Safe navigation strategies for quadrotors

Autonomous quadcopters are rapidly emerging as a mature technology poised to play a significant role in shaping society in the near future, thanks to their increasing availability and diverse range of applications. From agricultural operations to the transportation of goods and people, these vehicles are set to become an integral part of our daily lives. Given this growing presence, it is crucial to equip these autonomous systems with state-of-the-art algorithms for collision avoidance, which will help prevent damage to people and property, while also ensuring the continued autonomy and operational integrity of the vehicles. The central objective of this thesis is to address this critical challenge. The initial chapters of the thesis provide a comprehensive introduction to the subject matter, including an overview of the relevant literature. We begin by exploring the dynamical model of quadrotors, highlighting its key properties and the challenges these present when designing feasible trajectories for navigating cluttered environments. Following this, we delve into the fundamental concepts of Control Barrier Functions (CBFs) and their application to collision avoidance scenarios. We examine how safety filters can be derived from distance measurements and used to design robust control laws for safe navigation in unknown environments. Finally, driven by the limitations of the sensors commonly employed on autonomous quadrotors—such as monocular and stereo cameras—the concluding section of this work shifts focus towards addressing these constraints. Specifically, we propose an approach based on the CBF framework that accounts for the limited field of view inherent in visual sensors. Additionally, we present a control law, rooted in Control Lyapunov Functions approach, designed to track reference trajectories using feedback based on visual bearings.