CSF-Planner: A Lightweight Local Trajectory Planner Based on Cloth Simulation Field for UAV

Abstract

Existing planners commonly rely on cell-based environment representation approaches, which oversimplify the geometry and face the challenges of low spatial resolution and high maintenance costs. Inspired by cloth simulation techniques, we propose a novel environment representation method called Cloth Simulation Field (CSF). By simulating the interactions of cloth particles under gravity in the forward direction, obstacles and free areas can be quickly delineated. Furthermore, the distribution of particles provides natural auxiliary information for generating collision-free trajectories. Therefore, we further propose a trajectory generation method compatible with CSF. Different from traditional optimization-based planners, which often rely on repulsive forces from obstacles to push trajectories away and consequently introduce non-convexity, the proposed method leverages the unique gradient in CSF to transform repulsive forces into attractive forces, enabling trajectory generation to be formulated as a convex optimization problem, which is further solved in closed form, significantly reducing computational costs. Finally, the proposed method is validated through simulations and real-world experiments. The comparative experiments demonstrate that our planner outperforms state-of-the-art planners in terms of trajectory quality and replanning time. Our project has been open-sourced as a ROS package.