An effective self-collision detection algorithm for multi-degree-of-freedom manipulator

Liu, Zhenyu; Zhang, Lianhao; Qin, Xiaohong; Li, Gang

doi:10.1088/1361-6501/ac9920

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…The modeling of these colliders is simple, and the algorithm for distance calculation is relatively simple. For example, the distance between spheres can be converted to calculate the distance between the centers of spheres, and the distance between cylinders can be converted to calculate the distance between axes [ 34 ]. However, this modeling method of the colliders will affect the modeling accuracy at the irregular outer surface, thereby affecting the self-collision detection accuracy at these positions.…”

Section: Multi-manipulator System and Collider Modelingmentioning

confidence: 99%

A Self-Collision Detection Algorithm of a Dual-Manipulator System Based on GJK and Deep Learning

Zhi

Adili

et al. 2023

Sensors

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Self-collision detection is fundamental to the safe operation of multi-manipulator systems, especially when cooperating in highly dynamic working environments. Existing methods still face the problem that detection efficiency and accuracy cannot be achieved at the same time. In this paper, we introduce artificial intelligence technology into the control system. Based on the Gilbert-Johnson-Keerthi (GJK) algorithm, we generated a dataset and trained a deep neural network (DLNet) to improve the detection efficiency. By combining DLNet and the GJK algorithm, we propose a two-level self-collision detection algorithm (DLGJK algorithm) to solve real-time self-collision detection problems in a dual-manipulator system with fast-continuous and high-precision properties. First, the proposed algorithm uses DLNet to determine whether the current working state of the system has a risk of self-collision; since most of the working states in a system workspace do not have a self-collision risk, DLNet can effectively reduce the number of unnecessary detections and improve the detection efficiency. Then, for the working states with a risk of self-collision, we modeled precise colliders and applied the GJK algorithm for fine self-collision detection, which achieved detection accuracy. The experimental results showed that compared to that with the global use of the GJK algorithm for self-collision detection, the DLGJK algorithm can reduce the time expectation of a single detection in a system workspace by 97.7%. In the path planning of the manipulators, it could effectively reduce the number of unnecessary detections, improve the detection efficiency, and reduce system overhead. The proposed algorithm also has good scalability for a multi-manipulator system that can be split into dual-manipulator systems.

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Section: Multi-manipulator System and Collider Modelingmentioning

confidence: 99%

A Self-Collision Detection Algorithm of a Dual-Manipulator System Based on GJK and Deep Learning

Zhi

Adili

et al. 2023

Sensors

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“…The most modern collision detection methods are based on analytical approaches [19][20][21][22][23][24][25][26]. In [7], an ANN solution on the basis of the "Contextual" ANN to predicting the self-collision of multilink manipulators is proposed.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Method of Controlling Self-Collisions of Multilink Manipulators Based on the Solution of the Classification Problem

Kramar,

Kabanov

et al. 2023

Applied Sciences

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The problem of self-collisions of manipulators with several links installed on a robot can arise when they work together in one zone. To prevent self-collisions, it is necessary to develop methods for their detection and their subsequent inclusion in control algorithms. This paper proposes an approach for determining the occurrence of self-collisions of manipulators using the Artificial Neural Networks approach. In contrast to the regression problem, this article proposes a classification approach. The effectiveness of the proposed approach was tested on robots with multilink manipulators “Ichtiandr” and SAR-401 and their simulators. Self-collision detection using the proposed method is much faster than using the traditional approach of solving the inverse kinematics problem without loss of accuracy. The problem was solved by constructing various Artificial Neural Networks and then checking the accuracy of the solution. A comparative analysis of Artificial Neural Networks was carried out and as a result, the Artificial Neural Networks approach showing the best accuracy was selected. The problem was solved for a robot with two manipulators. The resulting solution can be extended to a larger number of manipulators installed on the robot.

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Interactive control algorithm for shoulder-amputated prosthesis and object based on reinforcement learning

Li,

Ye,

Guo

et al. 2024

Transactions of the Institute of Measurement and Control

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As a prosthesis made to compensate for the residual loss of the amputee’s limb, the shoulder disarticulation upper limb prosthesis replaces the missing arm function of the shoulder amputee to a certain extent. However, the current upper limb prosthesis mainly interacts with the outside world through the prosthetic hand for grasping and gripping, and the interaction between other parts and the environment is often neglected, which is not in line with the use habits of the human arm. To address this problem, this paper proposes a reinforcement learning–based method for controlling the forearm interaction of a shoulder-disconnected upper limb prosthesis, and analyzes and solves the forces during the interaction, reducing the impact of uncertainty on interaction actions and accelerating training while ensuring the stability of handheld items. We evaluated the performance of the control method during the interaction between the upper limb prosthesis and the external environment through simulation experiments. After the training, the bionic arm was able to push the object into the target range for different objects and pushing distance requirements, which showed the good control effect of the method. Also, the control method can be applied to improve the interaction between the robotic arm and the environment.

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An effective self-collision detection algorithm for multi-degree-of-freedom manipulator

Cited by 3 publications

References 19 publications

A Self-Collision Detection Algorithm of a Dual-Manipulator System Based on GJK and Deep Learning

A Self-Collision Detection Algorithm of a Dual-Manipulator System Based on GJK and Deep Learning

Neural Network Method of Controlling Self-Collisions of Multilink Manipulators Based on the Solution of the Classification Problem

Interactive control algorithm for shoulder-amputated prosthesis and object based on reinforcement learning

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