A Comprehensive Review of Key Technologies for Robot Motion Planning in Contact Tasks in Industrial Automation Scenarios
Shibo Jin, Kaichen Ke, Boyang Gao, Li Fu, Xingrong Huang
Beihang University École Centrale de Lyon Centre National de la Recherche Scientifique Laboratoire de Tribologie et Dynamique des Systèmes
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摘要与影响
With the swift advancement of industrial automation, robots have emerged as an essential component in emerging industries and high-end equipment, thereby propelling industrial production towards greater intelligence and efficiency. This paper reviews the pivotal technologies for motion planning of robots engaged in contact tasks within industrial automation contexts, encompassing environmental recognition, trajectory generation strategies, and sim-to-real transfer. Environmental recognition technology empowers robots to accurately discern objects and obstacles in their operational environment. Trajectory generation strategies formulate optimal motion paths based on environmental data and task specifications. Sim-to-real transfer is committed to effectively translating strategies from simulated environments to actual production, thereby diminishing the discrepancies between simulation and reality. The article also delves into the application of artificial intelligence in robot motion planning and how embodied intelligence models catalyze the evolution of robotics technology towards enhanced intelligence and automation. The paper concludes with a synthesis of the methodologies addressing this challenge and a perspective on the myriad challenges that warrant attention.
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工程Robot Manipulation and Learning
Robotic Path Planning Algorithms · Reinforcement Learning in Robotics
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