2016 IEEE. In this work, we present a behavioral modeling framework and coverage behavior that accounts for a battery constraint. This framework allows a user to model robot teams performing common robotic tasks such as exploration. It uses roadmap-based methods that identify the available paths in potentially complex environments. We present a coverage strategy that accounts for the available battery. It allows the agent to calculate a path through an environment that maximizes coverage and allows the agent to get back to a charging location. This eliminates the need to decide when to return to a charging location based on a threshold, as related methods do. It considers the actual path length as opposed to Euclidean distance which is generally used for estimating the energy spent in traversing a path. Different path scoring functions are used to score the path generated.
name of conference
2016 IEEE International Conference on Automation Science and Engineering (CASE)
