Efficient Swept Volume-Based Trajectory Generation for Arbitrary-Shaped Ground Robot Navigation

2025-04-10

AI Summary

This paper addresses the real-time, continuous collision avoidance (CCA) trajectory planning problem for ground robots with arbitrary geometries operating in dense environments. We propose a “coarse-to-fine” three-level collaborative framework: (1) topology-guided sampling for safe path generation; (2) decoupled modeling on the SE(2) manifold and Euclidean space for geometry-aware piecewise trajectory parameterization; and (3) Swept Volume Signed Distance Field (SVSDF)-driven subproblem optimization with seamless B-spline stitching. To our knowledge, this is the first framework integrating topological sampling, SE(2)/ℝ² decoupling, and SVSDF-based optimization—enabling millisecond-scale CCA trajectory generation. Experiments demonstrate a 10–1000× speedup over state-of-the-art methods, >98% planning success rate, and strict adherence to CCA constraints.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Navigating an arbitrary-shaped ground robot safely in cluttered environments remains a challenging problem. The existing trajectory planners that account for the robot's physical geometry severely suffer from the intractable runtime. To achieve both computational efficiency and Continuous Collision Avoidance (CCA) of arbitrary-shaped ground robot planning, we proposed a novel coarse-to-fine navigation framework that significantly accelerates planning. In the first stage, a sampling-based method selectively generates distinct topological paths that guarantee a minimum inflated margin. In the second stage, a geometry-aware front-end strategy is designed to discretize these topologies into full-state robot motion sequences while concurrently partitioning the paths into SE(2) sub-problems and simpler R2 sub-problems for back-end optimization. In the final stage, an SVSDF-based optimizer generates trajectories tailored to these sub-problems and seamlessly splices them into a continuous final motion plan. Extensive benchmark comparisons show that the proposed method is one to several orders of magnitude faster than the cutting-edge methods in runtime while maintaining a high planning success rate and ensuring CCA.

Problem

• Efficient trajectory planning for arbitrary-shaped ground robots
• Overcoming high runtime in geometry-aware path planning
• Ensuring continuous collision avoidance in cluttered environments

Innovation

• Coarse-to-fine navigation framework accelerates planning
• Geometry-aware front-end partitions paths into sub-problems
• SVSDF-based optimizer generates and splices trajectories