Communication in reactive multiagent robotic systems
Autonomous Robots
Mobile Robot Miniaturisation: A Tool for Investigation in Control Algorithms
The 3rd International Symposium on Experimental Robotics III
Cooperative Autonomous Low-Cost Robots for exploring Unknown Environments
The 4th International Symposium on Experimental Robotics IV
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
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Is it more efficient to use one or several robots? Will the performance of a group of robots working in a collaborative task be enhanced if the robots can communicate with one another? What learning abilities should the robot(s) be provided with for adapting to a continuously changing environment? We address these three issues in a specific task, namely learning the topography of an environment whose features change frequently. We propose a learning algorithm based on an associative memory which allows a group of robots to keep an up-to-date account of the environmental state when this changes regularly. A probabilistic model is developed which gives an abstract representation of the system. It is used to determine boundciries for the system's variables (the number of robots, the frequency of environmental changes, and the environment's configuration) within which the learning is successful. The predictions of the probabilistic model axe confirmed by simulations run in Webots, a 3-D simulator of Khepera robots.