SBAMP: Sampling Based Adaptive Motion Planning

TL;DR

SBAMP is a hybrid motion planning framework that combines global RRT*-based planning with real-time, Lyapunov-stable local control inspired by SEDS, enabling robust, adaptive robot navigation in dynamic environments.

Contribution

It introduces a novel hybrid approach integrating sampling-based global planning with online, data-free local control for adaptive motion planning.

Findings

Demonstrated robust disturbance recovery in simulation and hardware.

Achieved reliable obstacle avoidance and dynamic environment handling.

Maintained near-optimal global paths while enabling real-time adaptation.

Abstract

Autonomous robots operating in dynamic environments must balance global path optimality with real-time responsiveness to disturbances. This requires addressing a fundamental trade-off between computationally expensive global planning and fast local adaptation. Sampling-based planners such as RRT* produce near-optimal paths but struggle under perturbations, while dynamical systems approaches like SEDS enable smooth reactive behavior but rely on offline data-driven optimization. We introduce Sampling-Based Adaptive Motion Planning (SBAMP), a hybrid framework that combines RRT*-based global planning with an online, Lyapunov-stable SEDS-inspired controller that requires no pre-trained data. By integrating lightweight constrained optimization into the control loop, SBAMP enables stable, real-time adaptation while preserving global path structure. Experiments in simulation and on RoboRacer hardware demonstrate robust recovery from disturbances, reliable obstacle handling, and consistent performance under dynamic conditions.