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

IF 7.5 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Baojiang Li , Shengjie Qiu , Haiyan Ye , Yuting Guo , Haiyan Wang , Jibo Bai
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引用次数: 0

Abstract

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.
7 自由度仿生手臂的运动规划:基于模仿人类动作的深度确定性策略梯度算法
智能仿生臂在让截肢者重返社会方面发挥了巨大作用。然而,目前的仿生臂大多为自由度较小的桡骨构型和肱骨构型,仅适用于近端截肢的患者。肩部截肢患者迫切需要一种能完全模拟人类上肢运动的 7 自由度仿生手臂。同时,仿生手臂往往需要为受试者实施特定的编程才能初步满足控制要求,这使得仿生手臂的运动很难与佩戴者的运动习惯相匹配,降低了人机交互的适应性和可靠性。针对这一问题,本文提出了基于人体上肢关节点引导的运动模仿和基于强化学习方法的运动规划算法,通过学习人体上肢的动态运动模仿,达到使肩部断开的仿生手臂完成仿人运动的目的。该算法从未标明的视频中分析和学习人体手臂运动特征的三维姿势,然后根据人体运动模式设计奖励函数,并使用基于深度确定性策略梯度(DDPG)的强化学习算法来训练仿生手臂的仿人运动。我们在仿真环境中通过多个任务评估了肩部分离仿生手臂的有效性,结果表明该方法在规划仿生手臂的仿人运动方面具有良好的性能,可广泛应用于仿生机器控制领域。
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来源期刊
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence 工程技术-工程:电子与电气
CiteScore
9.60
自引率
10.00%
发文量
505
审稿时长
68 days
期刊介绍: Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.
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