A new type of coaxial-rotor unmanned helicopter capable of physically interacting with its environment is the subject of this paper. Its design is optimized in order to provide the means of robust environmental interaction through contact (e.g. docking and sliding on walls). Due to the rapid change of the dynamics from the free-flying helicopter to the helicopter subject to the forces and moments during contact a hybrid systems modeling approach is followed. This global model of the system's dynamics is the basis for the design of a hybrid model predictive controller that guarantees the stability of the hybrid system and provides the capability of controlled docking on walls as well as sliding on them. The capabilities of the platform and the efficiency of the control law are illustrated through experimental studies.

This paper describes an accurate and extendable rotorcraft dynamics simulator developed to support the design and control of autonomous coaxial rotor vehicles. This simulator is capable of accurately predicting the dynamic flight response of coaxial rotor vehicles purely based on geometric, inertial and aerodynamic specifications. The simulator is fully configurable and implements the typical mechanical layouts found in model-size coaxial helicopters. The corresponding software framework as well as the underlying theory is presented in detail. System parameters for a coaxial rotor prototype have been estimated and the resulting simulation results compared with real flight-data demonstrating the capabilities of the presented simulation software.

This video presents experiments conducted within the final review meeting demonstration session of the AIRobots project. AIRobots started at 2010 and the final review meeting took place on 22 of March, 2013. The presented experiments cover a wide area of the challenges related with aerial industrial inspection. In particular, multiple test-cases related with both vision-based and contact-based inspection and in general physical interaction are shown. It is highlighted that these experiments were recorded live during the project demonstration and evaluation process.

This work presents a method for estimating the egomotion of an aerial vehicle in challenging industrial environments. It combines binocular visual and inertial cues in a tightly-coupled fashion and operates in real time on an embedded platform. An extended Kalman filter fuses measurements and makes motion estimation rely more on inertial data if visual feature constellation is degenerate. Errors in roll and pitch are bounded implicitly by the gravity vector. Inertial sensors are used for efficient outlier detection and enable operation in poorly and repetitively textured environments. We demonstrate robustness and accuracy in an industrial scenario as well as in general indoor environments. The former is accompanied by a detailed performance evaluation supported with ground truth measurements from an external tracking system.