Trajectory Planning for Safe Dual Control with Active Exploration

2026-04-16

AI Summary

This work addresses safe trajectory planning under model uncertainty by proposing a Dual-gatekeeper framework that, for the first time, jointly incorporates safety constraints and task-performance budgets within a dual-controller architecture. The approach guarantees formal safety while triggering active exploration only when such exploration can be verified to improve long-term performance. By synergistically combining robust planning with conditional exploration, the method balances immediate task execution with the reduction of uncertainty. Experimental evaluations in quadrotor and autonomous racing scenarios demonstrate that the proposed framework generates trajectories that are not only provably safe and highly efficient but also exhibit strong adaptability, significantly outperforming existing baselines.

Technology Category

• Planning, Routing, and Scheduling: Planning under Uncertainty
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Natural Language Processing: Safety and Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

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.

Problem

• dual control
• model uncertainty
• safe trajectory planning
• active exploration
• budget-constrained optimization

Innovation

• dual control
• active exploration
• safety guarantees
• budget-constrained optimization
• robust trajectory planning