Twin Delayed Deep Determinist ic Policy Gradient (TD3) and HER-CA methods. Further, a full-body collision avoi dance potential field model of the robotic arm was established based on the arti ficial potential field. Lastly, the trained action network model was used to gui de APF in real-time collision avoidance planning. Comparative experiments betwee n HER and HER-CA were conducted."

Henan University of Science and Technolo gyLuoyangPeople's Republic of ChinaAsiaEmerging TechnologiesMachine Le arningReinforcement LearningRoboticsRobots

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Jun.20)