Design and analysis of proximate mechanisms for cooperative transport in real robots

Mohammed Alkilabi, Muhanad H., Narayan, Aparajit and Tuci, Elio (2016) Design and analysis of proximate mechanisms for cooperative transport in real robots. In: ANTS 2016:10th International Conference on Swarm Intelligence, 07-09 Sept 2016, Brussels, Belgium.

[img]
Preview
PDF - Final accepted version (with author's formatting)
Download (922kB) | Preview

Abstract

This paper describes a set of experiments in which a homogeneous group of real e-puck robots is required to coordinate their actions in order to transport cuboid objects that are too heavy to be moved by single robots. The agents controllers are dynamic neural networks synthesised through evolutionary computation techniques. To run these experiments, we designed, built, and mounted on the robots a new sensor that returns the agent displacement on the x/y plane. In this object transport scenario, this sensor generates useful feedback on the consequences of the robot actions, helping the robots to perceive whether their pushing forces are aligned with the object movement. The results of our experiments indicated that the best evolved controller can effectively operate on real robots. The group transport strategies turned out to be robust and scalable to effectively operate in a variety of conditions in which we vary physical characteristics of the object and group cardinality. From a biological perspective, the results of this study indicate that the perception of the object movement could explain how natural organisms manage to coordinate their actions to transport heavy items.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Mohammed Alkilabi M.H., Narayan A., Tuci E. (2016) Design and Analysis of Proximate Mechanisms for Cooperative Transport in Real Robots. In: Dorigo M. et al. (eds) Swarm Intelligence. ANTS 2016. Lecture Notes in Computer Science, vol 9882. Springer, Cham
Research Areas: A. > School of Science and Technology > Computer Science > Artificial Intelligence group
Item ID: 21938
Notes on copyright: The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-44427-7_9
Depositing User: Elio Tuci
Date Deposited: 13 Jun 2017 14:30
Last Modified: 04 Apr 2019 13:59
URI: https://eprints.mdx.ac.uk/id/eprint/21938

Actions (login required)

Edit Item Edit Item

Full text downloads (NB count will be zero if no full text documents are attached to the record)

Downloads per month over the past year