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This paper introduces AURA, a meta-planner framework for kinodynamic systems that combines online replanning with control input optimization to improve path quality and tracking accuracy under motion uncertainty. AURA leverages continuous state-space exploration and trajectory refinement during execution to achieve asymptotic optimality. Evaluations in simulation and real-world environments demonstrate that AURA consistently outperforms baseline methods in trajectory quality, tracking accuracy, and overall performance.
Achieve asymptotically optimal kinodynamic planning online while simultaneously improving execution accuracy under uncertainty with a novel meta-planner framework.
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.