Dynamic obstacle avoidance planning for multi-robot suspension system based on SDBO–IDWA algorithm and force–position cooperative optimization

Abstract

Abstract To address dynamic obstacle avoidance planning in multi-robot coordinated suspension systems (MCSS), this study proposes a hybrid method integrating an enhanced stable dung beetle optimization (SDBO) algorithm with an improved dynamic window approach (IDWA). Dynamic obstacles are addressed through IDWA-based trajectory prediction, while the SDBO–IDWA algorithm optimizes obstacle avoidance trajectories for suspended objects. Furthermore, leveraging force–position cooperative optimization, the method resolves coupled kinematic and dynamic constraints inherent in MCSS. Simulation and experimental results demonstrate that the SDBO–IDWA algorithm outperforms traditional approaches, achieving a 19.95% reduction in minimum trajectory length and a 57.77% decrease in runtime for suspended objects. For towing robots, it reduces optimal trajectory length by 9.52% and fitness values by 9.44%. The findings advance planning theory and enable safe, stable multi-robot suspension systems for diverse towing applications.