Safe and Efficient Trajectory Planning for Unmanned Ground Platforms in Complex Unstructured Environments

Abstract

Irregular, dense, and abruptly varying obstacle distributions complicate local trajectory planning in unstructured environments by imposing stringent safety constraints under limited computational resources. We propose an adaptive rectangular-inflation method that reduces the iterations required by fixed-step safe-corridor construction. A smoothed guide path defines the corridor geometry, and a circumscribed disk conservatively approximates the vehicle footprint. A signed distance field rapidly estimates the clearance between each rectangular boundary and nearby obstacles. The estimated clearances determine both the inflation step and the priority of the four expansion directions. Within the resulting convex corridor, we formulate a Minimum Snap trajectory using differential-flatness-based parameterization and piecewise seventh-degree polynomials. A quadratic program generates the local trajectory subject to continuity, corridor-containment constraints, and derivative bounds. After optimization, a Bézier convex-hull check provides a sufficient condition for continuous corridor containment. Across the two scenarios, adaptive inflation reduced corridor-construction time by 84.55% and 87.21%, respectively, while iteration counts decreased by 90.31% and 89.36%. Comparisons with model predictive control (MPC) and Minimum Acceleration in both scenarios showed smaller jerk variations for Minimum Snap over much of the motion. The method improved corridor-construction efficiency in the evaluated static grid environments while generating local reference trajectories with smooth position, velocity, and acceleration profiles.