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

TL;DR

This paper presents a novel context-aware task handling approach for resource-limited robots, combining dynamic time-sharing, event-driven scheduling, and virtualization to enhance real-time performance.

Contribution

It introduces a new integrated technique that improves task management and execution speed in resource-constrained robotic systems.

Findings

42% speedup in total execution time

Effective handling of multiple tasks like SLAM, sign detection, and speech recognition

Enhanced real-time performance on Raspberry-Pi robots

Abstract

Intelligent mobile robots are critical in several scenarios. However, as their computational resources are limited, mobile robots struggle to handle several tasks concurrently and yet 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 handling tasks in real-time, our proposed context-aware technique comprises of 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.