Hierarchical Motion Planning and Control under Unknown Nonlinear Dynamics via Predicted Reachability

TL;DR

This paper presents a hierarchical motion planning and control framework for unknown nonlinear systems, combining adaptive partitioning, reachability analysis, and online learning to enable safe and efficient navigation.

Contribution

It introduces a novel hierarchical approach that integrates piecewise-affine modeling, adaptive partitioning, and reachability-based graph search for unknown nonlinear dynamics.

Findings

Effective exploration-exploitation trade-offs demonstrated in simulations.

The framework provides formal reachability guarantees.

Extension to underactuated systems via relaxed reachability conditions.

Abstract

Autonomous motion planning under unknown nonlinear dynamics requires learning system properties while navigating toward a target. In this work, we develop a hierarchical planning-control framework that enables online motion synthesis with limited prior system knowledge. The state space is partitioned into polytopes and approximates the unknown nonlinear system using a piecewise-affine (PWA) model. The local affine models are identified once the agent enters the corresponding polytopes. To reduce computational complexity, we introduce a non-uniform adaptive state space partition strategy that refines the partition only in task-relevant regions. The resulting PWA system is abstracted into a directed weighted graph, whose edge existence is incrementally verified using reach control theory and predictive reachability conditions. Certified edges are weighted using provable time-to-reach bounds, while uncertain edges are assigned information-theoretic weights to guide exploration. The graph is updated online as new data becomes available, and high-level planning is performed by graph search, while low-level affine feedback controllers are synthesized to execute the plan. Furthermore, the conditions of classical reach control theory are often difficult to satisfy in underactuated settings. We therefore introduce relaxed reachability conditions to extend the framework to such systems. Simulations demonstrate effective exploration-exploitation trade-offs with formal reachability guarantees.