Chance-Constrained Trajectory Optimization for High-DOF Robots in Uncertain Environments

TL;DR

This paper introduces SCORA, a risk-bounded trajectory optimization method for high-DOF robots that effectively manages environmental and tracking uncertainties, improving safety and efficiency in motion planning.

Contribution

The paper presents a novel chance-constrained optimization algorithm, SCORA, that handles multiple sources of uncertainty in high-dimensional robotic motion planning.

Findings

SCORA outperforms existing risk-aware planners in simulation.

SCORA achieves safer trajectories with reduced planning time.

The method effectively manages environmental and tracking uncertainties.

Abstract

Many practical applications of robotics require systems that can operate safely despite uncertainty. In the context of motion planning, two types of uncertainty are particularly important when planning safe robot trajectories. The first is environmental uncertainty -- uncertainty in the locations of nearby obstacles, stemming from sensor noise or (in the case of obstacles' future locations) prediction error. The second class of uncertainty is uncertainty in the robots own state, typically caused by tracking or estimation error. To achieve high levels of safety, it is necessary for robots to consider both of these sources of uncertainty. In this paper, we propose a risk-bounded trajectory optimization algorithm, known as Sequential Convex Optimization with Risk Optimization (SCORA), to solve chance-constrained motion planning problems despite both environmental uncertainty and tracking error. Through experiments in simulation, we demonstrate that SCORA significantly outperforms state-of-the-art risk-aware motion planners both in planning time and in the safety of the resulting trajectories.