Three Cases of Connectivity and Global Information Transfer in Robot Swarms

In this work we consider three different cases of robot-robot interactions and resulting global information transfer in robot swarms. These mechanisms define cooperative properties of the system and can be used for designing collective behavior. These three cases are demonstrated and discussed based on experiments in a swarm of microrobots "Jasmine"

Multi-Functional Sensing for Swarm Robots Using Time Sequence Classification: HoverBot, an Example

Scaling up robot swarms to collectives of hundreds or even thousands without sacrificing sensing, processing, and locomotion capabilities is a challenging problem. Low-cost robots are potentially scalable, but the majority of existing systems have limited capabilities, and these limitations substantially constrain the type of experiments that could be performed by robotics researchers. Instead of adding functionality by adding more components and therefore increasing the cost, we demonstrate how low-cost hardware can be used beyond its standard functionality. We systematically review 15 swarm robotic systems and analyse their sensing capabilities by applying a general sensor model from the sensing and measurement community. This work is based on the HoverBot system. A HoverBot is a levitating circuit board that manoeuvres by pulling itself towards magnetic anchors that are embedded into the robot arena. We show that HoverBot’s magnetic field readouts from its Hall-effect sensor can be associated to successful movement, robot rotation and collision measurands. We build a time series classifier based on these magnetic field readouts. We modify and apply signal processing techniques to enable the online classification of the time-variant magnetic field measurements on HoverBot’s low-cost microcontroller. We enabled HoverBot with successful movement, rotation, and collision sensing capabilities by utilising its single Hall-effect sensor. We discuss how our classification method could be applied to other sensors to increase a robot’s functionality while retaining its cost

Mixed Reality Environment and High-Dimensional Continuification Control for Swarm Robotics

A significant challenge in control theory and technology is to devise agile and less resource-intensive experiments for evaluating the performance and feasibility of control algorithms for the collective coordination of large-scale complex systems. Many new methodologies are based on macroscopic representations of the emerging system behavior, and can be easily validated only through numerical simulations, because of the inherent hurdle of developing full scale experimental platforms. In this paper, we introduce a novel hybrid mixed reality set-up for testing swarm robotics techniques, focusing on the collective motion of robotic swarms. This hybrid apparatus combines both real differential drive robots and virtual agents to create a heterogeneous swarm of tunable size. We validate the methodology by extending to higher dimensions, and investigating experimentally, continuification-based control methods for swarms. Our study demonstrates the versatility and effectiveness of the platform for conducting large-scale swarm robotics experiments. Also, it contributes new theoretical insights into control algorithms exploiting continuification approaches