Abstract
Robot swarms consist of large numbers of autonomous robots, whose behaviour has been greatly inspired by existing complex biological, physical or chemical systems. This is especially the case for behaviours that involve mechanisms leading to spatial self-organisation of robots. The complex nature of these behaviours prevents a human operator from keeping a mental model of them, which makes it difficult to interact with them, even though this is necessary in certain cases: prediction of a loss of stability, detection of blocking situations, etc. How to allow an operator to grasp the complexity of self-organised robot swarms? This article aims at providing leads to answer this question, by investigating what humans are capable of perceiving of a complex system, and what additional information could be needed to enable them to understand its dynamics and state, and to predict the effects of their control. We first present what an operator is able to perceive from a large number of agents, self-organised or not, through a state of the art of existing works in cognitive sciences, vision and swarm robotics. Secondly, we identify in the literature the different types of information on robot swarms that are transmitted to the operator, with the aim of facilitating his perception and improving his understanding. Finally, we discuss what could be the information needed to build a mental model of the operator, the avenues being explored and the possible challenges to be taken into account.
Jérémy Rivière
Associate Professor
Aymeric Henard
PhD Student
Etienne Peillard
Associate Professor
My research interests include human perception issues in Virtual and Augmented Reality, spatial perception in virtual and augmented environments, and more generally, the effect of perceptual biases in mixed environments.
Sébastien Kubicki
Associate Professor
