ECAHD: Efficient Collision-Aware Hierarchical Diffusion Navigation

Abstract

In this work, we propose Efficient Collision-Aware Hierarchical Diffusion Navigation (ECAHD), a hierarchical diffusion-based framework designed for both safety and computational efficiency. ECAHD generates a sparse trajectory for global path planning and a dense trajectory for local path refinement. The robot follows a rapidly sampled sparse global trajectory, and when a potential collision is detected, a collision-aware guidance diffusion mechanism—which accounts for the robot’s shape—adjusts the local trajectory accordingly. Conventional full-sequence diffusion planners suffer from slow sampling speeds and performance degradation when collision-aware guidance is applied across the entire trajectory. ECAHD addresses these issues by significantly reducing the number of waypoints predicted by the global diffusion planner, while delegating robot shape aware collision guidance to the local diffusion planner. This separation not only accelerates planning but also preserves global trajectory quality, as goal-conditioned sampling is no longer disrupted by collision-related constraints. Furthermore, ECAHD allows for increasing the number of global trajectory samples to enhance performance, without incurring substantial computational overhead. In maze2d-large planning tests, ECAHD improved success rates by approximately 1.3% while reducing collision rates by more than 50%, all while cutting inference time by nearly half.