Collective Cognition on Global Density in Dynamic Swarm

Abstract

Swarm density plays a key role in the performance of a robot swarm, which can be averagely measured by swarm size and the area of a workspace. In some scenarios, the swarm workspace may not be fully or partially observable, or the swarm size may decrease over time due to out-of-battery or faulty individuals during operation. This can result in the average swarm density over the whole workspace being unable to be measured or changed in real-time. The swarm performance may not be optimal due to unknown swarm density. If the swarm density is too low, inter-robot communication will rarely be established, and robot swarm cooperation will not be effective. Meanwhile, a densely-packed swarm compels robots to permanently solve collision avoidance issues rather than performing the main task. To address this issue, in this work, the distributed algorithm for collective cognition on the average global density is proposed. The main idea of the proposed algorithm is to help the swarm make a collective decision on whether the current global density is larger, smaller or approximately equal to the desired density. During the estimation process, the swarm size adjustment is acceptable for the proposed method in order to reach the desired swarm density.

Keywords: collective cognition; collective decision making; hard-core point process; swarm robots.

Figure 1

(Left): the geometric parameters of the robot; (right): the geometric parameters between robots.

Figure 2

(Left): the black shaded region is the lens formed by the intersection of $b\left(x_i,\delta \right)$ and $b\left(x_j,\delta \right)$; (right): the orange shaded region is the lens formed by the intersection of $b\left(x_i,r\right)$ and $b\left(x_j,\delta \right)$.

Figure 3

The whole process of proposed method.

Figure 4

The robot platform utilized in this research is depicted in the image. On the left-hand side, there is a top view of the robot platform, including the IR-module with annotation numbers ranging from 0 to 5 that indicate their order and local frame notations. On the right-hand side, there is a side view of the robot platform.

Figure 5

The results of experiments conducted to evaluate the convergence of the estimated encounter rate in the swarm are presented for a static swarm size scenario. The experiments were conducted for a swarm size of 20 (top-left), 40 (top-right), and 60 (bottom-left).

Figure 6

The snapshots of the second experiments used to evaluate the performance of the proposed method on swarms with varying sizes are shown. From top to bottom, the number of robots in the workspace are 20, 40 and 60, respectively.

Figure 7

The recording of range of estimated encounter rate $range({\widehat{\mathsf{\pi }}}_e)$, mean of estimated encounter rate $E({\widehat{\mathsf{\pi }}}_e)$ and desired encounter rate ${\pi }_e$ over time during the experiment.

Figure 8

Number of robots at each decision and total number of robots in the swarm at each time point of the experiment.