Swarm Synergy: A Silent and Anonymous Way of Forming Community

TL;DR

The paper presents swarm synergy, a fully decentralized, communication-free algorithm for forming anonymous communities in swarms using only onboard sensing. Each robot detects neighbors within its FoV, assigns a goal based on the centroid of nearby agents, and navigates toward that goal while enforcing collision avoidance, stopping only when a minimum community size $M$ is reached. Key contributions include a formal neighbor-detection and centroid-based goal-assignment framework, analysis of untraceability, and validation through Gazebo simulations and real TurtleBot3 experiments, with favorable comparisons to baseline aggregation/clustering methods under similar conditions. The work offers a scalable, robust approach for crowd-like organization in environments where communication or localization is restricted, with potential applications in high-stress or dangerous scenarios such as fires or disasters.

Abstract

In this paper, we present a novel swarm algorithm, swarm synergy, designed for robots to form communities within a swarm autonomously and anonymously. These communities, characterized as clusters of robots, emerge without any (pre-defined or communicated) specific locations. Each robot operates as a silent agent, having no communication capability, making independent decisions based on local parameters. The proposed algorithm allows silent robots to achieve this self-organized swarm behavior using only sensory inputs from the environment. The robots intend to form a community by sensing the neighbors, creating synergy in a bounded environment. We further infer the behavior of swarm synergy to ensure the anonymity/untraceability of both robots and communities and show the results on dynamicity of various parameters relevant to swarm communities such as community size, community location, number of community, no specific agent structure in the community, etc. The results are further analysed to observe the effect of sensing limitations posed by the onboard sensor's field of view. Simulations and experiments are performed to showcase the algorithm's scalability, robustness, and fast convergence. Compared to the state-of-art with similar objectives, the proposed communication-free swarm synergy shows comparative time to synergize or form communities. The proposed algorithm finds applications in studying crowd dynamics under high-stress scenarios such as fire, attacks, or disasters.

Figures (8)

• Figure 1: Swarm synergy in the context of the existing literature
• Figure 2: Representation of Inter-robot detection, where $i, j, k$ represents the 2D robots with conic/restricted FoV with sensing range $R$, angle $2\Delta$, and a safe distance $D_s$. The sensed distance and bearing of robot $i$ with respect to robot $j$ are $d_{ij}$ and $\delta_{ij}$, respectively.
• Figure 3: Proposed Swarm Synergy with Algorithm Pipeline for Each Robot
• Figure 4: Non-collinearity condition with three robots, where small circles represent robot positions, cone represents their FoV, and 'x' mark represents the goal position.
• Figure 5: Formation of swarm communities for swarm size of 20, where blue cones represent the conic FoV of the robots.