Requirments for current and future Unmanned Air Vehicles with longer ight endurances have led to a need for an autonomous soaring capability. This paper investigates aircraft ight path guidance for search and localization of Regions of Interest, consisting of atmospheric phenomena. The problem is posed as an o ine agent learning problem, of localizing atmospheric thermal locations and then guid...

Abstract We introduce Air Learning, an open-source simulator, and a gym environment for deep reinforcement learning research on resource-constrained aerial robots. Equipped with domain randomization, Learning exposes UAV agent to diverse set of challenging scenarios. seed the toolset point-to-point obstacle avoidance tasks in three different environments Deep Q Networks (DQN) Proximal Policy Op...

We present two heuristics based on constraint technology that solve the problem of generating air traffic management contingency plans, which are used in the case of a catastrophic infrastructure failure within EUROCONTROL, the European Organisation for the Safety of Air Navigation. Of the heuristics presented, one is based on constraintbased local search and tabu search, and the other one is a...

This paper addresses issues of co-evolution of form and function for autonomous vehicles, specifically evolving morphology and control for an autonomous micro air vehicle (MAV). The evolution of an optimal minimum sensor suite and reactive strategies for navigation and collision avoidance for the simulated MAV is described. The details of the implementation of the simulated aircraft, the enviro...