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1. Recursive Integrity Monitoring for Mobile Robot Localization Safety

   https://doi.org/10.1109/ICRA.2019.8794115  

   Arana, Guillermo Duenas; Hafez, Osama Abdul; Joerger, Mathieu; Spenko, Matthew (May 2019, 2019 International Conference on Robotics and Automation (ICRA))

   null (Ed.)

   This paper presents a new methodology to quantify robot localization safety by evaluating integrity risk, a performance metric widely used in open-sky aviation applications that has been recently extended to mobile ground robots. Here, a robot is localized by feeding relative measurements to mapped landmarks into an Extended Kalman Filter while a sequence of innovations is evaluated for fault detection. The main contribution is the derivation of a sequential chi-squared integrity monitoring methodology that maintains constant computation requirements by employing a preceding time window and, at the same time, is robust against faults occurring prior to the window. Additionally, no assumptions are made on either the nature or shape of the faults because safety is evaluated under the worst possible combination of sensor faults. 

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2. Integrity Risk-Based Model Predictive Control for Mobile Robots

   https://doi.org/10.1109/ICRA.2019.8793521  

   Hafez, Osama Abdul; Arana, Guillermo Duenas; Spenko, Matthew (May 2019, IEEE International Conference on Robotics and Automation)

   null (Ed.)

   This paper presents a Model Predictive Controller (MPC) that uses navigation integrity risk as a constraint. Navigation integrity risk accounts for the presence of faults in localization sensors and algorithms, an increasingly important consideration as the number of robots operating in life and mission-critical situations is expected to increase dramatically in near future (e.g. a potential influx of self-driving cars). Specifically, the work uses a local nearest neighbor integrity risk evaluation methodology that accounts for data association faults as a constraint in order to guarantee localization safety over a receding horizon. Moreover, state and control-input constraints have also been enforced in this work. The proposed MPC design is tested using real-world mapped environments, showing that a robot is capable of maintaining a predefined minimum level of localization safety while operating in an urban environment. 

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3. Efficient Integrity Monitoring for KF-based Localization

   https://doi.org/10.1109/ICRA.2019.8794362  

   Arana, Guillermo Duenas; Joerger, Mathieu; Spenko, Matthew (May 2019, 2019 International Conference on Robotics and Automation (ICRA))

   null (Ed.)

   This paper presents a new method to efficiently monitor localization safety in mobile robots. Localization safety is quantified by measuring the system's integrity risk, which is a well-known aviation performance metric. However, aviation integrity monitoring solutions almost exclusively rely on the Global Navigation Satellite System (GNSS) while robot navigation usually needs the additional information provided by a state evolution model and/or relative positioning sensors, which makes previously established approaches impractical. In response, this paper develops an efficient integrity monitoring methodology applicable to Kalman Filter-based localization. The work is intended for life-or mission-critical operations such as co-robot applications where ignoring the impact of faults can jeopardize human safety. 

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4. On Robot Localization Safety for Fixed-Lag Smoothing: Quantifying the Risk of Misassociation

   https://doi.org/10.1109/PLANS46316.2020.9110126  

   Hafez, Osama Abdul; Arana, Guillermo Duenas; Chen, Yihe; Joerger, Mathieu; Spenko, Matthew (April 2020, 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS))

   Monitoring localization safety will be necessary to certify the performance of robots that operate in life-critical applications, such as autonomous passenger vehicles or delivery drones because many current localization safety methods do not account for the risk of undetected sensor faults. One type of fault, misassociation, occurs when a feature extracted from a mapped landmark is associated to a non-corresponding landmark and is a common source of error in feature-based navigation applications. This paper accounts for the probability of misassociation when quantifying landmark-based mobile robot localization safety for fixed-lag smoothing estimators. We derive a mobile robot localization safety bound and evaluate it using simulations and experimental data in an urban environment. Results show that localization safety suffers when landmark density is relatively low such that there are not enough landmarks to adequately localize and when landmark density is relatively high because of the high risk of feature misassociation. 

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5. A Novel Quotient Space Approach to Model-Based Fault Detection and Isolation: Theory and Preliminary Simulation Evaluation

   https://doi.org/10.1109/IROS51168.2021.9636026  

   Mao, Annie M.; Whitcomb, Louis L. (September 2021, 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   We report the development of novel fault detection and isolation (FDI) methods for model-based fault detection (MB-FD) and quotient-space fault isolation (QS-FI). This FDI approach performs MB-FD and QS-FI of single or multiple concurrent faults in plants and actuators simultaneously, without a priori knowledge of fault form, type, or dynamics. To detect faults, MB-FD characterizes deviation from nominal behavior using the plant velocity and plant and actuator parameters estimated by nullspace-based adaptive identification. To isolate (i.e. identify) faults, the QS-FI algorithm compares the estimated parameters to a nominal parameter class in progressively decreasing-dimensional quotient spaces of the parameter space. A preliminary simulation study of these proposed FDI methods applied to a three degree-of-freedom uninhabited underwater vehicle plant model shows their ability to detect as well as isolate faults for the cases of both single and multiple simultaneous faults and suggests the generalizability of the MB-FD and QS-FI approaches to any well-defined second-order plant and actuator model whose parameters enter linearly: a broad class of systems which includes aerial vehicles, marine vehicles, spacecraft, and robot arms. 

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