Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
How to learn an unknown environment (extended abstract)
SFCS '91 Proceedings of the 32nd annual symposium on Foundations of computer science
A mobile robot that learns its place
Neural Computation
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Quantitative evaluation of the exploration strategies of a mobile robot
International Journal of Robotics Research
How to learn an unknown environment. I: the rectilinear case
Journal of the ACM (JACM)
Frontier-based exploration using multiple robots
AGENTS '98 Proceedings of the second international conference on Autonomous agents
Xavier: a robot navigation architecture based on partially observable Markov decision process models
Artificial intelligence and mobile robots
Efficient and inefficient ant coverage methods
Annals of Mathematics and Artificial Intelligence
Cooperative Mobile Robotics: Antecedents and Directions
Autonomous Robots
Bootstrap learning for place recognition
Eighteenth national conference on Artificial intelligence
Context-based vision system for place and object recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Multi-robot Simultaneous Localization and Mapping using Particle Filters
International Journal of Robotics Research
The power of team exploration: two robots can learn unlabeled directed graphs
SFCS '94 Proceedings of the 35th Annual Symposium on Foundations of Computer Science
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
Coordinated multi-robot exploration
IEEE Transactions on Robotics
Loop-closing: A typicality approach
Robotics and Autonomous Systems
Multi robot exploration using a modified a algorithm
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part I
Effects of Multi-Robot Team Formations on Distributed Area Coverage
International Journal of Swarm Intelligence Research
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Whenever multiple robots have to solve a common task, they need to coordinate their actions to carry out the task efficiently and to avoid interferences between individual robots. This is especially the case when considering the problem of exploring an unknown environment with a team of mobile robots. To achieve efficient terrain coverage with the sensors of the robots, one first needs to identify unknown areas in the environment. Second, one has to assign target locations to the individual robots so that they gather new and relevant information about the environment with their sensors. This assignment should lead to a distribution of the robots over the environment in a way that they avoid redundant work and do not interfere with each other by, for example, blocking their paths. In this paper, we address the problem of efficiently coordinating a large team of mobile robots. To better distribute the robots over the environment and to avoid redundant work, we take into account the type of place a potential target is located in (e.g., a corridor or a room). This knowledge allows us to improve the distribution of robots over the environment compared to approaches lacking this capability. To autonomously determine the type of a place, we apply a classifier learned using the AdaBoost algorithm. The resulting classifier takes laser range data as input and is able to classify the current location with high accuracy. We additionally use a hidden Markov model to consider the spatial dependencies between nearby locations. Our approach to incorporate the information about the type of places in the assignment process has been implemented and tested in different environments. The experiments illustrate that our system effectively distributes the robots over the environment and allows them to accomplish their mission faster compared to approaches that ignore the place labels.