Get in touch: cooperative decision making based on robot-to-robot collisions

Abstract

We demonstrate the ability of a swarm of autonomous micro-robots to perform collective decision making in a dynamic environment. This decision making is an emergent property of decentralized self-organization, which results from executing a very simple bio-inspired algorithm. This algorithm allows the robotic swarm to choose from several distinct light sources in the environment and to aggregate in the area with the highest illuminance. Interestingly, these decisions are formed by the collective, although no information is exchanged by the robots. The only communicative act is the detection of robot-to-robot encounters. We studied the performance of the robotic swarm under four environmental conditions and investigated the dynamics of the aggregation behaviour as well as the flexibility and the robustness of the solutions. In summary, we can report that the tested robotic swarm showed two main characteristic features of swarm systems: it behaved flexible and the achieved solutions were very robust. This was achieved with limited individual sensor abilities and with low computational effort on each single robot in the swarm.

Biomimetic tactile target acquisition, tracking and capture

“…This single observation provides a strong motivation to the roboticist to understand biological systems and to exploit the solutions to which they have converged [3,4] (including to understand how biological solutions relate to probabilistic solutions [5]). This motivation is inspiring entirely novel (or, 'unconventional') approaches to mechanics [6], locomotion [7], sensing [8] and decision-making [9], to give only a handful of recent examples. At the same time, 'embodied' (i.e.…”

Power-Law Distribution of Long-Term Experimental Data in Swarm Robotics

“…We use BEECLUST method [18] as the aggregation scenario. In BEECLUST, robots move randomly in the environment.…”

“…Results of the performed experiments showed that robots are able to aggregate on the optimal zone where the intensity of the light is the highest. Schmickl et al [18] proposed two types of experiments: (i) static experiments in which there is a single light source and (ii) dynamic experiments in which there are two light sources with different intensities. The intensities of the sources are changed during an experiment.…”

Imitation of Honeybee Aggregation with Collective Behavior of Swarm Robots.

“…Based on the evaluated individual behaviors of honeybees, 6,18 those behaviors are fitted in the described criteria of swarm robotics. 3 Inefficiency of a single bee to detect the optimal zone, simple perception of homogeneous individual agents, and effects of the population size are clearly illustrated in honeybee aggregation.…”