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Navigation in an unknown environment without any preexisting positioning infrastructure has always been hard for mobile robots. This paper presents a self-deployable ultra wideband UWB infrastructure by mobile agents, that permits a dynamic placement and runtime extension of UWB anchors infrastructure while the robot explores the new environment. We provide a detailed analysis of the uncertainty of the positioning system while the UWB infrastructure grows. Moreover, we developed a genetic algorithm that minimizes the deployment of new anchors, saving energy and resources on the mobile robot and maximizing the time of the mission. Although the presented approach is general for any class of mobile system, we run simulations and experiments with indoor drones. Results demonstrate that maximum positioning uncertainty is always controlled under the user’s threshold, using the Geometric Dilution of Precision (GDoP). [ABSTRACT FROM AUTHOR]

The positioning problem addressed in this article amounts to finding the planar coordinates of a device from a collection of ranging measurements taken from other devices located at known positions. The solution based on weighted least square (WLS) is popular, but its accuracy depends from a number of factors only partially known. In this article, we explore the dependency of the uncertainty from the geometric configuration of the anchors. We show a refinement technique for the estimate produced by the WLS that compensates for the effects of geometry on the WLS and reduces the target uncertainty to a value very close to the Cramer–Rao Lower Bound. The resulting algorithm is called geometric WLS (G-WLS) and its application is particularly important in the most critical conditions for WLS (i.e., when the target is far apart from the anchors). The effectiveness of the G-WLS is proven theoretically and is demonstrated on a large number of experiments and simulations. [ABSTRACT FROM AUTHOR]

This article presents the application of the synthetic aperture radar (SAR) localization method for indoor positioning of ultrahigh-frequency (UHF)-radio frequency identification (RFID) tags when the robot-mounted reader antenna moves along multiple trajectories. By properly combining the phase data associated with a set of multiple paths, the whole length of the combined synthetic apertures enlarges, and then, the localization accuracy may improve. Besides, during consecutive inventory rounds, several tag position estimates are available, and they can be profitably combined to minimize the localization uncertainty. Different combination approaches are investigated to determine the best choice to improve the localization performance. The method capabilities are discussed through numerical analysis by considering different configurations of the multiple apertures and different measurement uncertainty sources. Finally, the proposed localization method is validated through an experimental analysis carried out with commercial RFID hardware and a robotic wheeled walker, in an indoor scenario, by employing different types of tags. The knowledge of the reader/robot trajectory required by the SAR method is here achieved with an optical system. [ABSTRACT FROM AUTHOR]

In this article, we present a framework for planning an activity to be executed with the support of a robotic navigation assistant. The two main components are the activity and the motion planner. The activity planner composes a sequence of abstract activities, chosen from a given set, to synthesize a plan. Each activity is associated with a point of interest in the environment and with probabilistic parameters that depend on the plan, which are characterized by simulations in realistic scenarios. The low-level action to pass from an activity to the next is handled by the motion planner, which secures the physical feasibility of the chosen actions and their compatibility with the constraints posed by the user and the environment. Indeed, the final plan must respect the user constraints and optimise his/her satisfaction from the activity. We show a possible model for the problem as a chance constrained optimization along with an efficient technique to find high-quality solutions. [ABSTRACT FROM AUTHOR]

The phase of the signals backscattered by the ultrahigh frequency radio-frequency identification (UHF-RFID) tags is generally more insensitive to multipath propagation than the received signal strength indicator (RSSI). However, signal phase measurements are inherently ambiguous and could be further affected by the unknown phase offsets added by the transponders. As a result, the localization of an agent by using only signal phase measurements looks infeasible. In this article, it is shown instead that the design of a dynamic position estimator (e.g., a Kalman filter) based only on the signal phase measurement is actually possible. To this end, the necessary conditions to ensure the theoretical local nonlinear observability are first demonstrated. However, a system that is locally observable guarantees the convergence of the localization algorithm only if the actual initial agent position is approximately known a priori. Therefore, the second part of the analysis covers the global observability, which ensures convergence starting from any initial condition in the state space. It is important to emphasize that complete observability holds only in theory. In fact, measurement uncertainty may greatly affect position estimation convergence. The validity of the analysis and the practicality of this localization approach are further confirmed by numerical simulations based on an unscented Kalman filter (UKF). [ABSTRACT FROM AUTHOR]

A well-known, crucial problem for indoor positioning of mobile agents (e.g., robots) equipped with exteroceptive sensors is related to the need to deploy reference landmarks in a given environment. Normally, anytime a landmark is detected, an agent estimates its own location and attitude with respect to landmark position and/or orientation in the chosen reference frame. When instead no landmark is recognized, other sensors (e.g., odometers in the case of wheeled robots) can be used to track the agent position and orientation from the last detected landmark. At the moment, landmark placement is usually based just on common-sense criteria, which are not formalized properly. As a result, positioning uncertainty tends to grow unpredictably. On the contrary, the purpose of this paper is to minimize the number of landmarks, while ensuring that localization uncertainty is kept within wanted boundaries. The developed approach relies on the following key features: a dynamic model describing agents’ motion, a model predicting the agents’ paths within a given environment and, finally, a conjunctive normal form formalization of the optimization problem, which can be efficiently (although approximately) solved by a greedy algorithm. The effectiveness of the proposed landmark placement technique is first demonstrated through simulations in a variety of conditions and then it is validated through experiments on the field, by using non-Bayesian and Bayesian position tracking algorithms. [ABSTRACT FROM AUTHOR]

Robotic walkers are a promising solution for physical and cognitive support to older adults. This paper proposes a low cost path following strategy combining the advantages of a simple mechanical braking guidance, such as safety, passivity, and a low cost, and the ones of a vibrotactile haptic guidance, such as comfort and portability. The user is guided by providing indications on the directions of motion using the haptic interface so that he/she can autonomously and comfortably follow the planned path. However, whenever the user significantly departs from the path (for instance, s/he gets too close to obstacles), the braking system kicks in to safely steer the user back along the proper direction. The formal correctnesses of the hybrid strategy ruling the combination of the two guidance systems is proved theoretically. Moreover, a comprehensive experimental study with users aged 64 to 100, including also psychological evaluations, has been performed. The hybrid combination of the braking and the haptic guidance systems is shown to outperform the two individual approaches in isolation. The combination of the two retains the same level of the users’ perceived comfort typical of the haptic-only guidance while ensuring the adequate path following performance typical of the braking-only guidance. In particular, the combined approach produces a mean path following error equal to 41% of the mean path following error ensured by the haptic-only approach. Conversely, thanks to the haptic feedback, the combined approach halves the activation time and the number of interventions needed in the braking-only approach. [ABSTRACT FROM AUTHOR]