This work presents a novel approach to efficient multirobot mapping and exploration which exploits a market architecture in order to maximize information gain while minimizing incurred costs. This system is reliable and robust in that it can accommodate dynamic introduction and loss of team members in addition to being able to withstand communication interruptions and failures. Results showing the capabilities of our system on a team of exploring autonomous robots are given.
When robots work together as a team, the members that perform each task should be the ones that promise to use the least resources to do the job.
When robots work together as a team, the members that perform each task should be the ones that promise to use the least resources to do the job. ABSTRACT | Market-based multirobot coordination approaches have received significant attention and are growing in popularity within the robotics research community. They have been successfully implemented in a variety of domains ranging from mapping and exploration to robot soccer. The research literature on market-based approaches to coordination has now reached a critical mass that warrants a survey and analysis. This paper addresses this need for a survey of the relevant literature by providing an introduction to marketbased multirobot coordination, a review and analysis of the state of the art in the field, and a discussion of remaining research challenges.
Current technological developments and application-driven demands are bringing us closer to the realization of autonomous multirobot systems performing increasingly complex missions. However, existing methods of distributing mission subcomponents among multirobot teams do not explicitly handle the required complexity and instead treat tasks as simple indivisible entities, ignoring any inherent structure and semantics that such complex tasks might have. These task properties can be exploited to produce more efficient team plans by giving individual robots the ability to come up with new, more localized ways to perform a task; by allowing multiple robots to cooperate by sharing the subcomponents of a task; or both. In this paper, we describe the complex task allocation problem and present a distributed solution for efficiently allocating a set of complex tasks among a robot team.Complex tasks are tasks that can be solved in many possible ways. In contrast, simple tasks can be accomplished in a straightforward, prescriptive manner. The current scope of our work is currently limited to complex tasks that can be decomposed into multiple subtasks related by Boolean logic operators. Our solution to multirobot coordination for complex tasks extends market-based approaches by generalizing task descriptions into task trees, which allows tasks to be traded in a market setting at variable levels of abstraction. In order to incorporate these task structures into a market mechanism, novel and efficient bidding and auction clearing algorithms are required. As an example scenario, we focus on an area reconnaissance problem which requires sensor coverage by a team of robots over a set of defined areas of interest. The advantages of explicitly modeling complex tasks during the allocation process is demonstrated by a comparison of our approach with existing task allocation algorithms in this application do- * This paper has been accepted for publication in the International Journal of Robotics Research and the final (edited, revised and typeset) version of this paper will be published in the main. In simulation we compare the quality of solution and the computation times of these different approaches. Implementations on two separate teams of indoor and outdoor robots further validates our approach.
Reliable data association techniques for simultaneous localization and mapping (SLAM) are necessary for the generation of large-scale maps in unstructured outdoor environments. Data association techniques are required at two levels: the local level represents the inner loop of the mapping algorithm, and the global level where newly mapped areas are matched to previously mapped areas to detect repeated coverage and close loops. Local map building is achieved using a robust iterative scan matching technique incorporated into an extended Kalman filter where the state consists of the current pose and previous poses sampled periodically and at a fixed lag from the current time. The introduction of states at a fixed time lag significantly reduces the growth of errors in the location estimate and the resultant map. For global matching, we enhance existing histogram cross-correlation techniques, introducing entropy sequences of projection histograms and an exhaustive correlation approach for reliable matching in unstructured environments. This enables loop closure without depending on prior knowledge of map alignment. These data association techniques are incorporated into the Atlas SLAM framework, enabling the generation of accurate twodimensional laser maps over tens of kilometers in challenging outdoor environments.
In developing autonomous solutions for mapping and localization, one problem that often needs to be dealt with is determining when an area is revisited despite having poor or no prior information on the relative alignment error. There are well-formulated approaches for recognizing such matches using the rich information in camera data; however, it is a much more challenging problem using lidar sensors alone. Most existing approaches employ a pairwise place comparison of place descriptors and thus finding matches requires linear time per place. We instead propose the use of a keypoint voting approach to achieve sub-linear matching times. A constant number of nearest neighbor votes per keypoint are queried from a database of local descriptors and aggregated to determine likely place matches. It becomes critical to analyze the distributions of vote scores such that a suitable threshold for matching scores can be determined a priori, so that the system is not overwhelmed by false positives nor starved for true matches. We have empirically determined that the vote scores follow a log-normal distribution, and we are able to fit a parametric model of its hyper-parameters based on the number of neighbors, the number of keypoints in a place, and the total number of keypoints in the database. We demonstrate the performance of our system in a variety of large scale 3D lidar datasets using data collected from a continually scanning handheld lidar sensor, and also on two publicly available lidar datasets.
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