Evolutionary robotics in high altitude wind energy applications

Recent years have seen the development of wind energy conversion systems that can exploit the superior wind resource that exists at altitudes above current wind turbine technology. One class of these systems incorporates a flying wing tethered to the ground which drives a winch at ground level. The wings often resemble sports kites, being composed of a combination of fabric and stiffening elements. Such wings are subject to load dependent deformation which makes them particularly difficult to model and control. Here we apply the techniques of evolutionary robotics i.e. evolution of neural network controllers using genetic algorithms, to the task of controlling a steerable kite. We introduce a multibody kite simulation that is used in an evolutionary process in which the kite is subject to deformation. We demonstrate how discrete time recurrent neural networks that are evolved to maximise line tension fly the kite in repeated looping trajectories similar to those seen using other methods. We show that these controllers are robust to limited environmental variation but show poor generalisation and occasional failure even after extended evolution. We show that continuous time recurrent neural networks (CTRNNs) can be evolved that are capable of flying appropriate repeated trajectories even when the length of the flying lines are changing. We also show that CTRNNs can be evolved that stabilise kites with a wide range of physical attributes at a given position in the sky, and systematically add noise to the simulated task in order to maximise the transferability of the behaviour to a real world system. We demonstrate how the difficulty of the task must be increased during the evolutionary process to deal with this extreme variability in small increments. We describe the development of a real world testing platform on which the evolved neurocontrollers can be tested

Vision-based control of near-obstacle flight

Lightweight micro unmanned aerial vehicles (micro-UAVs) capable of autonomous flight in natural and urban environments have a large potential for civil and commercial applications, including environmental monitoring, forest fire monitoring, homeland security, traffic monitoring, aerial imagery, mapping and search and rescue. Smaller micro-UAVs capable of flying inside houses or small indoor environments have further applications in the domain of surveillance, search and rescue and entertainment. These applications require the capability to fly near to the ground and amongst obstacles. Existing UAVs rely on GPS and AHRS (attitude heading reference system) to control their flight and are unable to detect and avoid obstacles. Active distance sensors such as radars or laser range finders could be used to measure distances to obstacles, but are typically too heavy and power-consuming to be embedded on lightweight systems. In this thesis, we draw inspiration from biology and explore alternative approaches to flight control that allow aircraft to fly near obstacles. We show that optic flow can be used on flying platforms to estimate the proximity of obstacles and propose a novel control strategy, called optiPilot, for vision-based near-obstacle flight. Thanks to optiPilot, we demonstrate for the first time autonomous near-obstacle flight of micro-UAVs, both indoor and outdoor, without relying on an AHRS nor external beacons such as GPS. The control strategy only requires a small series of optic flow sensors, two rate gyroscopes and an airspeed sensor. It can run on a tiny embedded microcontroller in realtime. Despite its simplicity, optiPilot is able to fully control the aircraft, including altitude regulation, attitude stabilisation, obstacle avoidance, landing and take-off. This parsimony, inherited from the biology of flying insects, contrasts with the complexity of the systems used so far for flight control while offering more capabilities. The results presented in this thesis contribute to a better understanding of the minimal requirements, in terms of sensing and control architecture, that enable animals and artificial systems to fly and bring closer to reality the perspective of using lightweight and inexpensive micro-UAV for civilian purposes

Design of Flying Robots for Collision Absorption and Self-Recovery

Flying robots have the unique advantage of being able to move through the air unaffected by the obstacles or precipices below them. This ability quickly becomes a disadvantage, however, as the amount of free space is reduced and the risk of collisions increases. Their sensitivity to any contact with the environment have kept them from venturing beyond large open spaces and obstacle-free skies. Recent efforts have concentrated on improving obstacle detection and avoidance strategies, modeling the environment and intelligent planning to navigate ever tighter spaces while remaining airborne. Though this strategy is yielding impressive and improving results, it is limited by the quality of the information that can be provided by on-board sensors. As evidenced by insects that collide with windows, there will always be situations in which sensors fail and a flying platform will collide with the obstacles around it. It is this fact that inspired the topic of this thesis: enabling flying platforms to survive and recover from contact with their environment through intelligent mechanical design. There are three main challenges tackled in this thesis: robustness to contact, self-recovery and integration into flight systems. Robustness to contact involves the protection of fast-spinning propellers, the stiff inner frame of a flying robot and its embedded sensors from damage through the elastic absorption of collision energy. A method is presented for designing protective structures that transfer the lowest possible amount of force to the platform's frame while simultaneously minimizing weight and thus their effect on flight performance. The method is first used to design a teardrop-shaped spring configuration for absorbing head-on collisions typically experienced by winged platforms. The design is implemented on a flying platform that can survive drops from a height of 2 m. A second design is then presented, this time using springs in a tetrahedral configuration that absorb energy through buckling. When embedded into a hovering platform the tetrahedral protective mechanisms are able to absorb dozens of high-speed collisions while significantly reducing the forces on the platforms frame compared to foam-based protection typically used on other platforms. Surviving a collision is only half of the equation and is only useful if a flying platform can subsequently return to flight without requiring human intervention, a process called self-recovery. The theory behind self-recovery as it applies to many types of flying platforms is first presented, followed by a method for designing and optimizing different types of self-recovery mechanisms. A gravity-based mechanism is implemented on an ultra-light (20.5 g) wing-based platform whose morphology and centre of gravity are optimized to always land on its side after a collision, ready to take off again. Such a mechanism, however, is limited to surfaces that are flat and obstacle-free and requires clear space in front of the platform to return to the air. A second, leg-based self-recovery mechanism is thus designed and integrated into a second hovering platform, allowing it to upright into a vertical takeoff position. The mechanism is successful in returning the platform to the air in a variety of complex environments, including sloped surfaces, corners and surface textures ranging from smooth hardwood to gravel and rocks. In a final chapter collision energy absorption and self-recovery mechanisms are integrated into a single hovering platform, the first example of a flying robot capable of crashing into obstacles, falling to the ground, uprighting and returning to the air, all without human intervention. These abilities are first demonstrated through a contact-based random search behaviour in which the platform explores a small enclosed room in complete darkness. After each collision with a wall the platform falls to the ground, recovers and then continues exploring. In a second experiment the platform is programmed with a basic phototaxis behaviour. Using only four photodiodes that provide a rough idea of the bearing to a source of light the platform is able to consistently cross a 13x2.2mcorridor and traverse a doorway without using any obstacle avoidance, modeling or planning

Spinoff 2015

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