Motion planning for 7-degree-of-freedom bionic arm: Deep deterministic policy gradient algorithm based on imitation of human action

Smart bionic arms have played a great role in returning amputees to society. However, most of the current bionic arms are radial configuration type with few degrees of freedom and humeral form configuration type, which are only applicable to patients with proximal amputation. Patients with shoulder...

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Vydané v:Engineering applications of artificial intelligence Ročník 140; s. 109673
Hlavní autori: Li, Baojiang, Qiu, Shengjie, Ye, Haiyan, Guo, Yuting, Wang, Haiyan, Bai, Jibo
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 15.01.2025
Predmet:
Bionic arm
Humanoid motion
Imitation learning
Motion planning
Reinforcement learning
Motion planning
Humanoid motion
Imitation learning
Bionic arm
Reinforcement learning
ISSN:0952-1976
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Shrnutí:Smart bionic arms have played a great role in returning amputees to society. However, most of the current bionic arms are radial configuration type with few degrees of freedom and humeral form configuration type, which are only applicable to patients with proximal amputation. Patients with shoulder amputation urgently need a 7-degree-of-freedom bionic arm that can fully mimic human upper limb movements. Meanwhile, bionic arms often require specific programming to be implemented for the subject to initially meet the control requirements, which makes it difficult to match the motion of the bionic arm with the wearer's movement habits and reduces the adaptability and reliability of human-computer interaction. To address this problem, this paper proposes a motion imitation based on human upper limb joint point guidance and a motion planning algorithm based on reinforcement learning method to achieve the purpose of making the shoulder disconnected bionic arm accomplish humanoid motion by learning the dynamic motion imitation of the human upper limb. The algorithm analyzes and learns 3D poses of human arm movement features from unlabeled videos, then designs a reward function based on human motion patterns, and uses a reinforcement learning algorithm based on deep deterministic policy gradient (DDPG) to train the humanoid motion of the bionic arm. We evaluated the effectiveness of shoulder detached bionic arms through several tasks in a simulation environment, and the results showed that this method has good performance in planning the humanoid motion of bionic arms and can be widely applied in bionic machine control.
ISSN:0952-1976
DOI:10.1016/j.engappai.2024.109673