AURA: Asymptotically Optimal Uncertainty-Robust Replanning Algorithm for Kinodynamic Systems

2026-05-26

AI Summary

This work addresses the limitations of conventional sampling-based motion planners, which are typically restricted to offline settings and struggle to handle motion uncertainties that lead to trajectory tracking errors. To overcome these challenges, the paper proposes a unified online replanning framework that continuously optimizes future control inputs during execution to enhance both trajectory quality and tracking accuracy. The approach uniquely integrates asymptotic optimality with real-time replanning by combining sampling-based planning, online state-space exploration, and control optimization, thereby enabling high-precision trajectory tracking for dynamical systems in uncertain environments. Extensive simulations and physical experiments demonstrate that the proposed method significantly outperforms existing baselines in terms of trajectory quality, tracking accuracy, and overall performance.

Abstract

Sampling-based motion planners offer a practical and scalable approach to kinodynamic motion planning, notably for high-dimensional, underactuated, or non-holonomic systems. However, these planners are typically used offline, requiring execution to begin only after the trajectory has been computed. In addition, the planned trajectory may not be accurately tracked in the presence of motion uncertainty, leading to deviations from the nominal solution. In this work, these limitations were addressed within a unified framework, \method, an asymptotically-optimal meta-planner framework that improves both path quality and tracking performance during execution. In addition to the main execution thread, this framework comprises a replanning method that continuously explores the state space and refines the trajectory during execution, and an optimization process that refines future control inputs to reduce tracking error. Together, these components enable \method to leverage asymptotically optimal planning online while improving execution accuracy under uncertainty. The proposed approach is evaluated in both simulation and real-world environments across multiple systems, demonstrating consistent improvements in trajectory quality, tracking accuracy, and overall performance compared with baseline methods.

Problem

• kinodynamic motion planning
• motion uncertainty
• trajectory tracking
• online replanning
• asymptotic optimality

Innovation

• asymptotically optimal
• uncertainty-robust
• online replanning
• kinodynamic planning
• trajectory tracking