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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. Fault Detection and Response for Safe Control of Artificial Muscles in Soft Robots

   https://doi.org/10.1109/LCSYS.2025.3610637  

   Jing, Ran; Hook, Charles Van; Yang, Ilyoung; Sabelhaus, Andrew P (September 2024, IEEE Control Systems Letters)

   Robots built from soft materials have the potential for intuitively-safer interactions with humans and the environment. However, soft robots’ embodiments have many sources of failure that could lead to unsafe conditions in closed-loop control, such as degradation of sensors or fracture of actuators. This letter proposes a fault detection system for sensors attached to artificial muscle actuators that satisfies a formal safety condition. Our approach combines redundant sensing, model-based state estimation, and Gaussian process regression to determine when one sensor’s reading statistically diverges from another, indicating a fault condition. We apply the approach to electrothermal shape memory alloy (SMA) artificial muscles, demonstrating that our method prevents the overheating and fire damage risk that could otherwise occur. Experiments show that when the muscle’s nominal sensor (temperature via a thermocouple) is fractured from the robot, the redundant sensor (electrical resistance) combined with our method prevents violation of state constraints. Deploying this system in real-world human-robot interaction could help make soft robots more robust and reliable. 

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