Opportunistic Localization Scheme Based on Linear Matrix Inequality

Enabling self-localization of mobile nodes is an important problem that has been widely studied in the literature. The general conclusions is that an accurate localization requires either sophisticated hardware (GPS, UWB, ultrasounds transceiver) or a dedicated infrastructure (GSM, WLAN). In this paper we tackle the problem from a different and rather new perspective: we investigate how localization performance can be improved by means of a cooperative and opportunistic data exchange among the nodes. We consider a target node, completely unaware of its own position, and a number of mobile nodes with some self-localization capabilities. When the opportunity occurs, the target node can exchange data with in-range mobile nodes. This opportunistic data exchange is then used by the target node to refine its position estimate by using a technique based on Linear Matrix Inequalities and barycentric algorithm. To investigate the performance of such an opportunistic localization algorithm, we define a simple mathematical model that describes the opportunistic interactions and, then, we run several computer simulations for analyzing the effect of the nodes duty-cycle and of the native self-localization error modeling considered. The results show that the opportunistic interactions can actually improve the self-localization accuracy of a strayed node in many different scenarios

A robust extended H-infinity filtering approach to multi-robot cooperative localization in dynamic indoor environments

Multi-robot cooperative localization serves as an essential task for a team of mobile robots to work within an unknown environment. Based on the real-time laser scanning data interaction, a robust approach is proposed to obtain optimal multi-robot relative observations using the Metric-based Iterative Closest Point (MbICP) algorithm, which makes it possible to utilize the surrounding environment information directly instead of placing a localization-mark on the robots. To meet the demand of dealing with the inherent non-linearities existing in the multi-robot kinematic models and the relative observations, a robust extended H∞ filtering (REHF) approach is developed for the multi-robot cooperative localization system, which could handle non-Gaussian process and measurement noises with respect to robot navigation in unknown dynamic scenes. Compared with the conventional multi-robot localization system using extended Kalman filtering (EKF) approach, the proposed filtering algorithm is capable of providing superior performance in a dynamic indoor environment with outlier disturbances. Both numerical experiments and experiments conducted for the Pioneer3-DX robots show that the proposed localization scheme is effective in improving both the accuracy and reliability of the performance within a complex environment.This work was supported inpart by the National Natural Science Foundation of China under grants 61075094, 61035005 and 61134009

Cooperative Control for Target Tracking with Onboard Sensing

Abstract We consider the cooperative control of a team of robots to estimate the position of a moving target using onboard sensing. In particular, we do not as-sume that the robot positions are known, but estimate their positions using relative onboard sensing. Our probabilistic localization and control method takes into ac-count the motion and sensing capabilities of the individual robots to minimize the expected future uncertainty of the target position. It reasons about multiple possi-ble sensing topologies and incorporates an efficient topology switching technique to generate locally optimal controls in polynomial time complexity. Simulations show the performance of our approach and prove its flexibility to find suitable sensing topologies depending on the limited sensing capabilities of the robots and the movements of the target. Furthermore, we demonstrate the applicability of our method in various experiments with single and multiple quadrotor robots tracking a ground vehicle in an indoor environment

Decentralized cooperative trajectory estimation for autonomous underwater vehicles

Autonomous agents that can communicate and make relative measurements of each other can improve their collective localization accuracies. This is referred to as cooperative localization (CL). Autonomous underwater vehicle (AUV) CL is constrained by the low throughput, high latency, and unreliability of of the acoustic channel used to communicate when submerged. Here we propose a CL algorithm specifically designed for full trajectory, or maximum a posteriori, estimation for AUVs. The method is exact and has the advantage that the broadcast packet sizes increase only linearly with the number of AUVs in the collective and do not grow at all in the case of packet loss. The approach allows for AUV missions to be achieved more efficiently since: 1) vehicles waste less time surfacing for GPS fixes, and 2) payload data is more accurately localized through the smoothing approach.Natural Sciences and Engineering Research Council of CanadaDefense Research and Development CanadaUnited States. Office of Naval Research (Grant N00014-13-1-0588

Cooperative localization for mobile agents: a recursive decentralized algorithm based on Kalman filter decoupling

We consider cooperative localization technique for mobile agents with communication and computation capabilities. We start by provide and overview of different decentralization strategies in the literature, with special focus on how these algorithms maintain an account of intrinsic correlations between state estimate of team members. Then, we present a novel decentralized cooperative localization algorithm that is a decentralized implementation of a centralized Extended Kalman Filter for cooperative localization. In this algorithm, instead of propagating cross-covariance terms, each agent propagates new intermediate local variables that can be used in an update stage to create the required propagated cross-covariance terms. Whenever there is a relative measurement in the network, the algorithm declares the agent making this measurement as the interim master. By acquiring information from the interim landmark, the agent the relative measurement is taken from, the interim master can calculate and broadcast a set of intermediate variables which each robot can then use to update its estimates to match that of a centralized Extended Kalman Filter for cooperative localization. Once an update is done, no further communication is needed until the next relative measurement

Multi-Robot Localization Using Relative Observations

In this paper we consider the problem of simultaneously localizing all members of a team of robots. Each robot is equipped with proprioceptive sensors and exteroceptive sensors. The latter provide relative observations between the robots. Proprioceptive and exteroceptive data are fused with an Extended Kalman Filter. We derive the equations for this estimator for the most general relative observation between two robots. Then we consider three special cases of relative observations and we present the structure of the filter for each case. Finally, we study the performance of the approach through many accurate simulations

METODE LOKALISASI ROBOT OTONOM DENGAN MENGGUNAKAN ADOPSI ALGORITMA HEURISTIC SEARCHING DAN PRUNING UNTUK PEMBANGUNAN PETA PADA KASUS SEARCH-AND-SAFE

Permasalahan search-and-safe merupakan salah satu contoh robot otonom dapat disimulasikan untuk menggantikan pekerjaan manusia di lingkungan berbahaya, misalnya pada kegiatan evakuasi manusia dari ruang tertutup yang terbakar. Dalam contoh ini, robot otonom harus dapat menemukan objek manusia untuk diselamatkan, serta objek api untuk dipadamkan. Lebih jauh lagi, untuk dapat menyelesaikan permasalahan seperti ini dengan baik, robot otonom harus dapat mengetahui keberadaannya, bukan hanya posisi dalam sistem koordinat global saja tetapi juga posisi relatif terhadap posisi tujuan dan keadaan lingkungan itu sendiri. Permasalahan ini kemudian dikenal juga sebagai lokalisasi yang menjadi bagian penting dari proses navigasi pada robot otonom. Salah satu metode yang dapat digunakan untuk menyelesaikan permasalahan lokalisasi adalah dengan menggunakan representasi internal peta lingkungan kerja dalam pengetahuan robot otonom. Pada kondisi ketika tidak tersedia informasi mengenai konfigurasi lingkungan, atau informasi yang tersedia sifatnya terbatas, robot harus dapat membangun sendiri representasi petanya dengan dibantu oleh komponen sensor yang dimilikinya. Pada paper ini kemudian dibahas salah satu metode yang dapat diterapkan dalam proses pembangunan peta seperti yang dijelaskan, yaitu melalui adopsi algoritma heuristic searching dan pruning yang sudah dikenal pada bidang kecerdasan buatan. Selain itu juga akan dijabarkan desain robot otonom yang digunakan, serta konfigurasi lingkungan yang digunakan pada studi kasus search-and-safe ini. Diharapkan nantinya hasil yang diperoleh dari penelitian ini dapat diterapkan untuk skala yang lebih besar

Improving the Precisionon Multi Robot Localization by Using a Series of Filters Hierarchically Distributed

International audienceThis paper introduces a new approach to the problem of simultaneously localizing a team of mobile robots equipped with proprioceptive sensors able to monitor their motion and with exteroceptive sensors able of sensing one another. The method is based on a series of extended Kalman filters hierarchically distributed. In particular, the team is decomposed in several groups and, for each group, an extended Kalman filter estimates the configurations of all the members of the group in a local frame attached to one robot, the group leader. Finally, at the highest level of the hierarchy, one single filter estimates the locations of all the group leaders. The key advantage of this approach is its ability to distribute the computation necessary to perform the multi robot localization under limited computation and communication capabilities. In particular, the approach significantly outperforms an optimal approach based on a single estimator. This is shown by analytically computing the precision on the localization of each robot in the case of one single degree of freedom. In particular, the best hierarchy is analytically determined by deriving the dependency of the localization precision on the communication and computation capabilities and on the sensors accuracy