Application of improved sparrow search algorithm and dynamic window method in mobile robot path planning and real‐time obstacle avoidance - Shen, Lingyu; bin Ahmad Hairuddin, Muhammad Khairi Faiz (Journal of engineering (Stevenage, England), 2025)
In complex dynamic environments, robot path planning faces challenges in multi‐objective optimization, such as path length, smoothness and obstacle avoidance capability. To address this, this paper proposes an improved sparrow search algorithm based on chaotic initialization and the golden positive cosine strategy for multi‐objective path planning. Diverse initial populations are generated through chaotic mapping to enhance global search capability and avoid falling into local optima. The golden positive cosine strategy optimizes individual position updates to accelerate convergence and ensure path smoothness. Results demonstrate that the proposed method outperforms sparrow search algorithm (SSA), with improvements of 21.1% in path length, 16.3% in smoothness and 14.2% in obstacle avoidance capability. After achieving global path optimization, the improved dynamic window approach (IDWA) is employed for real‐time obstacle avoidance in dynamic environments, dynamically adjusting the window size based on robot speed, obstacle density and target distance to adaptively expand or shrink the search space, thereby improving obstacle avoidance flexibility and efficiency. Simulation results show that the proposed method surpasses SSA in terms of path length, smoothness, obstacle avoidance capability and computational efficiency in both static and dynamic environments.