Mohsen Raoufi

Mohsen Raoufi, Pawel Romanczuk, Heiko Hamann

Collective decision-making is an essential capability of large-scale multi-robot systems to establish autonomy on the swarm level. A large portion of literature on collective decision-making in swarm robotics focuses on discrete decisions selecting from a limited number of options. Here we assign a decentralized robot system with the task of exploring an unbounded environment, finding consensus on the mean of a measurable environmental feature, and aggregating at areas where that value is measured (e.g., a contour line). A unique quality of this task is a causal loop between the robots' dynamic network topology and their decision-making. For example, the network's mean node degree influences time to convergence while the currently agreed-on mean value influences the swarm's aggregation location, hence, also the network structure as well as the precision error. We propose a control algorithm and study it in real-world robot swarm experiments in different environments. We show that our approach is effective and achieves higher precision than a control experiment. We anticipate applications, for example, in containing pollution with surface vehicles.

Arash S. Amjadi, Mohsen Raoufi, Ali E. Turgut et al.

Using robots for exploration of extreme and hazardous environments has the potential to significantly improve human safety. For example, robotic solutions can be deployed to find the source of a chemical leakage and clean the contaminated area. This paper demonstrates a proof-of-concept bio-inspired exploration method using a swarm robotic system, which is based on a combination of two bio-inspired behaviours: aggregation, and pheromone tracking. The main idea of the work presented is to follow pheromone trails to find the source of a chemical leakage and then carry out a decontamination task by aggregating at the critical zone. Using experiments conducted by a simulated model of a Mona robot, we evaluate the effects of population size and robot speed on the ability of the swarm in a decontamination task. The results indicate the feasibility of deploying robotic swarms in an exploration and cleaning task in an extreme environment.

Mohsen Raoufi, Ali Emre Turgut, Farshad Arvin

Collective motion is one of the most fascinating phenomena observed in the nature. In the last decade, it aroused so much attention in physics, control and robotics fields. In particular, many studies have been done in swarm robotics related to collective motion, also called flocking. In most of these studies, robots use orientation and proximity of their neighbors to achieve collective motion. In such an approach, one of the biggest problems is to measure orientation information using on-board sensors. In most of the studies, this information is either simulated or implemented using communication. In this paper, to the best of our knowledge, we implemented a fully autonomous coordinated motion without alignment using very simple Mona robots. We used an approach based on Active Elastic Sheet (AES) method. We modified the method and added the capability to enable the swarm to move toward a desired direction and rotate about an arbitrary point. The parameters of the modified method are optimized using TCACS optimization algorithm. We tested our approach in different settings using Matlab and Webots.