This paper proposes an improved dung beetle optimization algorithm (IDBO) combined with the dynamic window approach (DWA) for robot path planning in both static and dynamic environments. The IDBO models the rolling, breeding, foraging, and stealing behaviors of dung beetles, addressing the limitations of the conventional DBO by introducing four enhancements: an initial population initialization using Chebyshev chaos map, a curve adaptive golden sine strategy (CGSS) for position update, a Levy flights with Cauchy-$\gamma$ mutation strategy (LCFS) for reproductive and foraging beetles, and a dynamic weight coefficient for stealing behavior. These improvements enhance the algorithm's search efficiency, solution quality, and adaptability. The performance of the IDBO is validated through test functions and experiments, showing faster convergence and better global search capability compared to traditional DBO and other optimization algorithms. The study aims to improve robot navigation in complex and dynamic environments, contributing to advancements in robotics and society.This paper proposes an improved dung beetle optimization algorithm (IDBO) combined with the dynamic window approach (DWA) for robot path planning in both static and dynamic environments. The IDBO models the rolling, breeding, foraging, and stealing behaviors of dung beetles, addressing the limitations of the conventional DBO by introducing four enhancements: an initial population initialization using Chebyshev chaos map, a curve adaptive golden sine strategy (CGSS) for position update, a Levy flights with Cauchy-$\gamma$ mutation strategy (LCFS) for reproductive and foraging beetles, and a dynamic weight coefficient for stealing behavior. These improvements enhance the algorithm's search efficiency, solution quality, and adaptability. The performance of the IDBO is validated through test functions and experiments, showing faster convergence and better global search capability compared to traditional DBO and other optimization algorithms. The study aims to improve robot navigation in complex and dynamic environments, contributing to advancements in robotics and society.