Online Trajectory Optimization for Arbitrary-Shaped Mobile Robots via Polynomial Separating Hypersurfaces

TL;DR

This paper introduces a novel trajectory optimization method for arbitrary-shaped robots using polynomial hypersurfaces for collision avoidance, removing the need for convex approximations and enabling more accurate navigation in complex environments.

Contribution

It generalizes the separating hyperplane theorem to polynomial hypersurfaces, allowing collision avoidance for nonconvex geometries without conservative approximations.

Findings

Successfully avoids collisions in complex environments

Enables smooth and agile robot maneuvers

Outperforms convex-approximation baselines

Abstract

An emerging class of trajectory optimization methods enforces collision avoidance by jointly optimizing the robot's configuration and a separating hyperplane. However, as linear separators only apply to convex sets, these methods require convex approximations of both the robot and obstacles, which becomes an overly conservative assumption in cluttered and narrow environments. In this work, we unequivocally remove this limitation by introducing nonlinear separating hypersurfaces parameterized by polynomial functions. We first generalize the classical separating hyperplane theorem and prove that any two disjoint bounded closed sets in Euclidean space can be separated by a polynomial hypersurface, serving as the theoretical foundation for nonlinear separation of arbitrary geometries. Building on this result, we formulate a nonlinear programming (NLP) problem that jointly optimizes the robot's trajectory and the coefficients of the separating polynomials, enabling geometry-aware collision avoidance without conservative convex simplifications. The optimization remains efficiently solvable using standard NLP solvers. Simulation and real-world experiments with nonconvex robots demonstrate that our method achieves smooth, collision-free, and agile maneuvers in environments where convex-approximation baselines fail.