Mohammed A. Shalaby

Ultra-wideband (UWB) systems are becoming increasingly popular as a means of inter-robot ranging and communication. A major constraint associated with UWB is that only one pair of UWB transceivers can range at a time to avoid interference, hence hindering the scalability of UWB-based localization. In this article, a ranging protocol is proposed that allows all robots to passively listen on neighboring communicating robots without any hierarchical restrictions on the role of the robots. This is utilized to allow each robot to obtain more range measurements and to broadcast preintegrated inertial measurement unit (IMU) measurements for relative extended pose state estimation directly on SE2(3). Consequently, a simultaneous clock-synchronization and relative-pose estimator is formulated using an on-manifold extended Kalman filter (EKF) and is evaluated in simulation using Monte Carlo runs for up to seven robots. The ranging protocol is implemented in C on custom-made UWB boards fitted to three quadcopters, and the proposed filter is evaluated over multiple experimental trials, yielding up to 48% improvement in localization accuracy.

It is often convenient to separate a state estimation task into smaller “local” tasks, where each local estimator estimates a subset of the overall system state. However, neglecting cross-covariance terms between state estimates can result in overconfident estimates, which can ultimately degrade the accuracy of the estimator. Common cascaded filtering techniques focus on the problem of modelling cross-covariances when the local estimators share a common state vector. This letter introduces a novel cascaded and decentralized filtering approach that approximates the cross-covariances when the local estimators consider distinct state vectors. The proposed estimator is validated in simulations and in experiments on a three-dimensional attitude and position estimation problem. The proposed approach is compared to a naive cascaded filtering approach that neglects cross-covariance terms, a sigma point-based Covariance Intersection filter, and a full-state filter. In both simulations and experiments, the proposed filter outperforms the naive and the Covariance Intersection filters, while performing comparatively to the full-state filter.

The ability to accurately estimate the position of robotic agents relative to one another, in possibly GPS-denied environments, is crucial to execute collaborative tasks. Inter-agent range measurements are available at a low cost, due to technologies such as ultra-wideband radio. However, the task of three-dimensional relative position estimation using range measurements in multi-agent systems suffers from unobservabilities. This letter presents a sufficient condition for the observability of the relative positions, and satisfies the condition using a simple framework with only range measurements, an accelerometer, a rate gyro, and a magnetometer. The framework has been tested in simulation and in experiments, where 40-50 cm positioning accuracy is achieved using inexpensive off-the-shelf hardware.

This thesis examines the utilization of ultra-wideband (UWB) radio for robot navigation in indoor applications. UWB, a wireless communication technology, offers a means of generating distance (or range) measurements and establishing communication channels among mobile robots and fixed anchors at known locations. When fixed anchors are present, UWB can be harnessed for precise localization, enabling, for example, the tracking of robots in warehouse applications. In unexplored environments lacking a fixed infrastructure, UWB transceivers installed on different robots facilitate inter-robot ranging and communication, thereby enabling relative localization - an essential prerequisite for tasks such as maintaining a group formation, collaboratively mapping an area, or ensuring effective collision avoidance. In order to maximize the potential of UWB for localization purposes, it becomes imperative to tackle fundamental challenges of UWB, such as clock synchronization and the choice of ranging protocol, concurrently with the development of the state estimation algorithm. Conventionally, these two issues have been addressed separately.The primary objective of this thesis is to resolve both the underlying low-level UWB ranging and communication performance challenges, as well as the state estimation problem, ultimately providing a practical, robust, and implementable solution for indoor navigation. To this end, this thesis delves into the matter of UWB measurement calibration, aiming to enhance ranging accuracy and characterize measurement uncertainty for use in a probabilistic framework. Subsequently, the selection of an appropriate ranging protocol is motivated to facilitate localization and efficient inter-robot communication. Lastly, this thesis employs UWB measurements within a filtering framework to tackle the problem of state estimation, encompassing both a scenario involving a single robot performing a closed-loop teach-and-repeat experiment and a multi-robot scenario where the robots perform on-manifold relative localization and attitude estimation using preintegration techniques. Comprehensive testing of all proposed algorithms is conducted through simulation and real-world experiments employing custom-built UWB modules fitted onto quadcopters.

Time-of-flight-based ranging among transceivers with different clocks requires protocols that accommodate varying rates of the clocks. Double-sided two-way ranging (DS-TWR) is widely adopted as a standard protocol due to its accuracy; however, the precision of DS-TWR has not been clearly addressed. In this article, an analytical model of the variance of DS-TWR is derived as a function of the user-programmed response delays, which is then compared with the Cramer–Rao lower bound . This is then used to formulate an optimization problem over the response delays in order to maximize the information gained from range measurements. The derived analytical variance model and optimized protocol are validated experimentally with two ranging ultrawideband transceivers, where 29 million range measurements are collected.

Ultra-Wideband (UWB) systems are becoming increasingly popular for indoor localization, where range measurements are obtained by measuring the time-of-flight of radio signals. However, the range measurements typically suffer from a systematic error or bias that must be corrected for high-accuracy localization. In this paper, a ranging protocol is proposed alongside a robust and scalable antenna-delay calibration procedure to accurately and efficiently calibrate antenna delays for many UWB tags. Additionally, the bias and uncertainty of the measurements are modelled as a function of the received-signal power. The full calibration procedure is presented using experimental training data of 3 aerial robots fitted with 2 UWB tags each, and then evaluated on 2 test experiments. A localization problem is then formulated on the experimental test data, and the calibrated measurements and their modelled uncertainty are fed into an extended Kalman filter (EKF). The proposed calibration is shown to yield an average of 46% improvement in localization accuracy. Lastly, the paper is accompanied by an open-source UWB-calibration Python library, which can be found at https://github.com/decargroup/uwb_calibration.