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The problem of quantifying robot localization safety in the presence of undetected sensor faults is critical when preparing for future applications where robots may interact with humans in life-critical situations; however, the topic is only sparsely addressed in the robotics literature. In response, this work leverages prior work in aviation integrity monitoring to tackle the more challenging case of evaluating localization safety in Global Navigation Satellite System (GNSS)-denied environments. Localization integrity risk is the probability that a robot’s pose estimate lies outside pre-defined acceptable limits while no alarm is triggered. In this article, the integrity risk (i.e., localization safety) is rigorously upper bounded by accounting for both nominal sensor noise and other non-nominal sensor faults. An extended Kalman filter is employed to estimate the robot state, and a sequence of innovations is used for fault detection. The novelty of the work includes (1) the use of a time window to limit the number of monitored fault hypotheses while still guaranteeing safety with respect to previously occurring faults and (2) a new method to account for faults in the data association process. 

This research project aims to achieve a future urban environment where people and self-driving cars coexist together while guaranteeing safety. To modify the environment, our first approach is to understand the limitations of GPS/GNSS positioning in an urban area where signal blockages and reflections make positioning difficult. For the evaluation process, we assume reasonable integrity requirements and calculate navigation availability along a sample Chicago urban corridor (State Street). We reject all non-line-of-sight (NLOS) that are blocked and reflected using a 3-D map. The availability of GPS-only positioning is determined to be less than 10% at most locations. Using four full GNSS constellations, availability improves significantly but is still lower than 80 % at certain points. The results establish the need for integration with other navigation sensors, such as inertial navigation systems (INS) and Lidar, to ensure integrity. The analysis methods introduced will form the basis to determine performance requirements for these additional sensors. 

This paper describes a new type of compliant and configurable soft robot, a boundary-constrained swarm. The robot consists of a sealed flexible membrane that constrains both a number of mobile robotic subunits and passive granular material. The robot can change the volume fraction of the sealed membrane by applying a vacuum, which gives the robot the ability to operate in two distinct states: compliant and jammed. The compliant state allows the robot to surround and conform to objects or pass through narrow corridors. Jamming allows the robot to form a desired shape; grasp, (a) manipulate, and exert relatively high forces on external objects; and achieve relatively higher locomotion speeds. Locomotion is achieved with a combination of whegs (wheeled legs) and vibration motors that are located on the robotic subunits. The paper describes the mechanical design of the robot, the control methodology, and its object handling capability. 

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.