Aerial robotics have become ubiquitous, but (like most robots) they still struggle to operate at high speed in unstructured, cramped environments. By considering a vehicle’s mechanical design simultaneously with the design of controls and automation algorithms, we have more degrees of freedoms to find creative solutions to problems. In this talk I will present some of my group’s work on enhancing aerial robots, including purely algorithmic approaches (“how can I do more with the hardware I already have?”) and with hardware co-design (“how can I change the vehicle so that the hard problem is actually easy?”). Two challenges for aerial robots will motivate us: first: flight through narrow, unstructured environments, and second: long duration and range flight within the constraints of battery-electric power.

For flight through narrow environments, I will present an algorithmic approach for high speed path planning that incorporates perception uncertainty, and can be used on a standard drone. We will then present two alternative approaches that modify the system design: one a vehicle that can change its shape to fit through narrower spaces, and a second that is highly collision resilient, and for whom collisions are therefore neither mission- nor safety-critical.

For overcoming energetic challenges, we will present a strategy for real-time optimization of flight characteristics for a vehicle, specifically using extremum seeking control to modify the system airspeed and yaw angle; an algorithm that can be applied to any aerial robot. We then again show two design modifications to work around the problem — first, a morphing system that can reduce its drag area at speed, and secondly a system capable of mid-air battery replacement for indefinite flight.