Force FieldBased Indirect Manipulation Of UAV Flight Trajectories* For a variety of applications remote navigation of an unmanned aerial vehicle (UAV) along a flight trajectory is an essential task. For instance, during search and rescue missions in outdoor scenes, an important goal is to ensure safe navigation. Assessed by the remote operator, this could mean avoiding collisions with obstacles, but moreover avoiding hazardous flight areas. State of the art approaches enable navigation along trajectories, but do not allow for indirect manipulation during motion. In addition, they suggest to use egocentric views which could limit understanding of the remote scene. With this work we introduce a novel indirect manipulation method, based on gravitational law, to recover safe navigation in the presence of hazardous flight areas. The indirect character of our method supports manipulation at far distances where common direct manipulation methods typically fail. We combine it with an immersive exocentric view to improve understanding of the scene. We designed three flavors of our method and compared them during a user study in a simulated scene. While with this method we present a first step towards a more extensive navigation interface, as future work we plan experiments in dynamic realworld scenes.

Teaching a Drone to Accompany a Person from Demonstrations using NonLinear ASFM In this paper, we present a new method based on the Aerial Social Force Model (ASFM) to allow humandrone sidebyside social navigation in real environments. To tackle this problem, the present work proposes a new nonlinearbased approach using Neural Networks. To learn and test the rightness of the new approach, we built a new dataset with simulated environments and we recorded motion controls provided by a human expert teleoperating the drone. The recorded data is then used to train a neural network which maps interaction forces to acceleration commands. The system is also reinforced with a human path prediction module to improve the dronexe2x80x99s navigation, as well as, a collision detection module to completely avoid possible impacts. Moreover, a performance metric is defined which allows us to numerically evaluate and compare the fulfillment of the different learned policies. The method was validated by a large set of simulations; we also conducted reallife experiments with an autonomous drone to verify the framework described for the navigation process. In addition, a user study has been realized to reveal the social acceptability of the method.