Bruno Sinopoli
Arizona State University Campus Immersion, AZ·Engineering
Autonomous vehicles and delivery drones are cyber-physical systems, machines where onboard computers continuously sense and steer physical motion. They often run on small, limited processors that must juggle several control tasks at once.
- Fields
Engineering
Computer Science
Mathematics & Statistics
- Topics
cyber-physical systems
model predictive control
real-time scheduling
- Grants
- 1 known grant, through Jul 2028
What they work on
- Establish closed-loop stability guarantees for REAP solvers, optimization methods that return a safe, feasible answer even when stopped early, and develop ways to implement them on discrete-time digital hardware.
- Develop proactive, safe real-time scheduling methods that adjust how processor time is divided among control tasks as computing demand changes on constrained cyber-physical platforms.
- Design cooperative, computation-aware distributed model predictive control, in which multiple controllers that plan ahead by optimization coordinate while accounting for their limited computing time.
- Develop control methods for systems with time-varying constraints and apply them to automated drone delivery with industrial partners.
More about the research
As a result, the computing time available to any one controller is limited and can change from moment to moment. If a controller's calculation is cut short, the quality of control, and with it safety, can suffer.
Why it matters
Drones, self-driving vehicles and other autonomous machines must stay safe even when their onboard computers run short of processing time. Controllers that still deliver a safe, usable answer when interrupted, paired with scheduling that adapts to changing workloads, could make such systems more reliable on cheap, compact hardware. The planned work with industry partners targets commercial drone delivery.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Somil Bansal
Stanford University, CA·Engineering·Early-career
Develop training techniques that make machine-learning perception policies robust by construction, so camera-based controllers stay safe in new situations.
Ali Mesbah
University of California-Berkeley, CA·Engineering
Develop a multi-objective optimization framework that co-designs the software and hardware of model predictive control (MPC) chips for uncertain nonlinear systems with kilohertz-speed dynamics.
Justin Ruths
University of Texas at Dallas, TX·Engineering·Early-career
Represent each autonomy-stack module (mapping, perception, planning, control) and the vehicle's physical dynamics as hybrid zonotopes, sets made of unions of convex polytopes, to create one common form.
The school
Arizona State University Campus Immersion: 36 labs on PI Pages, $1.0B a year on research, 625 research doctorates a year.
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