Abstract
In this paper, an improved obstacle-avoidance-scheme-based kinematic control problem in acceleration level for a redundant robot manipulator is investigated. Specifically, the manipulator and obstacle are abstracted as mathematical geometries, based on the vector relationship between geometric elements, and the Cartesian coordinate of the nearest point to an obstacle on a manipulator can be found. The distance between the manipulator and an obstacle is described as the point-to-point distance, and the collision avoidance strategy is formulated as an inequality. To avoid the joint drift phenomenon of the manipulator, bi-criteria performance indices integrating joint-acceleration-norm minimization and repetitive motion planning is adopted by assigning a weighing factor. From the perspective of optimization, therefore, an acceleration level quadratic programming (QP) problem is eventually formulated. Considering the physical structure of robot manipulators, inherent joint angle, speed, and acceleration limits are also incorporated. To solve the resultant QP minimization problem, a recurrent neural network based neural dynamic solver is proposed. Then, simulation experiments performing on a four-link planar manipulator validate the feasibility and effectiveness of the proposed scheme.
Highlights
With the advances of society, ranging from industry to military, home furnishing, service, medical treatment, etc., robot technology has already become gradually mature
Series of related products have been reported, e.g., in Li et al (2016), from the perspective of game theory, and a distributed recurrent neural network (RNN)-based dynamic controller was proposed for the coordination control of multi-robot system
In this study based on the quadratic programming (QP) optimization, we investigated the bicriteria acceleration-level obstacle avoidance of the redundant manipulator
Summary
With the advances of society, ranging from industry to military, home furnishing, service, medical treatment, etc., robot technology has already become gradually mature. For QP-based methods, the obstacle avoidance strategy is usually formulated as an attachment constraint of the resultant QP minimization problem. To avoid the joint drift problem and improve the stability and reliability of robots in periodic tasks such as palletizing, welding, etc., the bi-criteria performance indices integrating joint-acceleration-norm minimization (MAN) and repetitive motion planning (RMP) is considered by assigning a weighing factor. The main contributions of this paper are summarized as follows: 1) The acceleration-level kinematic control problem of redundant manipulator with the obstacle collision avoidance is investigated. 3) An RNN-based dynamic controller combining the motion planner, obstacle avoidance and joint angles, joint speed, as well as joint acceleration constraints is proposed. The robot achieves the desired trajectory tracking task with a desired tracking error, and it successfully avoids collision with static and dynamic obstacles
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