Perception Computing-Aware Controller Synthesis for Autonomous Systems

Feedback control loops are ubiquitous in any autonomous system. The design flow for any controller starts by determining a control strategy, while abstracting away all implementation details. However, when designing controllers for autonomous systems, there is significant computation associated with the perception modules. For example, this involves vision processing using deep neural networks on multicore CPU+accelerator platforms. Such computation can be organized in many different ways, with each choice resulting in very different sensor-to-actuator delays and tradeoffs between cost, delay, and accuracy. Further, each of these choices requires the control strategy to be designed accordingly. It is not possible for a control designer to enumerate and account for all of these choices manually, or abstract them away as “implementation details” as done in traditional controller design. In this paper we outline this problem and discuss how automated controller-synthesis techniques could help in addressing it.

反馈控制回路在任何自主系统中都无处不在。任何控制器的设计流程都是从确定控制策略开始，同时抽象出所有实现细节。然而，在为自主系统设计控制器时，感知模块会涉及大量计算。例如，这包括在多核CPU +加速器平台上使用深度神经网络进行视觉处理。这种计算可以通过许多不同的方式组织，每种选择都会导致传感器到执行器的延迟有很大差异，并在成本、延迟和准确性之间产生不同的权衡。此外，这些选择中的每一个都要求相应地设计控制策略。控制设计师不可能手动列举并考虑所有这些选择，也不可能像传统控制器设计那样将它们抽象为“实现细节”。在本文中，我们概述了这个问题，并讨论了自动化控制器综合技术如何有助于解决该问题。