Integration of environment sensing and control functions for Robust Rotorcraft UAV (RUAV) guidance.

Abstract

Unmanned Air Vehicles (UAVs) have started supplanting manned aircraft in a broad range of tasks. Vehicles such as miniature rotorcrafts with broad maneuvering range and small size can enter remote locations that are hard to reach using other air and ground vehicles. Developing a guidance system which enables a Rotorcraft UAV (RUAV) to perform such tasks involves combing key elements from robotics motion planning, control system design, trajectory optimization as well as dynamics modeling. The focus of this thesis is to integrate a guidance system for a small-scale rotorcraft to enable a high level of performance and situational awareness. We cover large aspects of the system integration including modeling, control system design, environment sensing as well as motion planning in the presence of uncertainty. The system integration in this thesis is performed around a Blade-CX2 miniature coaxial helicopter. The first part of the thesis focuses on the development of the parameterized model for the Blade-CX2 helicopter with an emphasis on the coaxial rotor configuration. The model explicitly accounts for the dynamics of lower rotor and uses an implicit lumped parameter model for the upper rotor and stabilizer-bar. The parameterized model was identified using frequency domain system identification. In the second part of the thesis, we use the identified model to design a control law for the Blade-CX2 helicopter. The control augmentation for the Blade-CX2 helicopter was based on a nested attitude-velocity loop control architecture and was designed following classical loop-shaping and dynamic inversion techniques. A path following layer wrapped around the velocity control system enables the rotorcraft to follow reference trajectories specified by a sequence of waypoints and velocity vectors. Such reference paths are common in autonomous guidance systems. Finally, the third part of the thesis addresses the problem of autonomous navigation through a partially known or unknown 3D cluttered environment. The proposed multi-layer hierarchical guidance framework is based on optimal control principles and relies on the interaction of several subsystems such as environment sensing and mapping, Cost-to-Go (CTG) function update, reactive planning and Receding Horizon (RH) optimization. It is also tightly integrated with the path following controller.