FDSPC: Fast and Direct Smooth Motion Planning Via Continuous Curvature Integration
Zong Chen, Haoluo Shao, Ben Liu, Siyuan Qiao, Yu Zhou, Yiqun Li
AI summary
Problem
Existing path planning algorithms typically produce non-smooth, zigzag paths requiring costly post-processing, ignore 3D/2.5-D obstacle-crossing capabilities, and struggle with real-time trajectory tracking in complex terrains.
Approach
The method uses continuous curvature integration to iteratively explore and construct collision-free paths with G2 continuity directly in configuration space, combined with a heuristic binary tree search and curvature-based velocity planning.
Key results
- Direct generation of G2-smooth paths without post-processing
- Faster solution times and reduced memory usage versus SOTA
- Configurable z-axis expansion for 2.5-D obstacle crossing
- Experimental validation on a custom wheel-legged robot
Why it matters
Enables real-time, kinematically feasible trajectory tracking for mobile robots navigating complex 2.5-D environments without costly smoothing or replanning.
Abstract
In recent decades, mobile robot motion planning has seen significant advancements. Both search-based and sampling- based methods have demonstrated capabilities to find feasible solutions in complex scenarios. Mainstream path planning al- gorithms divide the map into occupied and free spaces, con- sidering only planar movement and ignoring the ability of mobile robots to traverse obstacles in the z-direction. Addi- tionally, paths generated often have numerous bends, requiring additional smoothing post-processing. In this work, a fast, and direct motion planning method based on continuous curvature integration that takes into account the robot’s obstacle-crossing ability under different parameter settings is proposed. This method generates smooth paths directly with pseudo-constant velocity and limited curvature, and performs curvature-based speed planning in complex 2.5-D terrain-based environment (take into account the ups and downs of the terrain), eliminating the subsequent path smoothing process and enabling the robot to track the path generated directly. The proposed method is also compared with some existing approaches in terms of solution time, path length, memory usage and smoothness under multiple scenarios. The proposed method is vastly superior to the average performance of state-of-the-art (SOTA) methods, especially in terms of the self-defined S2 smoothness (mean angle of steering). Furthermore, simulations and experiments are conducted on our self-designed wheel-legged robot with 2.5-D traversability. These results demonstrate the effectiveness and superiority of the proposed approach in several representative environments.1