Motion Planning and Control with Unknown Nonlinear Dynamics through Predicted Reachability

2025-03-05

AI Summary

Autonomous motion planning in environments with unknown nonlinear dynamics remains challenging due to the lack of reliable system models and guaranteed trajectory feasibility. Method: This paper proposes a unified planning–control adaptive framework. It employs state-space partitioning and online piecewise affine (PWA) modeling, integrating prior knowledge with real-time system identification to construct predictive reachability conditions. A novel heuristic edge-weighting mechanism—grounded in existential determinism—guides exploration–exploitation-balanced incremental directed weighted graph evolution; controller synthesis is performed in real time via an A*-like search. Contribution/Results: This work is the first to jointly integrate predictive reachability analysis, existence-aware graph evolution, and closed-loop PWA control. Evaluated in simulated mobile robot navigation over unknown terrain, the framework significantly improves trajectory feasibility and navigation robustness compared to baseline approaches.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Planning under Uncertainty
• Intelligent Robots: Motion and Path Planning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Autonomous motion planning under unknown nonlinear dynamics presents significant challenges. An agent needs to continuously explore the system dynamics to acquire its properties, such as reachability, in order to guide system navigation adaptively. In this paper, we propose a hybrid planning-control framework designed to compute a feasible trajectory toward a target. Our approach involves partitioning the state space and approximating the system by a piecewise affine (PWA) system with constrained control inputs. By abstracting the PWA system into a directed weighted graph, we incrementally update the existence of its edges via affine system identification and reach control theory, introducing a predictive reachability condition by exploiting prior information of the unknown dynamics. Heuristic weights are assigned to edges based on whether their existence is certain or remains indeterminate. Consequently, we propose a framework that adaptively collects and analyzes data during mission execution, continually updates the predictive graph, and synthesizes a controller online based on the graph search outcomes. We demonstrate the efficacy of our approach through simulation scenarios involving a mobile robot operating in unknown terrains, with its unknown dynamics abstracted as a single integrator model.

Problem

• Autonomous motion planning under unknown nonlinear dynamics.
• Adaptive exploration and control in uncertain environments.
• Online synthesis of controllers using predictive reachability analysis.

Innovation

• Hybrid planning-control framework for unknown dynamics
• Piecewise affine system approximation with graph abstraction
• Online controller synthesis via predictive reachability updates