This paper presents the development and hardware implementation of an autonomous battery maintenance mechatronic system that significantly extends the operational time of battery powered small-scaled unmanned aerial vehicles (UAVs). A simultaneous change and charge approach is used to overcome the significant downtime experienced by existing charge-only approaches. The automated system quickly swaps a depleted battery of a UAV with a replenished one while simultaneously recharging several other batteries. This results in a battery maintenance system with low UAV downtime, arbitrarily extensible operation time, and a compact footprint. Hence, the system can enable multiagent UAV missions that require persistent presence. This capability is illustrated by developing and testing in flight a centralized autonomous planning and learning algorithm that incorporates a probabilistic health model dependent on vehicle battery health that is updated during the mission, and replans to improve the performance based on the improved model. Flight test results are presented for a 3-h-long persistent mission with three UAVs that each has an endurance of 8-10 min on a single battery charge (more than 100 battery swaps).Index Terms-Battery management systems, learning (artificial intelligence), Markov processes, multiagent systems, unmanned aerial vehicles (UAVs).

A tool developed for the preliminary design of low-thrust trajectories is described. The trajectory is discretized into segments and a nonlinear programming method is used for optimization. The tool is easy to use, has robust convergence, and can handle many intermediate encounters. In addition, the tool has a wide variety of features, including several options for objective function and different low-thrust propulsion models (e.g., solar electric propulsion, nuclear electric propulsion, and solar sail). High-thrust, impulsive trajectories can also be optimized.

This paper introduces a hardware platform for automated battery changing and charging for multiple UAV agents. The automated station holds a buffer of 8 batteries in a novel dual-drum structure that enables a "hot" battery swap, thus allowing the vehicle to remain powered on throughout the battery changing process. Each drum consists of four battery bays, each of which is connected to a smartcharger for proper battery maintenance and charging. The hot-swap capability in combination with local recharging and a large 8-battery capacity allow this platform to refuel multiple UAVs for long-duration and persistent missions with minimal delays and no vehicle shutdowns. Experimental results from the RAVEN indoor flight test facility are presented that demonstrate the capability and robustness of the battery change/charge station in the context of a multi-agent, persistent mission where surveillance is continuously required over a specified region.

This paper presents algorithms and flight test results for multi-agent cooperative planning problems in presence of state-correlated uncertainty.An online learning and planning framework is used to address the problem of improving planner performance for missions with state-dependent uncertain agent health dynamics. The framework includes a previously introduced Decentralized Multi-agent Markov decision process (Dec-MMDP) as an online planning algorithm that is scalable in number of agents, and Incremental Feature Discovery (iFDD) which is a compact and fast learning algorithm for estimating parameters of a state-correlated uncertainty model. In combination, this architecture yield an integrated learning-planning algorithm where the planning performance improves as uncertainty is reduced through learning. The presented algorithms are validated in a persistent search and track scenario with a novel automated battery swapping/recharging system that enables the UAVs to collaboratively track targets over durations that are significantly larger than individual vehicle endurance with a single battery. The results indicate that the architecture can be used as an computationally efficient solution to multi-agent uncertain cooperative planning problems.