Design of a vision based range bearing and heading system for robot swarms
This addresses localization for robot swarms, but it appears incremental as it builds on existing vision-based methods with specific hardware.
The researchers tackled the problem of robot swarm localization by developing a vision-based system using passive landmarks to detect range, bearing, and heading, achieving errors within an acceptable range.
An essential problem of swarm robotics is how members of the swarm knows the positions of other robots. The main aim of this research is to develop a cost-effective and simple vision-based system to detect the range, bearing, and heading of the robots inside a swarm using a multi-purpose passive landmark. A small Zumo robot equipped with Raspberry Pi, PiCamera is utilized for the implementation of the algorithm, and different kinds of multipurpose passive landmarks with nonsymmetrical patterns, which give reliable information about the range, bearing and heading in a single unit, are designed. By comparing the recorded features obtained from image analysis of the landmark through systematical experimentation and the actual measurements, correlations are obtained, and algorithms converting those features into range, bearing and heading are designed. The reliability and accuracy of algorithms are tested and errors are found within an acceptable range.