SBAMP: Sampling Based Adaptive Motion Planning

Autonomous robotic systems must navigate complex, dynamic environments in real time, often facing unpredictable obstacles and rapidly changing conditions. Traditional sampling-based methods, such as RRT*, excel at generating collision-free paths but struggle to adapt to sudden changes without extensive replanning. Conversely, learning-based dynamical systems, such as the Stable Estimator of Dynamical Systems (SEDS), offer smooth, adaptive trajectory tracking but typically rely on pre-collected demonstration data, limiting their generalization to novel scenarios. This paper introduces Sampling-Based Adaptive Motion Planning (SBAMP), a novel framework that overcomes these limitations by integrating RRT* for global path planning with a SEDS-based local controller for continuous, adaptive trajectory adjustment. Our approach requires no pre-trained datasets and ensures smooth transitions between planned waypoints, maintaining stability through Lyapunov-based guarantees. We validate SBAMP in both simulated environments and real hardware using the RoboRacer platform, demonstrating superior performance in dynamic obstacle scenarios, rapid recovery from perturbations, and robust handling of sharp turns. Experimental results highlight SBAMP's ability to adapt in real time without sacrificing global path optimality, providing a scalable solution for dynamic, unstructured environments.