Active Persistent Localization of a Three-Dimensional Moving Target Under Set-Membership Uncertainty Description Through Cooperation of Multiple Mobile Robots

Abstract

Abstract-The persistent localization of moving targets is one of the most important applications of mobile robot systems. However, the sensors on each individual robot are often not sufficiently accurate for this task, presenting a severe limitation on the application of robots to many situations. To overcome this problem, systems of multiple robots have been established. These systems improve the localization accuracy of mobile targets by fusing together multiple sensing data. Numerous studies have demonstrated that the relative motion (or specifically, the relative pose) between the robot and the target, as well as that among different robots, strongly influences the final localization accuracy of the data fusion algorithm. In other words, the regulation of motion in a multiple-robot system will lead to better localization results. Thus, in this paper, a new active persistent localization (APL) scheme is proposed. The underlying concept of this scheme is based on set-membership descriptions of uncertainties. The basic problem is formulated in the framework of an enhanced set-membership filter, and a new data fusion algorithm with improved accuracy and convergence properties is designed. A motion-planning algorithm is then incorporated under the so-called optimal localization condition to complete the APL scheme. Simulations using multiple 3-D mobile robot systems and experiments on a multiple rotor-flying-robots test bed show that the proposed algorithm successfully enhances the accuracy of the persistent localization of the moving target.

A set-membership approach to find and track multiple targets using a fleet of UAVs

“…To implement the proposed approach, one has to evaluate the direct image of a set by a function as in (7) or (8), the reciprocal image of a set by a function, as in (10) and (11), to compute the difference between two sets, as in (13) or (14), and finally, to compute the volume of sets. Here, all sets are described using subpavings, i.e., unions of non-overlapping interval vectors, as in [10] and [9].…”

“…Here, all sets are described using subpavings, i.e., unions of non-overlapping interval vectors, as in [10] and [9]. The ImageSp algorithm can then be employed to evaluate an outer-approximating subpaving of (7) or (8). The SIVIA algorithm can provide an outer-approximation of the sets defined by (10) and (11).…”

“…In all of the aforementioned approaches, a stochastic framework is used to describe the measurement errors and model uncertainties. As pointed out in [8], the resulting performance may prove sensitive to the a priori assumptions on the probability density functions (pdfs) describing the process and measurement noises. As the definition of suitable pdfs might prove tedious, a set-membership descriptions of uncertainties is suggested in [8].…”

“…As pointed out in [8], the resulting performance may prove sensitive to the a priori assumptions on the probability density functions (pdfs) describing the process and measurement noises. As the definition of suitable pdfs might prove tedious, a set-membership descriptions of uncertainties is suggested in [8]. The only assumption made on the noises and uncertainties is that their realizations remain within known bounds.…”

Data fusion of multiple kinect sensors for a rehabilitation system

“…Set-Membership-Filter (SMF) method [10] is used for the purpose of data fusion. With difference from other methods based on probabilities such as Kalman-Filter (KF), SMF method do not need any prior knowledge of the distribution of sensor's noise.…”