Real-time path planning for long-term information gathering with an aerial glider

Autonomous thermal soaring offers an opportunity to extend the flight duration of unmanned aerial vehicles (UAVs). In this work, we introduce the informative soaring problem, where a gliding UAV performs an information gathering mission while simultaneously replenishing energy from known thermal energy sources. We pose this problem in a way that combines convex optimisation with graph search and present four path planning algorithms with complementary characteristics. Using a target-search task as a motivating example, finite-horizon and Monte Carlo tree search methods are shown to be appropriate for situations with little prior knowledge, but suffer from either myopic planning or high computation cost in more complex scenarios. These issues are addressed by two novel tree search algorithms based on creating clusters that associate high uncertainty regions with nearby thermals. The cluster subproblems are solved independently to generate local plans, which are then linked together. Numerical simulations show that these methods find high-quality nonmyopic plans quickly. The more promising cluster-based method, which uses dynamic programming to compute a total ordering over clusters, is demonstrated in hardware tests on a UAV. Fifteen-minute plans are generated in less than four seconds, facilitating online replanning when simulated thermals are added or removed in-flight.