Relative Localization Approach for Combined Aerial and Ground Robotic System

Wanasinghe, Thumeera R.; Mann, George K. I.; Gosine, Raymond G.

doi:10.1007/s10846-014-0094-x

Cited by 11 publications

(9 citation statements)

References 37 publications

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“…A general 3-DOF discrete-time kinematic model is assumed for the robots (1) where is the robot's pose at discrete time and represents the nonlinear state propagation function. is the sampling time interval.…”

Section: A Robots' Motion Modelmentioning

confidence: 99%

“…Robustness to individual failures, shorter mission completion time, improved productivity through parallel tasking, better area coverage, and collaborative perception are some of the advancements that a MRS can offer [1]. Moreover, heterogeneous MRS with both aerial and ground robots can enhance the visual perception of teammates while increasing accessibility in cluttered environments [2].…”

Section: Introductionmentioning

confidence: 99%

“…Child robot's pose can be estimated either on a given coordinate frame (or global coordinate frame) [8] or on the body-fixed coordinate system of leader robots [1], [29], [30]. The former can either be performed in a distributed manner or centralized manner.…”

Section: Introductionmentioning

confidence: 99%

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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: A Robots' Motion Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Distributed Leader-Assistive Localization Method for a Heterogeneous Multirobotic System

Wanasinghe

Mann

Gosine

2015

IEEE Trans. Automat. Sci. Eng.

Self Cite

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“…Other than the exact model, a 6-DOF motion model is proposed for governing a quadrotor in a distributed setup 7 . Later, it was reduced to 4-DOF motion model 8 .…”

Section: Map Building and Motion Model Selectionmentioning

confidence: 99%

Trajectory Tracking For Quadrotors: An Optimization-Based Planning Followed By Controlling Approach

Kulathunga

Devitt

Klimchik

2021

Preprint

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We present an optimization-based reference trajectory tracking method for quadrotor robots for slow-speed maneuvers. The proposed method uses planning followed by the controlling paradigm. The basic concept of the proposed method is an analogy to Linear Quadratic Gaussian (LQG) in which Nonlinear Model Predictive Control (NMPC) is employed for predicting optimal control policy in each iteration. Multiple-shooting (MS) is suggested over Direct-collocation (DC) for imposing constraints when modelling the NMPC. Incremental Euclidean Distance Transformation Map (EDTM) is constructed for obtaining the closest free distances relative to the predicted trajectory; these distances are considered obstacle constraints. The reference trajectory is generated, ensuring dynamic feasibility. The objective is to minimize the error between the quadrotor’s current pose and the desired reference trajectory pose in each iteration. Finally, we evaluated the proposed method with two other approaches and showed that our proposal is better than those two in terms of reaching the goal without any collision. Additionally, we published a new dataset, which can be used for evaluating the performance of trajectory tracking algorithms.

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“…Although this kind of localization scheme is simple to achieve, the localization accuracy is limited by the insufficiency of information sharing [5,6]. The relative range or relative bearing between different mobile robots, observed by binocular cameras, monocular cameras or other kinds of sensors, is exchanged among the mobile robot group [1,7]. In addition, the performance of this kind of navigation scheme, which is called cooperative navigation, is superior to the one of the separate navigation scheme [8,9].…”

Section: Introductionmentioning

confidence: 99%

Cooperative Localization Algorithm for Multiple Mobile Robot System in Indoor Environment Based on Variance Component Estimation

Sun

Diao²,

Zhang³

et al. 2017

Symmetry

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Abstract:The Multiple Mobile Robot (MMR) cooperative system is becoming a focus of study in various fields due to its advantages, such as high efficiency and good fault tolerance. However, the uncertainty and nonlinearity problems severely limit the cooperative localization accuracy of the MMR system. Thus, to solve the problems mentioned above, this manuscript presents a cooperative localization algorithm for MMR systems based on Cubature Kalman Filter (CKF) and adaptive Variance Component Estimation (VCE) methods. In this novel algorithm, a nonlinear filter named CKF is used to enhance the cooperative localization accuracy and reduce the computational load. On the other hand, the adaptive VCE method is introduced to eliminate the effects of unknown system noise. Furthermore, the performance of the proposed algorithm is compared with that of the cooperative localization algorithm based on normal CKF by utilizing the real experiment data. In addition, the results demonstrate that the proposed algorithm outperforms the CKF cooperative localization algorithm both in accuracy and consistency.

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Relative Localization Approach for Combined Aerial and Ground Robotic System

Cited by 11 publications

References 37 publications

Distributed Leader-Assistive Localization Method for a Heterogeneous Multirobotic System

Distributed Leader-Assistive Localization Method for a Heterogeneous Multirobotic System

Trajectory Tracking For Quadrotors: An Optimization-Based Planning Followed By Controlling Approach

Cooperative Localization Algorithm for Multiple Mobile Robot System in Indoor Environment Based on Variance Component Estimation

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