Obstacle Avoidance and Tracking Control of Redundant Robotic Manipulator: An RNN-Based Metaheuristic Approach
In this article, we present a metaheuristic-based control framework, called beetle antennae olfactory recurrent neural network, for simultaneous tracking control and obstacle avoidance of a redundant manipulator. The ability to avoid obstacles while tracking a predefined reference path is critical f...
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| Published in | IEEE transactions on industrial informatics Vol. 16; no. 7; pp. 4670 - 4680 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Piscataway
IEEE
01.07.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
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| Abstract | In this article, we present a metaheuristic-based control framework, called beetle antennae olfactory recurrent neural network, for simultaneous tracking control and obstacle avoidance of a redundant manipulator. The ability to avoid obstacles while tracking a predefined reference path is critical for any industrial manipulator. The formulated control framework unifies the tracking control and obstacle avoidance into a single constrained optimization problem by introducing a penalty term into the objective function, which actively rewards the optimizer for avoiding the obstacles. One of the significant features of the proposed framework is the way that the penalty term is formulated following a straightforward principle: maximize the minimum distance between a manipulator and an obstacle. The distance calculations are based on Gilbert-Johnson-Keerthi algorithm, which calculates the distance between a manipulator and an obstacle by directly using their three-dimensional geometries, which also implies that our algorithm works for a manipulator and an arbitrarily shaped obstacle. Theoretical treatment proves the stability and convergence, and simulations results using an LBR IIWA seven-DOF manipulator are presented to analyze the performance of the proposed framework. |
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| AbstractList | In this article, we present a metaheuristic-based control framework, called beetle antennae olfactory recurrent neural network, for simultaneous tracking control and obstacle avoidance of a redundant manipulator. The ability to avoid obstacles while tracking a predefined reference path is critical for any industrial manipulator. The formulated control framework unifies the tracking control and obstacle avoidance into a single constrained optimization problem by introducing a penalty term into the objective function, which actively rewards the optimizer for avoiding the obstacles. One of the significant features of the proposed framework is the way that the penalty term is formulated following a straightforward principle: maximize the minimum distance between a manipulator and an obstacle. The distance calculations are based on Gilbert-Johnson-Keerthi algorithm, which calculates the distance between a manipulator and an obstacle by directly using their three-dimensional geometries, which also implies that our algorithm works for a manipulator and an arbitrarily shaped obstacle. Theoretical treatment proves the stability and convergence, and simulations results using an LBR IIWA seven-DOF manipulator are presented to analyze the performance of the proposed framework. |
| Author | Khan, Ameer Hamza Li, Shuai Luo, Xin |
| Author_xml | – sequence: 1 givenname: Ameer Hamza orcidid: 0000-0002-5367-5277 surname: Khan fullname: Khan, Ameer Hamza email: ameer.h.khan@connect.polyu.hk organization: Department of Computing, Hong Kong Polytechnic University, Hong Kong – sequence: 2 givenname: Shuai orcidid: 0000-0001-8316-5289 surname: Li fullname: Li, Shuai email: shuaili@ieee.org organization: Swansea University, Swansea, U.K – sequence: 3 givenname: Xin orcidid: 0000-0002-1348-5305 surname: Luo fullname: Luo, Xin email: luoxin21@cigit.ac.cn organization: Department of Electrical and Electronic Engineering, Chongqing Engineering Research Center of Big Data Application for Smart Cities and the Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China |
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| SubjectTerms | Algorithms Antennae Collision avoidance Computer simulation Heuristic methods Kinematics Manipulators Mathematical analysis Metaheuristic optimization Obstacle avoidance Optimization recurrent neural network (RNN) Recurrent neural networks Robot arms Robot control Task analysis Tracking control Trajectory |
| Title | Obstacle Avoidance and Tracking Control of Redundant Robotic Manipulator: An RNN-Based Metaheuristic Approach |
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