TY - GEN
T1 - Planning, Fast and Slow
T2 - 2018 IEEE International Conference on Robotics and Automation, ICRA 2018
AU - Fridovich-Keil, David
AU - Herbert, Sylvia L.
AU - Fisac, Jaime F.
AU - Deglurkar, Sampada
AU - Tomlin, Claire J.
N1 - Funding Information:
This research is supported by NSF under the CPS Frontiers VehiCal project (1545126), by the UC-Philippine-California Advanced Research Institute under project IIID-2016-005, and by the ONR MURI Embedded Humans (N00014-16-1-2206). S. Herbert and D. Fridovich-Keil are funded by the NSF GRFP. S. Herbert is also funded by the UC Berkeley Chancellor's Fellowship
Publisher Copyright:
© 2018 IEEE.
PY - 2018/9/10
Y1 - 2018/9/10
N2 - Motion planning is an extremely well-studied problem in the robotics community, yet existing work largely falls into one of two categories: computationally efficient but with few if any safety guarantees, or able to give stronger guarantees but at high computational cost. This work builds on a recent development called FaSTrack in which a slow offline computation provides a modular safety guarantee for a faster online planner. We introduce the notion of 'meta-planning' in which a refined offline computation enables safe switching between different online planners. This provides autonomous systems with the ability to adapt motion plans to a priori unknown environments in real-time as sensor measurements detect new obstacles, and the flexibility to maneuver differently in the presence of obstacles than they would in free space, all while maintaining a strict safety guarantee. We demonstrate the meta-planning algorithm both in simulation and in hardware using a small Crazyflie 2.0 quadrotor.
AB - Motion planning is an extremely well-studied problem in the robotics community, yet existing work largely falls into one of two categories: computationally efficient but with few if any safety guarantees, or able to give stronger guarantees but at high computational cost. This work builds on a recent development called FaSTrack in which a slow offline computation provides a modular safety guarantee for a faster online planner. We introduce the notion of 'meta-planning' in which a refined offline computation enables safe switching between different online planners. This provides autonomous systems with the ability to adapt motion plans to a priori unknown environments in real-time as sensor measurements detect new obstacles, and the flexibility to maneuver differently in the presence of obstacles than they would in free space, all while maintaining a strict safety guarantee. We demonstrate the meta-planning algorithm both in simulation and in hardware using a small Crazyflie 2.0 quadrotor.
UR - http://www.scopus.com/inward/record.url?scp=85063133189&partnerID=8YFLogxK
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U2 - 10.1109/ICRA.2018.8460863
DO - 10.1109/ICRA.2018.8460863
M3 - Conference contribution
AN - SCOPUS:85063133189
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 387
EP - 394
BT - 2018 IEEE International Conference on Robotics and Automation, ICRA 2018
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 21 May 2018 through 25 May 2018
ER -