Trajectory Planning for Safe Dual Control with Active Exploration

TL;DR

This paper introduces Dual-gatekeeper, a framework for safe dual control that balances active exploration and task performance under safety and budget constraints, demonstrated on autonomous vehicle case studies.

Contribution

It presents a novel dual control approach that formally guarantees safety and budget adherence while actively reducing uncertainty during missions.

Findings

Framework achieves safe exploration without violating safety constraints.

Demonstrated on quadrotor navigation and autonomous racing with uncertain parameters.

Balances immediate task performance with long-term uncertainty reduction.

Abstract

Planning safe trajectories under model uncertainty is a fundamental challenge. Robust planning ensures safety by considering worst-case realizations, yet ignores uncertainty reduction and leads to overly conservative behavior. Actively reducing uncertainty on-the-fly during a nominal mission defines the dual control problem. Most approaches address this by adding a weighted exploration term to the cost, tuned to trade off the nominal objective and uncertainty reduction, but without formal consideration of when exploration is beneficial. Moreover, safety is enforced in some methods but not in others. We study a budget-constrained dual control problem, where uncertainty is reduced subject to safety and a mission-level cost budget that limits the allowable degradation in task performance due to exploration. In this work, we propose Dual-gatekeeper, a framework that integrates robust planning with active exploration under formal guarantees of safety and budget feasibility. The key idea is that exploration is pursued only when it provides a verifiable improvement without compromising safety or violating the budget, enabling the system to balance immediate task performance with long-term uncertainty reduction in a principled manner. We provide two implementations of the framework based on different safety mechanisms and demonstrate its performance on quadrotor navigation and autonomous car racing case studies under parametric uncertainty.