TY - JOUR
T1 - Assured runtime monitoring and planning
T2 - Toward verification of neural networks for safe autonomous operations
AU - Yel, Esen
AU - Carpenter, Taylor J.
AU - Di Franco, Carmelo
AU - Ivanov, Radoslav
AU - Kantaros, Yiannis
AU - Lee, Insup
AU - Weimer, James
AU - Bezzo, Nicola
N1 - Publisher Copyright:
© 1994-2011 IEEE.
PY - 2020/6
Y1 - 2020/6
N2 - Autonomous systems operating in uncertain envi ronments under the effects of disturbances and noises can reach unsafe states even while using finetuned controllers and precise sensors and actuators. To provide safety guarantees on such systems during motion planning operations, reachability analysis (RA) has been demonstrated to be a powerful tool. RA, however, suffers from computational complexity, especially when dealing with intricate systems characterized by high-order dynamics, making it hard to deploy for runtime monitoring.
AB - Autonomous systems operating in uncertain envi ronments under the effects of disturbances and noises can reach unsafe states even while using finetuned controllers and precise sensors and actuators. To provide safety guarantees on such systems during motion planning operations, reachability analysis (RA) has been demonstrated to be a powerful tool. RA, however, suffers from computational complexity, especially when dealing with intricate systems characterized by high-order dynamics, making it hard to deploy for runtime monitoring.
UR - https://www.scopus.com/pages/publications/85083698520
U2 - 10.1109/MRA.2020.2981114
DO - 10.1109/MRA.2020.2981114
M3 - Article
AN - SCOPUS:85083698520
SN - 1070-9932
VL - 27
SP - 102
EP - 116
JO - IEEE Robotics and Automation Magazine
JF - IEEE Robotics and Automation Magazine
IS - 2
M1 - 9068251
ER -