Introducing a Scalable and Modular Control Framework for Low-cost Monocular Robots in Hazardous Environments

Abstract

Robotics for hazardous environments is currently an important area of research, with the ambition of reducing human risk in potentially devastating situations. Here, we are presenting a Modular Control Framework (MCF) for a low-cost robot with limited sensory resources to address this issue. As a proof of concept, we emulate 3 scenarios - (1) adaptive planning for obstruction avoidance (road block), (2) object identification and support-case-based behaviour adjustment (search and rescue) and (3) autonomous navigation through the environment with reporting of structural status (patrol and monitoring). These were implemented and validated using a Cozmo robot in a small-scale Lego environment. We found that our system can reroute in 90%, can help an injured person 80% and report about failing equipment in 80% of all tested cases, where most of the fails were caused by the object detection used. Our MCF is implemented using ROS, making it easy to use and adjust for other robotic platforms.