Bearing-only Cooperative Localization

Sharma, Rajnikant; Quebe, Stephen; Beard, Randal W.; Taylor, Clark N.

doi:10.1007/s10846-012-9809-z

Cited by 40 publications

(21 citation statements)

References 19 publications

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“…Different inter-robot-relative-measurement approaches have been investigated for multirobot cooperative localization: relative range-only [35], [36], relative bearing-only [37], [38], both relative range and relative bearing [6], [29], and full relative pose measurement [4], [5], [17], [22], [23], [27]. Work presented in [39] evaluated the effect of various relative measurement approaches on the accuracy of the cooperative localization.…”

Section: Introductionmentioning

confidence: 99%

Distributed Leader-Assistive Localization Method for a Heterogeneous Multirobotic System

Wanasinghe

Mann

Gosine

2015

IEEE Trans. Automat. Sci. Eng.

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This paper presents a distributed leader-assistive localization approach for a heterogeneous multirobotic system (MRS). The localization algorithm is formulated to estimate the position and orientation (pose) of a group of robots in a given reference coordinate frame (or global coordinate frame). It is assumed that the heterogeneous-MRS has one or a group of robots (which we refer as leader robots) with higher sensor payload, higher processing power, and larger memory capacity, enabling accurate self-localization capabilities. Robots with limited resources (which we refer as child robots) rely on leader robots, and inter-robot observations between leaders and themselves for localization. Finite-range sensing is a key challenge for such leader-assistive localization. This study presents a sensor sharing technique which virtually enhances the sensing range of leader robots. In the proposed method, each robot locally runs a cubature Kalman filter to estimate its own pose and hosts a low cost, lightweight, and low-power sensory system to periodically measure relative pose of neighbors. Each robot transmits these relative pose measurements to leader robots. Leader robots then combine available relative observations in order to synthesise global pose measurements and associated noise covariances for child robots. Child robots are acknowledged by the leader robots with the synthesized global pose measurements and fuse these measurements with their local belief in order to improve their localization. Theoretical developments are presented to virtually enhance the leader robots' sensing range. The performance of the proposed distributed leader-assistive localization algorithm is evaluated on a multirobot simulation test-bed and on a publicly available multirobot localization and mapping data-set. The results illustrate that the proposed algorithm is capable of establishing accurate and consistent localization for the child robots even when they operate beyond the sensing range of the leader robots.Note to Practitioners-MRS can be used to perform environmental monitoring, exploration tasks, and search and rescue missions. Accurate localization is a critical factor that governs the success of the autonomous mobile robots-based missions. In order to improve the localization accuracy, robots can be equipped with advanced sensory systems which will increase the cost. Additionally, robots can execute advanced localization algorithms to generate an accurate localization which entails higher processing capabili-

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Section: Introductionmentioning

confidence: 99%

Distributed Leader-Assistive Localization Method for a Heterogeneous Multirobotic System

Wanasinghe

Mann

Gosine

2015

IEEE Trans. Automat. Sci. Eng.

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“…In this article, the Extended Information Filter (EIF), which is the information form of the Extended Kalman Filter (EKF), is used to estimate the states of the system x, as the vehicles travel along their assigned paths. In our previous work, 10 we have shown that using the Hermann-Krener criterion for local observability, if each edge traversed by the vehicle is covered by at least two installed LMs (i.e. two LMs within the sensing range of the vehicle), the system will be observable.…”

Section: Solution Methodologymentioning

confidence: 99%

Routing Multiple Unmanned Vehicles in GPS Denied Environments

Wang

Misra

Sundar

et al. 2018

2018 AIAA Information Systems-Aiaa Infotech @ Aerospace

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This article aims to develop novel path planning algorithms required to deploy multiple unmanned vehicles in Global Positioning System (GPS) denied environments. Unmanned vehicles (ground or aerial) are ideal platforms for executing monitoring and data gathering tasks in civil infrastructure management, agriculture, public safety, law enforcement, disaster relief and transportation. Significant advancement in the area of path planning for unmanned vehicles over the last decade has resulted in a suite of algorithms that can handle heterogeneity, motion and other on-board resource constraints for these vehicles. However, most of these routing and path planning algorithms rely on the availability of the GPS information. Unintentional and intentional interference and design errors can cause GPS service outages, which in turn, can crucially affect all the systems that depend on GPS information. This article addresses a multiple vehicle path planning problem that arises while deploying a team of unmanned vehicles for monitoring applications in GPS-denied environments and presents a mathematical formulation and algorithms for solving the problem. Simulation results are also presented to corroborate the performance of the proposed algorithms. * PhD Student,

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“…where z i represents the fused estimate of the relative coordinate of the source in the local frame Σ i , andp i0 andp j i0 are the direct and indirect estimate of the source obtained in (9) and (17), respectively. Then we have the following result.…”

Section: Fusion Schemementioning

confidence: 99%

Bearing angle based cooperative source localization

Lin

Chai

Lin

et al. 2014

53rd IEEE Conference on Decision and Control

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Abstract-This paper deals with the cooperative source localization problem with the goal of having an accurate estimate of the coordinate of the source cooperatively by a group of unicycle-type mobile agents. Neither absolute positioning information nor a common sense of direction is shared by the agents. Each agent gets its estimate about the source's coordinate in its own local frame based on the bearing measurements about its neighbors (that may or may not include the source) together with its own linear and angular speed information. A continuous time estimation scheme and a distributed fusion scheme are proposed for this goal such that the source's relative coordinate can be estimated at any time by each agent no matter whether it can directly detect the source or not. The globally asymptotic convergence of the estimation scheme and the fusion scheme is rigorously analyzed. Simulation results are also provided to verify the effectiveness of the proposed algorithms.

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Bearing-only Cooperative Localization

Cited by 40 publications

References 19 publications

Distributed Leader-Assistive Localization Method for a Heterogeneous Multirobotic System

Distributed Leader-Assistive Localization Method for a Heterogeneous Multirobotic System

Routing Multiple Unmanned Vehicles in GPS Denied Environments

Bearing angle based cooperative source localization

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