This paper proposes an improved dung beetle optimizer algorithm (IDBO) combined with the dynamic window approach (DWA) for robot path planning in static and dynamic environments. The algorithm models the rolling, breeding, foraging, and stealing behaviors of dung beetles. To enhance the algorithm's performance, four improvements are introduced: (1) an initial population initialization method using Chebyshev chaos map to increase search randomness and diversity; (2) curve adaptive golden sine strategy (CGSS) to replace the rolling dung beetle position update formula to improve convergence rate and accuracy; (3) Levy flights with Cauchy-t mutation strategy (LCTS) to enhance exploratory power and adaptability; and (4) dynamic weight coefficient to adjust stealing behavior for better adaptability and robustness. The improved algorithm shows significant improvements in search efficiency and solution quality, outperforming traditional DBO and other optimization algorithms in convergence speed and global search capability. The algorithm is tested on 23 classical test functions and applied to a raster map environment, demonstrating its effectiveness in path planning. The study fills a gap in the literature by applying improved DBO to robot path planning, aiming to enhance robot navigation in complex and dynamic environments and promote robotics applications. The paper is organized into five chapters: an introduction, DBO principles, algorithm improvements, experimental verification, and a conclusion. The DBO algorithm is inspired by dung beetle behaviors, with key steps including rolling ball behavior and dancing behavior location update formulas. The improved algorithm combines these with DWA to achieve dynamic path planning.This paper proposes an improved dung beetle optimizer algorithm (IDBO) combined with the dynamic window approach (DWA) for robot path planning in static and dynamic environments. The algorithm models the rolling, breeding, foraging, and stealing behaviors of dung beetles. To enhance the algorithm's performance, four improvements are introduced: (1) an initial population initialization method using Chebyshev chaos map to increase search randomness and diversity; (2) curve adaptive golden sine strategy (CGSS) to replace the rolling dung beetle position update formula to improve convergence rate and accuracy; (3) Levy flights with Cauchy-t mutation strategy (LCTS) to enhance exploratory power and adaptability; and (4) dynamic weight coefficient to adjust stealing behavior for better adaptability and robustness. The improved algorithm shows significant improvements in search efficiency and solution quality, outperforming traditional DBO and other optimization algorithms in convergence speed and global search capability. The algorithm is tested on 23 classical test functions and applied to a raster map environment, demonstrating its effectiveness in path planning. The study fills a gap in the literature by applying improved DBO to robot path planning, aiming to enhance robot navigation in complex and dynamic environments and promote robotics applications. The paper is organized into five chapters: an introduction, DBO principles, algorithm improvements, experimental verification, and a conclusion. The DBO algorithm is inspired by dung beetle behaviors, with key steps including rolling ball behavior and dancing behavior location update formulas. The improved algorithm combines these with DWA to achieve dynamic path planning.