2017/10/12 by David Fridovich-Keil, Sylvia Herbert, Fridovich-Keil, David +7 · 3 citations
Computer Science · Engineering · #Computer Science and Game Theory (cs.GT) #FOS: Computer and information sciences #FOS: Electrical engineering #Formal Methods in Verification #Robotic Path Planning Algorithms #Robotics and Sensor-Based Localization #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1710.04731
openalex publication_date 2017/10/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Motion planning is an extremely well-studied problem in the robotics\ncommunity, yet existing work largely falls into one of two categories:\ncomputationally efficient but with few if any safety guarantees, or able to\ngive stronger guarantees but at high computational cost. This work builds on a\nrecent development called FaSTrack in which a slow offline computation provides\na modular safety guarantee for a faster online planner. We introduce the notion\nof "meta-planning" in which a refined offline computation enables safe\nswitching between different online planners. This provides autonomous systems\nwith the ability to adapt motion plans to a priori unknown environments in\nreal-time as sensor measurements detect new obstacles, and the flexibility to\nmaneuver differently in the presence of obstacles than they would in free\nspace, all while maintaining a strict safety guarantee. We demonstrate the\nmeta-planning algorithm both in simulation and in hardware using a small\nCrazyflie 2.0 quadrotor.\n