Evolutionary Computation Based Real-time Robot Arm Path-planning Using Beetle Antennae Search Evolutionary Computation Based Real-time Robot Arm Path-planning Using Beetle Antennae Search

This paper presents a model-free real-time kinematic tracking controller for a redundant manipulator. Redundant manipulators are common in industrial applications because of the flexibility and dexterity they get from redundant joints. However, at the same time, the modeling of these systems becomes quite challenging, even for simple tasks like trajectory tracking. Some classical approaches are being used to tackle the issue, including a numerical approximation of the Jacobian and pseudo-inverse of the Jacobian matrix. These approaches have their limitations as they require exact parameters for the modeling of the manipulator; they are not immune to position error accumulation with time and put the manipulator way off the target position. Swarm-based meta-heuristic algorithms have given a new direction to the solution of the redundancy resolution problem. However, they are computationally intensive, formulated in discrete-time, and better suited for offline computation rather than real-time. We proposed a novel continuous-time Zeroing Neural Network with Beetle Antennae Search (ZNNBAS). The ZNNBAS algorithm can solve the quadratic optimization problem for redundancy resolution in real-time. To test its performance, we applied it on 7-DOF redundant manipulator with two trajectories to follow: character M" and hypotrochoid. The manipulator was able to trace the reference trajectories with minimal tracking errors. This paper presents a model-free real-time kinematic tracking controller for a redundant manipulator. Redundant manipulators are common in industrial applications because of the flexibility and dexterity they get from redundant joints. However, at the same time, the modeling of these systems becomes quite challenging, even for simple tasks like trajectory tracking. Some classical approaches are being used to tackle the issue, including a numerical approximation of the Jacobian and pseudo-inverse of the Jacobian matrix. These approaches have their limitations as they require exact parameters for the modeling of the manipulator; they are not immune to position error accumulation with time and put the manipulator way off the target position. Swarm-based meta-heuristic algorithms have given a new direction to the solution of the redundancy resolution problem. However, they are computationally intensive, formulated in discrete-time, and better suited for offline computation rather than real-time. We proposed a novel continuous-time Zeroing Neural Network with Beetle Antennae Search (ZNNBAS). The ZNNBAS algorithm can solve the quadratic optimization problem for redundancy resolution in real-time. To test its performance, we applied it on 7-DOF redundant manipulator with two trajectories to follow: characterM" and hypotrochoid. The manipulator was able to trace the reference trajectories with minimal tracking errors.