Manipulator Trajectory Planning Based on Multi-Strategy Improved Chicken Swarm Optimization Algorithm

Trajectory optimization of manipulators is crucial for achieving efficient, low-energy-consumption, and stable operation. The standard Chicken Swarm Optimization (CSO) algorithm and its variants tend to fall into local optima during optimization, making it difficult to obtain the optimal trajectory. Therefore, this paper employs multiple strategies to collaboratively improve the chicken swarm optimization algorithm: Tent chaotic mapping is used for population initialization, the position update strategies of the three populations are respectively reconstructed to expand the search scope of the population, and a genetic evolution strategy is introduced to escape from local optimal solutions. Simulation outcomes based on the CEC2022 benchmark functions show that the Multi-strategy Improved Chicken Swarm Optimization (MICSO) algorithm outperforms commonly used optimization algorithms at present in both convergence speed and solution accuracy. The MICSO is applied to the 3-5-3 polynomial interpolation trajectory planning of manipulators, and a comprehensive optimal objective function is constructed by combining time, energy consumption, and jerk. Simulation and experimental results demonstrate that the MICSO algorithm can flexibly adjust the optimization tendency according to practical requirements. The optimized trajectory effectively reduces running time, energy consumption, and joint impact.