Robust and Recursively Feasible Real-Time Trajectory Planning in Unknown Environments

TL;DR

This paper introduces a real-time trajectory planner for mobile robots in unknown environments that guarantees robustness and persistent feasibility without relying on braking, suitable for systems where braking is risky.

Contribution

The paper presents a novel recursive planning algorithm that ensures robustness and feasibility without braking, applicable to a wider range of robotic systems.

Findings

Runs at over 16 Hz in experiments

Successfully avoids dead-ends in unknown environments

Maintains safety and feasibility during real-time operation

Abstract

Motion planners for mobile robots in unknown environments face the challenge of simultaneously maintaining both robustness against unmodeled uncertainties and persistent feasibility of the trajectory-finding problem. That is, while dealing with uncertainties, a motion planner must update its trajectory, adapting to the newly revealed environment in real-time; failing to do so may involve unsafe circumstances. Many existing planning algorithms guarantee these by maintaining the clearance needed to perform an emergency brake, which is itself a robust and persistently feasible maneuver. However, such maneuvers are not applicable for systems in which braking is impossible or risky, such as fixed-wing aircraft. To that end, we propose a real-time robust planner that recursively guarantees persistent feasibility without any need of braking. The planner ensures robustness against bounded uncertainties and persistent feasibility by constructing a loop of sequentially composed funnels, starting from the receding horizon local trajectory's forward reachable set. We implement the proposed algorithm for a robotic car tracking a speed-fixed reference trajectory. The experiment results show that the proposed algorithm can be run at faster than 16 Hz, while successfully keeping the system away from entering any dead-end, to maintain safety and feasibility.