Hover and fast flight of minimum-mass mission-capable flying robots

Highly automated Unmanned Aerial Vehicles (UAVs) or "flying robots" are rapidly becoming an important asset to society. The last decade has seen the advent of an impressive number of new UAV types and applications. For many applications, the UAVs need to be safe, highly automated, and versatile. Safety is a prerequisite to allowing their use in society. While flight safety comprises many aspects, one important safety factor is the total system mass. The common thread through this research is therefore to minimize the system mass while maintaining mission capabilities to increase safety. Flight automation is required to reach many applications' full potential by addressing operational labor costs and scalability. But despite great advances in ground-based robotics, the weight and power constraints of flying robots still constitute important challenges. Last but not least, many applications also require versatile aircraft that combine the ability to hover and fly fast efficiently. Hover is required for precision take-off & landing in confined areas at a growing number of locations and for the close-up inspection of assets. Fast and efficient flight is needed to reach distant locations, perform large surveys, cope with high headwind conditions, or simply reach destinations quickly. Unfortunately, the requirements for hover and fast flight are conflicting, and this drives the search for solutions to ``combine hover with fast flight in mission-capable flying robots while cost-effectively minimizing their size and maximizing their safety.'' To investigate the minimal feasible mass of mission-capable robots, in this thesis, a novel 20 g tailed flapping-wing robot called DelFly Explorer is presented that can autonomously explore unknown unprepared rooms. It was equipped with a 4 g micro stereo-vision system which necessitated algorithms that were optimized for tiny microcontrollers with low memory. Combined with a navigation strategy that keeps the area in front of the robot free of obstacles, a 0.9 g autopilot, and DelFly's novel stable slow hovering flight regime, this led to the lightest flying indoor exploration robot that could navigate in unknown environments. But to combine passive dynamic longitudinal stability at slow hover and fast flight in tailed ornithopters, a shift in the center of gravity location was shown to be needed. Moreover, the aerodynamically stabilizing tail also causes sensitivity to turbulence. Therefore, by using four pairs of flapping wings, a new tail-less flapping-wing concept called Quad-thopter was created which can hover precisely and transition to fast forward flight. The cranked-rocker-based mechanism contains no expensive parts and by re-using the main propulsion motors for attitude control, powerful control moments can be created which are very important in disturbance rejection. This design represents one of the first tailless flapping wing designs that was sufficiently light and agile for performing real missions while featuring a mechanism simple enough to permit large-scale production. Versatility of flight is also an asset for outdoor flight. Theory predicts that the most efficient hover is achieved by using a single large rotor while the most efficient forward flight is performed by using high aspect ratio fixed wings to generate lift. The combination of both has led to a novel helicopter-with-wings concept called DelftaCopter. The control of this platform yields unique challenges such as the inertia of the large fixed-wing interferes with the dynamics of the helicopter rotor. A controller was derived, and the dynamics were identified in hover and forward flight. The real-world performance of this flying robot is presented by analyzing the results of its participation in the outback medical challenge, showing that large single-rotor-equipped fixed-wing aircraft combine powerful attitude control, efficient hover, and efficient forward flight. Since efficiency in forward flight is not sufficient to achieve a very long endurance in electrically powered flying robots, a novel platform was developed around a hydrogen pressure cylinder and a fuel cell. The concept focuses on versatility, minimal weight, good control, and redundancy. A 12-motor tail-sitter is presented that re-uses all its motors for attitude control, hover, and forward flight and uses the wing structure to carry the propulsion. A dual automotive CAN-bus control network and dual flight modes remove the most critical single points of failure. The platform is called the NederDrone and is shown to fly 3h38 in a test departing from a moving ship at sea in 5 Beaufort wind conditions. While reaching fast flight in large free blocks of air is mainly a challenge for the design of the airframe and its energy source, as soon as obstacles are introduced, new bottlenecks appear and the weight and power consumption of sensing and processing become driving design considerations. Increasing the flight speed of flying robots in obstacle-packed or GPS-denied environments highlights the need for very lightweight fast but intelligent systems, as the processing weight and power not only reduce the flight times but also reduce the maneuvering capabilities and the maximum speed. Therefore, in this work, an extreme example is studied in the context of autonomous drone racing. A computationally light Artificial Intelligence (AI) based monocular navigation system is presented for indoor flight through obstacles. It enabled the flying robot to fly at higher speeds than what was possible with state-of-the-art visual-inertial odometry solutions. Overall, aerospace platforms require extreme optimization as every gram kept in the air requires constant energy. The consequence is that different missions will require vastly different platforms, while traditionally a lot of flying robot applications are still performed by multicopters. This thesis contributes to the design of intelligent flying robots that can both hover and fly fast, by solving several fundamental problems in novel concepts optimized around the five key requirements of mass, agility, efficiency, range, and speed-near-obstacles. These concepts are expected to contribute to the improvement of the mission capabilities of minimal-size flying robots to address the needs of society.