SBAMP: Sampling Based Adaptive Motion Planning
For autonomous robots operating in dynamic environments, SBAMP addresses the trade-off between global path optimality and real-time responsiveness without requiring pre-trained data.
SBAMP combines RRT*-based global planning with an online Lyapunov-stable controller to enable real-time adaptation to disturbances while preserving global path structure, achieving robust recovery and obstacle handling in dynamic environments.
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.