Design, Control and Trajectory Planning of Reconfigurable Quadrotors

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Description
Unmanned aerial vehicles (UAVs) have revolutionized various fields, but their use in dynamic environments is still limited due to safety concerns arising from sensor malfunctions and localization errors. Inspired by birds, which exhibit unparalleled maneuverability and adaptability to dynamic environments

Unmanned aerial vehicles (UAVs) have revolutionized various fields, but their use in dynamic environments is still limited due to safety concerns arising from sensor malfunctions and localization errors. Inspired by birds, which exhibit unparalleled maneuverability and adaptability to dynamic environments by synergizing mechanical compliance with control, this research focused on developing a new generation of bio-inspired soft/compliant UAVs with mechanical intelligence that can withstand collisions and enable aerial interaction. The proposed approach is to harness collision energies and switch into the next favorable configuration, which helps retain stability and successfully fly even in the presence of external forces. It investigated various types of active/passive reconfigurable UAVs to demonstrate this idea. The first approach looked into designs of compliant reconfigurable quadrotors by employing springs which can reduce their dimension under external forces, thereby sustaining 2D planar collision forces and enabling flights through narrow gaps in a squeeze-and-fly manner. Next, fabric-based soft UAVs made of pneumatic beams were successfully explored to design lightweight and collision-resilient quadrotors to demonstrate 3D collision-resilience and impact-based perching. This research contributes to thorough modeling of the unique dynamics of these reconfigurable quadrotors and proposes various adaptive and learning-based controllers for robust low-level tracking. Finally, these controllers were integrated into a novel collision-inclusive motion planning framework based-on optimal control theory to perform physical interaction tasks, such as contact-based navigation, mapping, and inspection. In essence, this research redefines safety for UAVs and expands their capabilities for contact-rich tasks.