Context-Aware Task Handling in Resource-Constrained Robots with Virtualization

Abstract

Intelligent mobile robots are critical in several scenarios. However, as their computational resources are limited, mobile robots struggle to handle several tasks concurrently while guaranteeing real timeliness. To address this challenge and improve the real-timeliness of critical tasks under resource constraints, we propose a fast context-aware task handling technique. To effectively handle tasks in real-time, our proposed context-aware technique comprises three main ingredients: (i) a dynamic time-sharing mechanism, coupled with (ii) an event-driven task scheduling using reactive programming paradigm to mindfully use the limited resources; and, (iii) a lightweight virtualized execution to easily integrate functionalities and their dependencies. We showcase our technique on a Raspberry-Pi-based robot with a variety of tasks such as Simultaneous localization and mapping (SLAM), sign detection, and speech recognition with a 42% speedup in total execution time compared to the common Linux scheduler.