Bearing only cooperative localization has been used successfully on aerial and ground vehicles. In this paper we present an extension of the approach to the underwater domain. The focus is on adapting the technique to handle the challenging visibility conditions underwater. Furthermore, data from inertial, magnetic, and depth sensors are utilized to improve the robustness of the estimation. In addition to robotic applications, the presented technique can be used for cave mapping and for marine archeology surveying, both by human divers. Experimental results from different environments, including a fresh water, low visibility, lake in South Carolina; a cavern in Florida; and coral reefs in Barbados during the day and during the night, validate the robustness and the accuracy of the proposed approach.
The concept of CL was first introduced by Kurazume and Hirosi @cite_26 , and the term was first used by Rekleitis al @cite_21 . At a recent count, more than 100 papers have been published since then covering many aspects. The CL approach has been used both in 2D and 3D, for localization @cite_24 and also for SLAM @cite_4 . Using images @cite_1 @cite_6 , LIDAR @cite_36 @cite_35 , and sonar @cite_15 . The problem of CL with anonymous robots'' was presented by Franchi al @cite_18 . More recently, Martinelli and Renzaglia @cite_10 provided the fundamental equations for fusing CL estimates with inertial data.
@cite_35 @cite_18 @cite_26 @cite_4 @cite_36 @cite_21 @cite_1 @cite_6 @cite_24 @cite_15 @cite_10
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In this paper we present a probabilistic framework for the reduction in the uncertainty of a moving robot pose during exploration by using a second robot to assist. A Monte Carlo Simulation technique (specifically, a Particle Filter) is employed in order to model and reduce the accumulated odometric error. Furthermore, we study the requirements to obtain an accurate yet timely pose estimate. A team of two robots is employed to explore an indoor environment in this paper, although several aspects of the approach have been extended to larger groups. The concept behind our exploration strategy has been presented previously and is based on having one robot carry a sensor that acts as a "robot tracker" to estimate the position of the other robot. By suitable use of the tracker as an appropriate motion-control mechanism we can sweep areas of free space between the stationary and the moving robot and generate an accurate graph-based description of the environment. This graph is used to guide the exploration process. Complete exploration without any overlaps is guaranteed as a result of the guidance provided by the dual graph of the spatial decomposition (triangulation) of the environment. We present experimental results from indoor experiments in our laboratory and from more complex simulated experiments. Recent research on multi-agent systems has produced a plethora of decentralized controllers that implicitly assume various degrees of agent localization. However, many practical arrangements commonly taken to allow and achieve localization imply some form of centralization, from the use of physical tagging to allow the identification of the single agent to the adoption of global positioning systems based on cameras or GPS. These devices clearly decrease the system autonomy and range of applicability, and should be avoided if possible. Following this guideline, this work addresses the mutual localization problem with anonymous relative position measures, presenting a robust solution based on a probabilistic framework. The proposed localization system exhibits higher accuracy and lower complexity (O(n2)) than our previous method [1]. Moreover, with respect to more conventional solutions that could be conceived on the basis of the current literature, our method is theoretically suitable for tasks requiring frequent, many-to-many encounters among agents (e.g., formation control, cooperative exploration, multiple-view environment sensing). The proposed localization system has been validated by means of an extensive experimental study. A number of positioning identification techniques have been used for mobile robots. Dead reckoning is a popular method, but is not reliable when a robot travels long distances or over an uneven surface because of variations in wheel diameter and wheel slippage. The landmark method, which estimates the current position relative to landmarks, cannot be used in an uncharted environment. The authors propose a new method called "cooperative positioning with multiple robots". For cooperative positioning, the authors divide the robots into two groups, A and B. One group, say A, remains stationary and acts as a landmark while group B moves. The moving group B then stops and acts as a landmark for group A. This "dance" is repeated until the target robot position are reached. Cooperative positioning has a far lower accumulated positioning error than dead reckoning, and can work in three-dimensions which is not possible with dead reckoning. Also, this method has inherent landmarks and therefore works in uncharted environments. This paper discusses the positioning accuracy of the authors' method with error variances for an example with three mobile robots. > This paper presents a new sensing modality and stratagem for multirobot exploration. The approach is based on using pairs of robots that observe each other's behavior, acting in concert to reduce odometry errors. We assume the robots can both directly sense nearby obstacles and see each other. This allows the robots to obtain a map of higher accuracy than would be possible with robots acting independently by reducing inaccuracies that occur over time from dead reckoning errors. Furthermore, by exploiting the ability of the robots to see each other, we can detect opaque obstacles in the environment independently of their surface reflectance properties. Two different algorithms, based on the size of the environment, are introduced with a complexity analysis, and experimental results in simulation and with real robots. This paper describes a method for localizing the members of a mobile robot team, using only the robots themselves as landmarks, that is, we describe a method whereby each robot can determine the relative range, bearing and orientation of every other robot in the team, without the use of GPS, external landmarks, or instrumentation of the environment. Our method assumes that each robot is able to measure the relative pose of nearby robots, together with changes in its own pose. Using a combination of maximum likelihood estimation and numerical optimization, we can subsequently infer the relative pose of every robot in the team. This paper describes the basic formalism, its practical implementation, and presents experimental results obtained using a team of four mobile robots. This paper describes a technique for multi-agent exploration of an unknown environment, that improves the quality of the map by reducing the inaccuracies that occur over time from dead reckoning errors. We present an algorithmic solution, simulation results, as well as a cost analysis and experimental data. The approach is based on using a pair of robots that observe one another’s behaviour, thus greatly reducing odometry errors. We assume the robots can both directly sense nearby obstacles and see one another. We have implemented both these capabilities with actual robots in our lab. By exploiting the ability of the robots to see one another, we can detect opaque obstacles in the environment independent of their surface reflectance properties. 1 A key difficulty in the design of multi-agent robotic systems is the size and complexity of the space of possible designs. In order to make principled design decisions, an understanding of the many possible system configurations is essential. To this end, we present a taxonomy that classifies multi-agent systems according to communication, computational and other capabilities. We survey existing efforts involving multi-agent systems according to their positions in the taxonomy. We also present additional results concerning multi-agent systems, with the dual purposes of illustrating the usefulness of the taxonomy in simplifying discourse about robot collective properties, and also demonstrating that a collective can be demonstrably more powerful than a single unit of the collective.