A Metaheuristic Optimization Approach to Solve Inverse Kinematics of Mobile Dual-Arm Robots

Abstract

This work presents an approach to solving the inverse kinematics of mobile dual-arm robots based on metaheuristic optimization algorithms. First, a kinematic analysis of a mobile dual-arm robot is presented. Second, an objective function is formulated based on the forward kinematics equations. The kinematic analysis does not require using any Jacobian matrix nor its estimation; for this reason, the proposed approach does not suffer from singularities, which is a common problem with conventional inverse kinematics algorithms. Moreover, the proposed method solves cooperative manipulation tasks, especially in the case of coordinated manipulation. Simulation and real-world experiments were performed to verify the proposal’s effectiveness under coordinated inverse kinematics and trajectory tracking tasks. The experimental setup considered a mobile dual-arm system based on the KUKA® Youbot® robot. The solution of the inverse kinematics showed precise and accurate results. Although the proposed approach focuses on coordinated manipulation, it can be implemented to solve non-coordinated tasks.

A Comparative Study of Swarm Intelligence Metaheuristics in UKF-Based Neural Training Applied to the Identification and Control of Robotic Manipulator

“…[30] ABC Limit: 100, F l : 0.1, F u : 0.9, p: 0.5 [31] ACO α: 1.0, β: 3.0, Evaporation Rate: 0.5 [32] ALO Probabilistic Switch: 0.1, Random Walk Length: 1.5, Levy Flight a: 1.0, b: 1.0 [33] BA A: 0.25, r: 0.5, α: 1.0, γ: 0.1, f min : 0.0, f max : 2.0 [34] CS Discover Rate p a : 0.25, Levy Flight a: 0.1, b: 0.9 [35] GWO a 0 : 2.0 [36] MFO a: 1.0, b: 1.0 [37] PSO ϕ 1 : 2.0, ϕ 1 : 2.0, w: 0.7, v max : 0.1 [37] APSO ϕ 1 : 1.5, ϕ 2 : 1.5, w: 0.7, v max : 0.1, p a : 0.1, p r : 0.1 [38] WOA a 1 : 2.0, a 2 : −1.0 SI algorithms presented in Table 2 were considered for comparison purposes because they have been employed previously in the state-of-the-art for neural network training, as reported in [39][40][41][42][43][44][45][46][47][48].…”

Design and Experiment of Greenhouse Self-Balancing Mobile Robot Based on PR Joint Sensor

“…After obtaining the adjustment amount, if each wheel-leg is adjusted at a constant speed, when the attitude angle is adjusted to a small range, the extension and retraction joints will adjust the tiny angle at a relatively high speed, which is prone to over-adjustment, causing the inclination angle to repeatedly adjust around the desired angle. When the robot moves on complex terrain, the body will continue to shake [25,26].…”