Motion Behaviour Recognition Dataset Collected from Human Perception of Collective Motion

Collective motion behaviour such as the movement of swarming bees, flocking birds or schooling fish has inspired computer-based swarming systems. They are widely used in agent formation control, including aerial and ground vehicles, teams of rescue robots, and exploration of dangerous environments with groups of robots. Collective motion behaviour is easy to describe, but highly subjective to detect. Humans can easily recognise these behaviours; however, it is hard for a computer system to recognise them. Since humans can easily recognise these behaviours, ground truth data from human perception is one way to enable machine learning methods to mimic this human perception. Hence ground truth data has been collected from human perception of collective motion behaviour recognition by running an online survey. In this survey, participants provide their opinion about the behaviour of ‘boid’ point masses. Each question of the survey contains a short video (around 10 seconds), captured from simulated boid movements. Participants were asked to drag a slider to label each video as either ‘flocking’ or ‘not flocking’; ‘aligned’ or ‘not aligned’ or ‘grouped’ or ‘not grouped’. By averaging these responses, three binary labels were created for each video. This data has been analysed to confirm that it is possible for a machine to learn binary classification labels from the human perception of collective behaviour dataset with high accuracy

A Mechanism for Transferring Evolved Collective Motion Behaviour Libraries onto Real Collective Robots

Evolutionary computation algorithms are heuristic techniques that can find multiple good solutions to solve complex problems. A recent, emerging application of evolutionary computation is the evolution of behaviour libraries for collective robots. However, a limitation of these approaches is that behaviours are evolved under simple, simulated conditions that differ from the dynamics of real robots. This paper proposes a mechanism for transferring evolved behaviours onto real collective robots and demonstrates that the robots exhibit collective motion characteristics consistent with the evolved simulated behaviours. We show that this library includes a greater number of robust and more diverse collective motion behaviours than what was possible with existing techniques of collective motion tuning

Generating Collective Motion Behaviour Libraries using Developmental Evolution

This paper presents an evolutionary framework for generating diverse libraries of collective motion behaviours. It builds upon recent advancements in machine recognition of collective motion and the transformation of random motions into structured collective behaviours. The paper describes the design of the framework, including the use of a fitness function and diversity metrics specifically tailored for this purpose. The proposed framework generates diverse behaviours with distinct collective motion characteristics. Analysing the relationship between genotypic and behavioural diversity, we observed that greater diversity emerges after a moderate number of evolutionary generations. Our findings highlight the effectiveness of task non-specific fitness functions in distinguishing structured collective behaviours in an evolutionary setting