Research on obstacle avoidance path planning of robotic manipulator based on deep reinforcement learning

Dawei Ge

doi:10.1117/12.3049584

8 November 2024 Research on obstacle avoidance path planning of robotic manipulator based on deep reinforcement learning

Dawei Ge

Author Affiliations +

Proceedings Volume 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024); 134161Q (2024) https://doi.org/10.1117/12.3049584
Event: 2024 4th International Conference on Advanced Algorithms and Neural Networks, 2024, Qingdao, China

Abstract

In recent years, there have been significant advancements in path planning and obstacle avoidance for robotic manipulators. This paper introduces a novel approach to path planning for robotic manipulators by employing the Deep Deterministic Policy Gradient (DDPG) algorithm. The manipulator model is developed using SolidWorks software and integrated into the Simulink environment, incorporating two spherical obstacles. The experimental results demonstrate the efficacy of the DDPG algorithm in navigating complex environments and avoiding obstacles. The proposed method is validated through extensive simulations, demonstrating enhanced path efficiency and collision avoidance compared to traditional approaches.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Dawei Ge "Research on obstacle avoidance path planning of robotic manipulator based on deep reinforcement learning", Proc. SPIE 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), 134161Q (8 November 2024); https://doi.org/10.1117/12.3049584

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