Stanford Seminar - Hardware / controls co-design to overcome challenges for aerial robots

May 2, 2025 Mark Mueller, UC Berkeley 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 freedom 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?'). I will discuss two exemplary challenges for aerial robots: first: flight through narrow, unstructured environments, and second: long duration and range flight within the constraints of battery-electric power. Lastly, I will discuss some work on adaptive and learning control, specifically for robustness to parametric uncertainty. 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 modfiy 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. About the speaker: https://me.berkeley.edu/people/ma