The increase in applications of unmanned aerial vehicles (UAVs) has led to a growing interest for limiting (or entirely removing) human control from complex operations, such as near-ground manoeuvres (e.g. landing). These operations present substantial challenges due to the need of accurately tracking and controlling the vehicle’s velocity and position, making their human supervision necessary. Conventional alternatives to remove the human element have relied on information provided by external reference frames (e.g. Global Navigations Satellite Systems, or GNSS) to measure or estimate the vehicle’s velocity and height. However, these approaches have important drawbacks regarding the contexts where they can operate (e.g. indoor situations, limited GNSS coverage areas), given their complete dependency on external reference frames. It is thus fundamental to explore alternative guidance methods that expand the UAVs’ usability within these contexts. This thesis looks into Nature for inspiration to solve this issue: The same complex nearground manoeuvres are performed by flying animals with ease, relying on information provided exclusively by their vision and vestibular systems instead of external reference frames. Specifically, this project is built upon Tau theory, a biologically inspired guidance strategy that relates the animals’ visual information to their locomotion, and whose potential applications to UAVs have been very limited so far. It employs computer hardware to mimic Tau theory to achieve autonomous visual vertical and horizontal guidance of a quadrotor, thus removing the need of external reference frames. This proposed system is then tested in a simulated environment and compared with a state-of-the-art guidance system relying on external reference frames to ensure its applicability and to identify potential limitations. The analysis of these simulations offers results at two levels: firstly, they demonstrate the system’s success when landing on a moving target under diverse sets of environmental conditions using exclusively visual and inertial information; and secondly, they show a similar performance than position-based systems indoors and equal performance as position-based systems under ideal environmental conditions outdoors. Therefore, this research demonstrates the viability and suitability of Tau theory and its application on UAVs’ onboard computer to achieve autonomous guidance.