Climbing Motion Planning of Inchworm Robot Based on Improved Particle Swarm Optimisation Algorithm

Climbing robots have broad potential for various practical applications. In this study, a five‐degree‐of‐freedom inchworm robotic platform is developed using modular T/I joints and its kinematic model is derived based on the Denavit–Hartenberg approach. Multiple gait modes, including creeping, rotation, flipping and obstacle negotiation, are investigated. To address the high energy consumption during the creeping gait, an optimisation framework constrained by both kinematic and dynamic factors is established. This framework adopts a deviation‐tracking strategy that combines cubic polynomials in the Cartesian space with 7th‐order polynomials in the joint space. Mathematical models for energy evaluation, trajectory formulation and spatial curve similarity are also developed. Additionally, an improved adaptive particle swarm optimisation algorithm is proposed, integrating three enhancements: a Sigmoid‐based update rule, a position‐updating scheme inspired by the golden sine algorithm and a tabu‐region constraint mechanism. These modifications result in faster convergence and higher precision. Finally, a complete control system is designed, and experimental tests conducted under the creeping and rotating gaits verify the practicality and effectiveness of the proposed motion‐planning method.