Autonomous exploration of unknown environments usually focuses on maximizing the volumetric exploration of the surroundings. Object-oriented exploration, on the other hand, tries to minimize the time spent on the localization of some given objects of interest. While the former problem equally considers map growths in any free direction, the latter fosters exploration towards objects of interest partially seen and not yet accurately identified.The proposed work relates to a novel algorithm that focuses on an object-oriented exploration of unknown environments for aerial robots, able to generate volumetric representations of surroundings, semantically enhanced by labels for each object of interest.As a case study, this method is applied both in a simulated environment and in real-life experiments on a small aerial platform.

Robots face the eventuality of falling. Unplanned events, external disturbances and technical failures may lead a robot to a condition where even an effective dynamic stabilization is not sufficient to maintain the equilibrium. Therefore, it is essential to equip robotic platforms with both active and passive fall protection means to minimize damages, and enable the recovery and restart without physical human intervention. This work introduces a method to design an integrated safety system for two-wheeled humanoids. As a case study, the method is applied to a robot and experimentally tested under several conditions corresponding to different causes of robot instability, such as motor jamming, external disturbances, and sudden shutdown .