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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Bibliographic Details
Published in:IEEE transactions on industrial informatics 2020-07, Vol.16 (7), p.4670-4680
Main Authors: Khan, Ameer Hamza, Li, Shuai, Luo, Xin
Format: Article
Language:English
Subjects:
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
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Summary: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.
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2019.2941916