High-Precision Dynamics Modeling of Robots Integrating Swarm Intelligence Algorithms

To address the challenge of identifying robotic dy namics parameters, a high-precision dynamics modeling method integrating swarm intelligence algorithm is proposed. First, a joint dynamics model of the robot is developed. Then, an improved algorithm based on the modified humpback whale optimization algorithm (MWOA) is employed, combining Bernoulli chaotic mapping with an adaptive tuning evolutionary algorithm to optimize the excitation trajectory. This approach enables the synchronized identification of both the robot's joints and base. Experimental results demonstrate that the proposed method significantly improves the accuracy of robotic dynamics parameter identification, thus achieving precision force control of the robot.