Battery Charge Scheduling in Long-Life Autonomous Mobile Robots

The daily working hours of long-life mobile robots are limited primarily by battery life. Most systems use a combination of hard thresholds and fixed periods to decide when to charge. This produces charging behaviour that ignores high-value tasks that must be performed within time-windows or by deadlines. Instead the robot should schedule charging adaptively, taking into account the times of day when it is expected to be given more valuable tasks to perform. This paper proposes an approach that exploits the fact that, during long-term deployments, the robot can learn when it is most probable that valuable tasks are added to the system, thus it can plan to charge on times that are expected to be less busy. We pose the problem of scheduling battery charging as a multi-objective sequential decision making problem over a time-dependent Markov decision process model of expected task rewards and battery behaviour. We compare a typical rule-based approach to our multi-objective scheduler and show that our approach enables for more flexible and efficient robot behaviour, which takes into account both the value of current available tasks and the predicted value of future tasks to decide whether to charge at a given time.