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The localization problem for multi-robot teams has been extensively studied with the goal of obtaining precise positioning information, such as required by a variety of robotic applications. This paper proposes a dynamic localization approach that exploits multiple robots equipped with range-only ultrawideband sensors to create and maintain a common self-adaptive coordinate system. For 2-D localization, we use three robots with relative range measurements to build a global coordinate system. We recursively apply an extended Kalman filter, which results in accurate position estimates over time. We also propose a reconfiguration approach that prevents error accumulation from ultra-wideband sensors. The applicability of our approach is tested through a campaign of simulations, which show promising results.

This study provides an effective new method to solve the range-only localization in the presence of sensor position errors. In practice, the sensors can stay only within a limited region whereas the target can be far from there. To increase the estimation capability, some peripheral measurements with moving sensors can be obtained, which results in the issue of imprecise sensor positions. In these situations, sensor positions also become unknown parameters which need to be jointly estimated together with the target location. Because of the large number of unknown parameters, reaching the global minimum becomes a significant challenge. Our study is dedicated to build a robust localization scheme for these scenarios. We propose a new search strategy, namely Circular Uncertainty which allows the localization system to safely find the global minimum of maximum likelihood cost function in case of imprecise sensor positions. Circular Uncertainty not only makes it possible to reach maximum likelihood estimation, but also significantly simplifies this task. Our solution is based on the observation that when the initial estimation is disturbed with new measurements, the disturbed estimation moves along the Circular Uncertainty which can be viewed as a circular valley along the cost surface. The new method is compared to nonlinear least squares as well as the squared range weighted least-squares algorithm which was previously designed in the literature specifically for localization with imprecise sensor positions. Since the proposed solution obtains maximum likelihood estimation, it attains Cramer Rao lower bound, where other competing methods partly fail. [ABSTRACT FROM AUTHOR]

The paper addresses observability issues related to the general problem of single and multiple Autonomous Underwater Vehicle (AUV) localization using only range measurements. While an AUV is submerged, localization devices, such as Global Navigation Satellite Systems, are ineffective, due to the attenuation of electromagnetic waves. AUV localization based on dead reckoning techniques and the use of affordable motion sensor units is also not practical, due to divergence caused by sensor bias and drift. For these reasons, localization systems often build on trilateration algorithms that rely on the measurements of the ranges between an AUV and a set of fixed transponders using acoustic devices. Still, such solutions are often expensive, require cumbersome calibration procedures and only allow for AUV localization in an area that is defined by the geometrical arrangement of the transponders. A viable alternative for AUV localization that has recently come to the fore exploits the use of complementary information on the distance from the AUV to a single transponder, together with information provided by on-board resident motion sensors, such as, for example, depth, velocity and acceleration measurements. This concept can be extended to address the problem of relative localization between two AUVs equipped with acoustic sensors for inter-vehicle range measurements. Motivated by these developments, in this paper, we show that both the problems of absolute localization of a single vehicle and the relative localization of multiple vehicles can be treated using the same mathematical framework, and tailoring concepts of observability derived for nonlinear systems, we analyze how the performance in localization depends on the types of motion imparted to the AUVs. For this effect, we propose a well-defined observability metric and validate its usefulness, both in simulation and by carrying out experimental tests with a real marine vehicle during which the performance of an Extended Kalman Filter state observer is shown to depend on the types of motion imparted to the vehicle.

In this paper, the target tracking problem is investigated for a tracking system with mobile range-only sensors. Being different from most previous studies, both additive and multiplicative noises in measurements are taken into consideration. An optimal coordination strategy, including sensor selection and sensor motion, is proposed to maximize the tracking accuracy. In particular, by fully utilizing the properties of objective function, the search space and variables of the original optimization problem can be significantly reduced. Based on this reduction, three algorithms are designed, respectively, for the following: 1) efficient selection of task sensors; 2) reduction on combinations of task sensors; and 3) efficient search of optimal sensor motion. The performance of the proposed coordination strategy is illustrated by simulations. [ABSTRACT FROM PUBLISHER]

The paper addresses observability issues related to the general problem of single and multiple Autonomous Underwater Vehicle (AUV) localization using only range measurements. While an AUV is submerged, localization devices, such as Global Navigation Satellite Systems, are ineffective, due to the attenuation of electromagnetic waves. AUV localization based on dead reckoning techniques and the use of affordable motion sensor units is also not practical, due to divergence caused by sensor bias and drift. For these reasons, localization systems often build on trilateration algorithms that rely on the measurements of the ranges between an AUV and a set of fixed transponders using acoustic devices. Still, such solutions are often expensive, require cumbersome calibration procedures and only allow for AUV localization in an area that is defined by the geometrical arrangement of the transponders. A viable alternative for AUV localization that has recently come to the fore exploits the use of complementary information on the distance from the AUV to a single transponder, together with information provided by on-board resident motion sensors, such as, for example, depth, velocity and acceleration measurements. This concept can be extended to address the problem of relative localization between two AUVs equipped with acoustic sensors for inter-vehicle range measurements. Motivated by these developments, in this paper, we show that both the problems of absolute localization of a single vehicle and the relative localization of framework, and tailoring concepts of observability derived for nonlinear systems, we analyze how the performance in localization depends on the types of motion imparted to the AUVs. For this effect, we propose a well-de?ned observability metric and validate its usefulness, both in simulation and by carrying out experimental tests with a real marine vehicle during which the performance of an Extended Kalman Filter state observer is shown to depend on the types of motion imparted to the vehicle.multiple vehicles can be treated using the same mathematical [ABSTRACT FROM AUTHOR]

We present an application of a novel framework and algorithms for: (1) conservatively and recursively incorporating information obtained through sensors that yield observations that are non-linear functions of the state; and (2) finding control inputs that continuously improve the quality of the resulting estimates. We present an experimental study of the application of our framework to mobile robots utilizing range-only sensors, and demonstrate its effectiveness in dealing with problems of target localization with one or more robots where traditional approaches involving linearization fail to consistently capture uncertainty. [ABSTRACT FROM AUTHOR]