4 accepted papers
Purposeful interaction with objects usually requires certain constraints to be respected, e.g. keeping a bottle upright to avoid spilling. In reinforcement learning, such constraints are typically encoded in the reward function. As a consequence, constraints can only be learned by violating them. Th…
Safe navigation of Micro Aerial Vehicles (MAVs) requires not only obstacle-free flight paths according to a static environment map, but also the perception of and reaction to previously unknown and dynamic objects. This implies that the onboard sensors cover the current flight direction. Due to the…
The past years have shown a remarkable growth in use-cases for micro aerial vehicles (MAVs). Conceivable indoor applications require highly robust environment perception, fast reaction to changing situations, and stable navigation, but reliable sources of absolute positioning such as global navigati
Complex indoor and outdoor missions for autonomous micro aerial vehicles (MAV) require fast generation of collision-free paths in 3D space. Often not all obstacles in an environment are known prior to the mission execution. Consequently, the ability for replanning during a flight is key for success.…