  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.

Robotics