Particle Swarm Algorithm Based on Snake Heron Behavior for Solving Inverse Solution of Redundant Robotic Arms

This study discusses the inverse kinematics problem of redundant robotic arms that do not follow the Pieper criteria (Three consecutive rotary joints have axes that are parallel or intersect at a single point), aiming to minimize the pose error of the end-effector. We propose an improved particle swarm optimization algorithm (Sine-CPSO) inspired by the snake heron's hunting strategy, which transforms the inverse solution problem into a minimization problem. The convergence accuracy of the algorithm is improved by improving the population initialization, adaptive inertia weights, particle evolution mechanism and out-of-bounds handling. For the upgraded redundant robotic arm of Xintuo Company, the MD-H (Modified Directed-Heuristic) method is used to construct the mathematical model, and the algorithm is applied with the traditional Improved Particle Swarm Algorithm (IPSO) to carry out the simulation test of the same position and different positions. The simulation results show that the convergence accuracy of the Sine-CPSO algorithm reaches 10<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−15</sup> order of magnitude, and the success rate of inverse solution solving reaches 97%, which verifies the superiority of the algorithm in terms of effectiveness and stability.