Time-aware Motion Planning in Dynamic Environments with Conformal Prediction

TL;DR

This paper introduces two conformal prediction-based motion planning frameworks that provide safety guarantees and adaptively manage uncertainty for navigation in dynamic environments.

Contribution

It presents novel global and local planners using conformal prediction with adaptive quantile tuning for safe, robust, and feasible navigation amidst uncertain obstacle behaviors.

Findings

Global planner offers distribution-free safety guarantees.

Adaptive quantile mechanism improves trajectory feasibility.

Framework validated in dynamic, cluttered environments.

Abstract

Safe navigation in dynamic environments remains challenging due to uncertain obstacle behaviors and the lack of formal prediction guarantees. We propose two motion planning frameworks that leverage conformal prediction (CP): a global planner that integrates Safe Interval Path Planning (SIPP) for uncertainty-aware trajectory generation, and a local planner that performs online reactive planning. The global planner offers distribution-free safety guarantees for long-horizon navigation, while the local planner mitigates inaccuracies in obstacle trajectory predictions through adaptive CP, enabling robust and responsive motion in dynamic environments. To further enhance trajectory feasibility, we introduce an adaptive quantile mechanism in the CP-based uncertainty quantification. Instead of using a fixed confidence level, the quantile is automatically tuned to the optimal value that preserves trajectory feasibility, allowing the planner to adaptively tighten safety margins in regions with higher uncertainty. We validate the proposed framework through numerical experiments conducted in dynamic and cluttered environments. The project page is available at https://time-aware-planning.github.io