Computing for Control and Control for Computing
Xinkai Zhang1,a and Justin Bradley1,d
Department of Electrical and Computer Engineering University of Nebraska-Lincoln Lincoln, USA
axinkai.zhang@huskers.unl.edu
bjbradley@cse.unl.edu
ABSTRACT
Computing can be thought of as a service provided to a system to yield actionable tasks enacted by physical hardware. But rarely is control thought to be in the service of enhancing computation. Consideration of that perspective is what motivates co-regulation, our framework for holistic cyber-physical control of autonomous vehicles. In this paper we elaborate on how co-regulation will enable the next generation of autonomous vehicles precisely because it considers computation as an enabler and consumer of autonomous behavior. We report on the latest advances in this space showing how co-regulation exceeds results in event-triggered, self-triggered, and fixed-rate control strategies yielding more robustness and adaptivity to changing and uncertain conditions – a requirement for next-gen autonomous vehicles. We then describe a co-regulated decision making algorithm based on Markov Decision Processes showing how full consideration of computational resource allocation can increase decision-making capabilities in uncertain environments.
Keywords: Co-Regulation, Time-Varying Sampling, Resourceaware Control, Planning, Markov Decision Processes.
