Intelligent Battery Management for aquatic drones based on Task Difficulty driven POMDPs

Autonomous drones typically have limited battery capacity, which presents a major challenge for persistent, long-term deployments. In the con- text of water quality monitoring, aquatic drones must navigate rivers and lakes to collect real-time data, where the battery consumption is heavily influenced by dynamic environmental factors such as flowing current and wind. Intelligent bat- tery management is a requirement for the success of these missions. We propose a formalization of the battery management problem in terms of Partially Observ- able Markov Decision Processes (POMDPs). We model the problem as a POMDP where the agent modulates the energy provided to its propellers. The drone can estimate the “difficulty” to traverse an interval by observing the environment. An important aspect of this formalization is the prediction of future intervals’ difficulty values based on current observations by exploiting a priori geometric information, which we call Task Difficulty Propagation (TDP). We investigate variations of this approach and analyze related performance.