A Hybrid Task-Constrained Motion Planning for Collaborative Robots in Intelligent Remanufacturing
<|reference_start|>A Hybrid Task-Constrained Motion Planning for Collaborative Robots in Intelligent Remanufacturing: Industrial manipulators have extensively collaborated with human operators to execute tasks, e.g., disassembly of end-of-use products, in intelligent remanufacturing. A safety task execution requires real-time path planning for the manipulator's end-effector to autonomously avoid human operators. This is even more challenging when the end-effector needs to follow a planned path while avoiding the collision between the manipulator body and human operators, which is usually computationally expensive and limits real-time application. This paper proposes an efficient hybrid motion planning algorithm that consists of an A$^*$ algorithm and an online manipulator reconfiguration mechanism (OMRM) to tackle such challenges in task and configuration spaces respectively. The A$^*$ algorithm is first leveraged to plan the shortest collision-free path of the end-effector in task space. When the manipulator body is risky to the human operator, our OMRM then selects an alternative joint configuration with minimum reconfiguration effort from a database to assist the manipulator to follow the planned path and avoid the human operator simultaneously. The database of manipulator reconfiguration establishes the relationship between the task and configuration space offline using forward kinematics, and is able to provide multiple reconfiguration candidates for a desired end-effector's position. The proposed new hybrid algorithm plans safe manipulator motion during the whole task execution. Extensive numerical and experimental studies, as well as comparison studies between the proposed one and the state-of-the-art ones, have been conducted to validate the proposed motion planning algorithm.<|reference_end|>
@article{liu2024a,
    title={A Hybrid Task-Constrained Motion Planning for Collaborative Robots in
  Intelligent Remanufacturing},
    author={Wansong Liu, Chang Liu, Xiao Liang, Minghui Zheng},
    journal={arXiv preprint arXiv:2406.08283},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.08283},
    primaryClass={cs.RO cs.SY eess.SY}
}