TY - GEN
T1 - Localization safety validation for autonomous robots
AU - Arana, Guillermo Duenas
AU - Hafez, Osama Abdul
AU - Joerger, Mathieu
AU - Spenko, Matthew
N1 - Funding Information:
*This work was supported by NSF Grant #1637899. 1GD. Arana, OA. Hafez and M. Spenko are with the Mechanical, Materials and Aerospace Department, Illinois Institute of Technology, Chicago, IL, USA gdueasar@hawk.iit.edu 2M. Joerger is with the Department of Aerospace and Ocean Engineering, Virginia Tech, Blacksburg, VA, USA
Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/24
Y1 - 2020/10/24
N2 - This paper presents a method to validate localization safety for a preplanned trajectory in a given environment. Localization safety is defined as integrity risk and quantified as the probability of an undetected localization failure. Integrity risk differs from previously used metrics in robotics in that it accounts for unmodeled faults and evaluates safety under the worst possible combination of faults. The methodology can be applied prior to mission execution and thus can be employed to evaluate the safety of potential trajectories. The work has been formulated for localization via smoothing, which differs from previously reported integrity monitoring methods that rely on Kalman filtering. Simulation and experimental results are analyzed to show that localization safety is effectively quantified.
AB - This paper presents a method to validate localization safety for a preplanned trajectory in a given environment. Localization safety is defined as integrity risk and quantified as the probability of an undetected localization failure. Integrity risk differs from previously used metrics in robotics in that it accounts for unmodeled faults and evaluates safety under the worst possible combination of faults. The methodology can be applied prior to mission execution and thus can be employed to evaluate the safety of potential trajectories. The work has been formulated for localization via smoothing, which differs from previously reported integrity monitoring methods that rely on Kalman filtering. Simulation and experimental results are analyzed to show that localization safety is effectively quantified.
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U2 - 10.1109/IROS45743.2020.9341434
DO - 10.1109/IROS45743.2020.9341434
M3 - Conference contribution
AN - SCOPUS:85095962232
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 6276
EP - 6281
BT - 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020
Y2 - 24 October 2020 through 24 January 2021
ER -