A consensus algorithm for common reference frame estimation in networked multi-agent systems

Lopez-Limon, C.; Franceschelli, Mauro; Seatzu, Carla; Ramírez-Treviño, A.

doi:10.1109/cdc.2014.7040347

Cited by 3 publications

(7 citation statements)

References 15 publications

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“…Consequently, the solution T of the optimization problem (22) coincides to the transformation matrix T −1 c T i . We consider the case for d = 2 and T in (10) to illustrate the way to solve (24) and (25). Then, explicit solutions can be derived as follows.…”

Section: Resultsmentioning

confidence: 99%

“…Then, explicit solutions can be derived as follows. First, (25) is associated with the pattern matching technique of image data [16] because ⟨T ⟩ = SE(d) for T in (10) holds, and an explicit solution can be obtained. Second, (24) is reduced to min where R : R → SO( 2) is defined as…”

Section: Resultsmentioning

confidence: 99%

“…For example, it was reported in [15] that the self-localization scheme using Hokuyo UST-20LX in a corridor environment caused a position error of up to 0.15 m and an attitude error of up to 1 deg. Hence, we set T in (10) with r b = 0.15 m and r θ = 1 deg in this case.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Paper [9] has proposed a distributed algorithm for static sensor networks in two-dimensional space, based on a gossip communication scheme, i.e., asynchronous and random pairwise communication. In [10], a consensus-based algorithm for multi-robot systems in two-dimensional space has been proposed. This method uses a consensus protocol of the estimations of a common reference frame by communication, exploiting measured relative positions.…”

Section: Introductionmentioning

confidence: 99%

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Cooperative reference frame estimation for multi-agent systems via formation control

Asai

Sakurama

2022

Advanced Robotics

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In this study, we propose a distributed method for multi-agent systems to estimate a global reference frame by constructing a common reference frame for all agents with only local measurements. Our method is a combination of formation control and similarity evaluation of feature point matching. Here, we design a distributed controller to achieve a prescribed configuration and a distributed estimator to construct a common reference frame, which calculates the similarity between the measured relative positions and the prescribed configuration. Then, we quantify the error range between the common reference frame and the global reference frame and show that the constructed common reference frame is the best. The proposed method requires only the information on the relative positions of neighbor robots through local measurements and does not require inter-robot communication or relative posture observation. Finally, we conducted simulations with 12 agents and an experiment with 8 two-wheeled robots to verify the effectiveness of the proposed method.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cooperative reference frame estimation for multi-agent systems via formation control

Asai

Sakurama

2022

Advanced Robotics

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“…Motion planning algorithms typically require a local reference frame with respect to which the swarm poses are defined. Some multi-agent robotics literature discusses the estimation of a common reference frame (Nagavalli et al, 2014;Franceschelli and Gasparri, 2013;López-Limón et al, 2014). For formation flying spacecraft applications, the goal of the common reference frame estimation is to obtain knowledge of the absolute orbital state.…”

Section: Related Workmentioning

confidence: 99%

Decentralized formation pose estimation for spacecraft swarms

Matsuka

Feldman

Lupu

et al. 2021

Advances in Space Research

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For spacecraft swarms, the multi-agent localization algorithm must scale well with the number of spacecraft and adapt to time-varying communication and relative sensing networks. In this paper, we present a decentralized, scalable algorithm for swarm localization, called the Decentralized Pose Estimation (DPE) algorithm. The DPE considers both communication and relative sensing graphs and defines an observable local formation. Each spacecraft jointly localizes its local subset of spacecraft using direct and communicated measurements. Since the algorithm is local, the algorithm complexity does not grow with the number of spacecraft in the swarm. As part of the DPE, we present the Swarm Reference Frame Estimation (SRFE) algorithm, a distributed consensus algorithm to coestimate a common Local-Vertical, Local-Horizontal (LVLH) frame. The DPE combined with the SRFE provides a scalable, fully-decentralized navigation solution that can be used for swarm control and motion planning. Numerical simulations and experiments using Caltech's robotic spacecraft simulators are presented to validate the effectiveness and scalability of the DPE algorithm.

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