This paper proposes a method called Segmentation-adaptive Pseudospectral collocation to address the problem of safe trajectory generation in missions with cooperating multiple aerial vehicles. Pseudospectral collocation can generate optimized collision-free trajectories, but for multiple aerial vehicles it cannot guarantee that the safety separation distance is maintained in the whole trajectories, since the constraints are only enforced in discrete points in the trajectory (collocation points). Hp-adaptive pseudospectral collocation increases iteratively the number of collocation points and the degree of the approximating polynomial, but this may lead to an exponential increase of the computational load. The proposed method solves the problem by selectively adding new collocation points where they are needed, only in the segments with conflicts in each iteration, thus effectively reducing the number of collocation points and the computation time with respect to other pseudospectral collocation formulations. The proposed method allows both changes of speed and changes of heading for each aerial vehicle to guarantee the safety distance between them. Its computational load and scalability are studied in randomly generated scenarios. Moreover, a comparison with other method is presented. Several experiments to test the validity of the approach have been also carried out in the multivehicle aerial testbed of the Center for Advanced Aerospace Technologies.

This paper proposes the application of an hp-adaptive pseudospectral for trajectory generation in scenarios with multiple aerial vehicles in order to avoid collisions. This method computes an optimal solution numerically. The method assigns a speed profile to each aerial vehicle in real time such that the separation between them is greater than a minimum safety value and the total deviation from the initial trajectories is minimized. The Estimated Time of Arrival (ETA) of each aerial vehicle is also taken into account to solve the conflicts. Its computational load and scalability depending on the main parameters of the method are studied. Many simulations have been performed to analyze the best parameters of the method. Experiments have been also carried out in the multivehicle aerial testbed of the Center for Advanced Aerospace Technologies (CATEC).

This paper addresses the problem of extending the flight duration of cooperative missions with multiple gliding fixed-wing UAVs by using the energy that comes from static soaring. We consider exploration missions where UAVs should pass through a set of Point of Interest (PoI) with the presence of thermals in the space. These thermals can be exploited to provide energy in terms of altitude for each gliding UAV. The objective of the mission is to extend the flight duration of each UAV to explore the environment without landing and decreasing the time to perform the mission. An algorithm named Bounded Recursive Heuristic Search (BRHS), based on Depth-First search techniques, is applied to the PoIs and to the UAVs. The main advantage is the real time application because of the low computational load. The paper presents a set of simulations and experiments have carried out in the airfield of La Cartuja (Seville, Spain).