{"ID":2841190,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.12022","arxiv_id":"2511.12022","title":"SBAMP: Sampling Based Adaptive Motion Planning","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.","short_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 p...","url_abs":"https://arxiv.org/abs/2511.12022","url_pdf":"https://arxiv.org/pdf/2511.12022v3","authors":"[\"Shreyas Raorane\",\"Kabir Ram Puri\",\"Anh-Quan Pham\"]","published":"2025-11-15T04:16:20Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"eess.SY\"]","methods":"[]","has_code":false}