Group Transport of an Object to a Target That Only Some Group Members May Sense
This paper addresses the cooperative transport of a heavy object, called prey, towards a sporadically changing target location by a group of robots. The study is focused on the situation in which some robots are given the opportunity to localize the target, while the others (called the blind ones) are not. We propose the use of relatively simple robots capable of self-assembling into structures which pull or push the prey. To enable a blind robot to contribute to the group’s performance, it can locally perceive traction forces, and whether it is moving or not. The robot group is controlled in a distributed manner, using a modular control architecture. A collection of simple hand-coded and artificially evolved control modules is presented and discussed. For group sizes ranging from 2 to 16 and different proportions of blind robots within the group, it is shown that controlled by an evolved solution, blind robots make an essential contribution to the group’s performance.
The study is carried out using a physics-based simulation of a real robotic system that is currently under construction.
KeywordsRobotic System Transport Performance Green Object Robot Group Group Transport
Unable to display preview. Download preview PDF.
- Beyer, H.-G.: The Theory of Evolution Strategies. Springer, Berlin (2001)Google Scholar
- Deneubourg, J.-L., Goss, S., Sandini, G., Ferrari, F., Dario, P.: Self-organizing collection and transport of objects in unpredictable environments. In: Proc. of Japan – U.S.A Symp. on Flexible Automation, Kyoto, Japan. ISCIE, pp. 1093–1098 (1990)Google Scholar
- Kube, C.R., Zhang, H.: Stagnation recovery behaviours for collective robotics. In: 1994 IEEE/RSJ/GI Int. Conf. on Intelligent Robotics and Systems, pp. 1883–1890. IEEE Computer Society Press, Los Alamitos (1995)Google Scholar
- Parker, L.E.: Current state of the art in distributed autonomous mobile robotics. In: Distributed Autonomous Robotic System, Tokyo, Japan, vol. 4, pp. 3–12. Springer, Heidelberg (2000)Google Scholar
- Schwefel, H.-P.: Evolutionsstrategie und numerische Optimierung. PhD thesis, Technische Universität Berlin, Germany (1975)Google Scholar