[논문]Multi-Robot Localization based on Distance Mapping

Je, Hong-Mo; Kim, Jung-Tae; Kim, Dai-Jin

[국내논문] Multi-Robot Localization based on Distance Mapping
거리매칭에 기반한 다수로봇 위치추정 원문보기

Je, Hong-Mo (POSTECH) , Kim, Jung-Tae (POSTECH) , Kim, Dai-Jin (POSTECH)

This paper presents a distance mapping-based localization method with incomplete data which means partially observed data. We make three contributions. First, we propose the use of Multi Dimensional Scaling (MDS) for multi-robot localization. Second, we formulate the problem to accomodate partial observations common in multi-robot settings. We solve the resulting optimization problem using #Scaling by Majorizing a Complicated function (SMACOF)#, a popular algorithm fur iterative MDS. Third, we not only verify the performance of MDS-based multi-robot localization by computer simulations, but also implement a real world localization of multi-robot team. Using extensive empirical results, we show that the accuracy of the proposed method is almost similar to that of Monte Carlo Localization(MCL).

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문제 정의

In general we are trying to find relations and distinctions between probabilistic approaches and distance mapping-based localization with motion information. Furthermore, we are interested in a theoretical analysis of error bounds and uncertainty of this approach.

가설 설정

These can be classified according to whether the data are qualitative or quantitative, the number of similarity matrices, the nature of the MDS model, and the implementation of the algorithm to solve the MDS problem. In this paper, we assum that MDS-based multi-robot localization is a classical-metric-unweighted MDS problem because a distance on Euclidean space must be quantitative, may be an unweighted matrix, and can be treated with only one similarity matrix. As one of the distance mapping, MDS finds a embedding function which minimize a 'stress'.

제안 방법

team. In this paper we propose a distance mapping approach to multi-robot localization with partially observed inter-robot distances as the relative observations. We apply Multidimensional scaling (MDS) to this problem.
In addition we utilize the motion information of robots to improve the performance on both convergence and stability by predicting likely positions of robots when direct inter-robot range estimates are unavailable. At each iteration of our algorithm if range measurements are unavailable, we experiment with three choices to estimate the missing information - random, where no information is passed from one step to the next, previous, where at each iteration a missing range value is substituted by the value at the previous iteration, and prediction, where the value at the previous iteration is combined with the current motion information to produce an estimate of the current missing value. We performed extensive simulations to verify which choice is good.
Line search and trust-region methods can find a local optimum, whereas a genetic algorithm, tabu search, and simulated annealing can achieve global optimum. This paper considers o미y line search methods for optimization because of both efficiency and speed of convergence. The procedure of generic iterative MDS is summarized as follows: k = 0
To check the accuracy of the proposed distance mapping with the three initialization strategies, we evaluated the mean of the relative errors between the real position and the computed position in 2D coordinate space. The error is defined in Eq.

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[국내논문] Multi-Robot Localization based on Distance Mapping
거리매칭에 기반한 다수로봇 위치추정 원문보기

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[국내논문] Multi-Robot Localization based on Distance Mapping 거리매칭에 기반한 다수로봇 위치추정 원문보기

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[국내논문] Multi-Robot Localization based on Distance Mapping
거리매칭에 기반한 다수로봇 위치추정 원문보기

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