Convex Risk Bounded Continuous-Time Trajectory Planning in Uncertain Nonconvex Environments

TL;DR

This paper introduces a novel risk bounded trajectory planning method for uncertain nonconvex environments, transforming the problem into a deterministic optimization solved with convex sum-of-squares techniques, enabling efficient online planning.

Contribution

It presents a risk contour-based approach that handles arbitrary probabilistic uncertainties and nonconvex obstacles without relying on sampling or discretization.

Findings

Successfully plans risk-bounded trajectories in complex environments.

Handles non-Gaussian uncertainties and dynamic obstacles.

Provides a convex optimization framework for real-time applications.

Abstract

In this paper, we address the trajectory planning problem in uncertain nonconvex static and dynamic environments that contain obstacles with probabilistic location, size, and geometry. To address this problem, we provide a risk bounded trajectory planning method that looks for continuous-time trajectories with guaranteed bounded risk over the planning time horizon. Risk is defined as the probability of collision with uncertain obstacles. Existing approaches to address risk bounded trajectory planning problems either are limited to Gaussian uncertainties and convex obstacles or rely on sampling-based methods that need uncertainty samples and time discretization. To address the risk bounded trajectory planning problem, we leverage the notion of risk contours to transform the risk bounded planning problem into a deterministic optimization problem. Risk contours are the set of all points in the uncertain environment with guaranteed bounded risk. The obtained deterministic optimization is, in general, nonlinear and nonconvex time-varying optimization. We provide convex methods based on sum-of-squares optimization to efficiently solve the obtained nonconvex time-varying optimization problem and obtain the continuous-time risk bounded trajectories without time discretization. The provided approach deals with arbitrary probabilistic uncertainties, nonconvex and nonlinear, static and dynamic obstacles, and is suitable for online trajectory planning problems.