Reliable simultaneous localization and mapping (SLAM) algorithms are necessary for safety-critical autonomous navigation. In the communication-constrained multi-agent setting, navigation systems increasingly use point-to-point range sensors as they afford measurements with low bandwidth requirements and known data association. The state estimation problem for these systems takes the form of range-aided (RA) SLAM. However, distributed algorithms for solving the RA-SLAM problem lack formal guarantees on the quality of the returned estimate. To this end, we present the first distributed algorithm for RA-SLAM that can efficiently recover certifiably globally optimal solutions. Our algorithm, distributed certifiably correct RA-SLAM (DCORA), achieves this via the Riemannian Staircase method, where computational procedures developed for distributed certifiably correct pose graph optimization are generalized to the RA-SLAM problem. We demonstrate DCORA's efficacy on real-world multi-agent datasets by achieving absolute trajectory errors comparable to those of a state-of-the-art centralized certifiably correct RA-SLAM algorithm. Additionally, we perform a parametric study on the structure of the RA-SLAM problem using synthetic data, revealing how common parameters affect DCORA's performance.

Routine inspections for critical infrastructures such as bridges are required in most jurisdictions worldwide. Such routine inspections are largely visual in nature, which are qualitative, subjective, and not repeatable. Although robotic infrastructure inspections address such limitations, they cannot replace the superior ability of experts to make decisions in complex situations, thus making human-robot interaction systems a promising technology. This study presents a novel gaze-based human-robot interaction system, designed to augment the visual inspection performance through mixed reality. Through holograms from a mixed reality device, gaze can be utilized effectively to estimate the properties of the defect in real-time. Additionally, inspectors can monitor the inspection progress on-line, which enhances the speed of the entire inspection process. Limited controlled experiments demonstrate its effectiveness across various users and defect types. To our knowledge, this is the first demonstration of the real-time application of eye gaze in civil infrastructure inspections.

Early detection of small and large leaks in water distribution pipes allows for proactive maintenance and corrective actions to take place in a timely manner, thus mitigating significant water loss and increasing the longevity of the network. Most of the acoustic leak detection methods today are geared toward inspections—focused on probing periodic short-term data acquired in the process of inspection—rather than dealing with large volumes of long-term data acquired from monitoring programs. The common challenge encountered in both the acoustic inspection methods and in long-term monitoring of acoustic signatures lies in delineating weak leak-induced signatures within the highly noisy and nonstationary acoustic environment typical of uncontrolled real-world operating water distribution systems. This paper focuses on addressing the problem of leak detection where long-term monitoring acoustic data is available to characterize the operating conditions, without relying on controlled experiments to acquire data or expert user knowledge. The key contribution of this paper is to present a new data-driven approach using association rules (ARs) to extract information from large volumes of monitored acoustic data which can enable identification of relatively small changes in the acoustic signatures due to leaks. ARs are employed to model and synthesize the information contained in long-term monitored acoustic data and associations between statistical features obtained from such measurements are identified and used to design a leak indicator that captures the deviation of leak-induced data from a reference leak-free model. It will be shown that the proposed indicator has a high detection rate, can detect relatively small leaks, and crucially, conducive to work in uncontrolled long-term monitoring situations.

Techniques for diagnosing faults in hybrid systems that combine digital (discrete) supervisory controllers with analog (continuous) plants need to be different from those used for discrete or continuous systems. This paper presents a methodology for online tracking and diagnosis of hybrid systems. We demonstrate the effectiveness of the approach with experiments conducted on the fuel-transfer system of fighter aircraft

Model based approaches to diagnosis for dynamic systems have been based on continuous and discrete event models. Systems that combine continuous and discrete behaviors, i.e., hybrid systems have been typically abstracted into discrete event models or approximated by continuous models with steep slopes so that existing algorithms can be applied for fault isolation tasks. This approach runs into problems when both discrete events and continuous behaviors provide vital diagnostic information. We propose a diagnostic methodology that uses hybrid models of the system to perform diagnosis.