Localizing a Robot with Minimum Travel
SIAM Journal on Computing
Navigating Mobile Robots: Systems and Techniques
Navigating Mobile Robots: Systems and Techniques
Multi-robot collaboration for robust exploration
Annals of Mathematics and Artificial Intelligence
Monte Carlo Localization with Mixture Proposal Distribution
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
A frontier-based approach for autonomous exploration
CIRA '97 Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation
Planning and Acting in Partially Observable Stochastic Domains
Planning and Acting in Partially Observable Stochastic Domains
Coordination in ambiguity: coordinated active localization for multiple robots
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems: demo papers
Multi-robot exploration of an unknown environment, efficiently reducing the odometry error
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Probabilistic robot navigation in partially observable environments
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Estimating the absolute position of a mobile robot using position probability grids
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
The revisiting problem in mobile robot map building: a hierarchical bayesian approach
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Performance analysis of multirobot Cooperative localization
IEEE Transactions on Robotics
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The task of the robot in localization is to find out where it is, through sensing and motion. In environments which possess relatively few features that enable a robot to unambiguously determine its location, global localization algorithms can result in 'multiple hypotheses' locations of a robot. This is inevitable with global localization algorithms, as the local environment seen by a robot repeats at other parts of the map. Thus, for effective localization, the robot has to be actively guided to those locations where there is a maximum chance of eliminating most of the ambiguous states - which is often referred to as 'active localization'. When extended to multi-robotic scenarios where all robots possess more than one hypothesis of their position, there is an opportunity to do better by using robots, apart from obstacles, as 'hypotheses resolving agents'. The paper presents a unified framework which accounts for the map structure as well as measurement amongst robots, while guiding a set of robots to locations where they can singularize to a unique state. The strategy shepherds the robots to places where the probability of obtaining a unique hypothesis for a set of multiple robots is a maximum. Another aspect of framework demonstrates the idea of dispatching localized robots to locations where they can assist a maximum of the remaining unlocalized robots to overcome their ambiguity, named as 'coordinated localization'. The appropriateness of our approach is demonstrated empirically in both simulation & real-time (on Amigo-bots) and its efficacy verified. Extensive comparative analysis portrays the advantage of the current method over others that do not perform active localization in a multi-robotic sense. It also portrays the performance gain by considering map structure and robot placement to actively localize over methods that consider only one of them or neither. Theoretical backing stems from the proven completeness of the method for a large category of diverse environments.