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A novel framework for trajectory planning in robotic arm developed by integrating dynamical movement primitives with particle swarm optimization
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A novel framework for trajectory planning in robotic arm developed by integrating dynamical movement primitives with particle swarm optimization
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Journal Article
A novel framework for trajectory planning in robotic arm developed by integrating dynamical movement primitives with particle swarm optimization
2025
Overview
In human-robot collaboration, imitation learning and autonomous adaptation to new scenarios are pivotal concerns for robotic arms. To address these challenges, a novel framework (
DMP-PSO
) for trajectories planning in robotic arm is presented by integrating dynamical movement primitives (
DMP
) with particle swarm optimization (
PSO
) in this paper. Firstly,
DMP
is employed to learn and generalize motion trajectories. Secondly, the initial state and search region of
PSO
are enhanced based on the generalized trajectories to rapidly generate obstacle avoidance trajectories within the search region. Finally, the proposed
DMP-PSO
framework autonomously generates diverse trajectories for robotic arms encompassing obstacle avoidance paths through its ingenious design. The effectiveness of this framework is validated through various means. The numerical simulation results show that the trajectory planning based on
DMP-PSO
has good adaptability and strong consistency, and significantly improves the efficiency. Furthermore, virtual simulations along with physical experiments corroborate the exceptional robustness and practicality exhibited by the proposed framework.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
Subject
