TY - GEN
T1 - Sampling-based min-max uncertainty path planning
AU - Englot, Brendan
AU - Shan, Tixiao
AU - Bopardikar, Shaunak D.
AU - Speranzon, Alberto
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/12/27
Y1 - 2016/12/27
N2 - We propose a new sampling-based path planning algorithm, the Min-Max Rapidly Exploring Random Tree (MM-RRT∗), for robot path planning under localization uncertainty. The projected growth of error in a robot's state estimate is curbed by minimizing the maximum state estimate uncertainty encountered on a path. The algorithm builds and maintains a tree that is shared in state space and belief space, with a single belief per robot state. Due to the fact that many states will share the same maximum uncertainty, resulting from a shared parent node, the algorithm uses secondary objective functions to break ties among neighboring nodes with identical maximum uncertainty. The algorithm offers a compelling alternative to sampling-based algorithms with additive cost representations of uncertainty, which will penalize high-precision navigation routes that are longer in duration.
AB - We propose a new sampling-based path planning algorithm, the Min-Max Rapidly Exploring Random Tree (MM-RRT∗), for robot path planning under localization uncertainty. The projected growth of error in a robot's state estimate is curbed by minimizing the maximum state estimate uncertainty encountered on a path. The algorithm builds and maintains a tree that is shared in state space and belief space, with a single belief per robot state. Due to the fact that many states will share the same maximum uncertainty, resulting from a shared parent node, the algorithm uses secondary objective functions to break ties among neighboring nodes with identical maximum uncertainty. The algorithm offers a compelling alternative to sampling-based algorithms with additive cost representations of uncertainty, which will penalize high-precision navigation routes that are longer in duration.
UR - http://www.scopus.com/inward/record.url?scp=85010817840&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85010817840&partnerID=8YFLogxK
U2 - 10.1109/CDC.2016.7799326
DO - 10.1109/CDC.2016.7799326
M3 - Conference contribution
AN - SCOPUS:85010817840
T3 - 2016 IEEE 55th Conference on Decision and Control, CDC 2016
SP - 6863
EP - 6870
BT - 2016 IEEE 55th Conference on Decision and Control, CDC 2016
T2 - 55th IEEE Conference on Decision and Control, CDC 2016
Y2 - 12 December 2016 through 14 December 2016
ER -