Decision estimation and classification: an introduction to pattern recognition and related topics
Decision estimation and classification: an introduction to pattern recognition and related topics
Marsupial and Shape-Shifting Robots for Urban Search and Rescue
IEEE Intelligent Systems
ISER '00 Experimental Robotics VII
Backcasting: adaptive sampling for sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Convex Optimization
Estimating inhomogeneous fields using wireless sensor networks
IEEE Journal on Selected Areas in Communications
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Robotic teams are often proposed for solving a number of problems, ranging from exploring unknown environments to monitoring areas for security or environmental contamination. These teams are composed of individual robots which may lack the capabilities to complete a task on their own. One critical capability required by teams regardless of the mission is the ability to have sufficient battery life to remain active for the duration of the mission. We present an approach for maintaining battery life by developing a hierarchical team composed of deployable robots and docking stations. Unlike other approaches, the approach presented here focuses on docking stations supporting multiple deployed robots simultaneously. In order to do so the docking stations must continually optimize their locations with respect to the robots in need of service. Discussion of the optimization is presented, along with simulation in multiple environments to illustrate the scalability of the approach to large robotic teams. The on-going transition of this algorithm to actual hardware is also discussed.