Swarm Synergy: A Silent and Anonymous Way of Forming Community (2311.17697v2)

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.

• The paper introduces a decentralized algorithm that enables robots to form communities solely through local sensor inputs without explicit communication.
• Experimental and simulation results highlight key conditions like maintaining a safe goal distance and avoiding collinear arrangements to ensure collision-free synergy.
• The approach improves security and scalability in constrained settings, promising practical applications in areas such as underwater exploration and military operations.

Swarm Synergy: A Silent Way of Forming Community

The paper "Swarm Synergy: A Silent Way of Forming Community," authored by Sweksha Jain, Rugved Katole, and Leena Vachhani from the Indian Institute of Technology Bombay, presents an innovative approach to swarm robotics. The principal focus is on an algorithm enabling a swarm of robots to form communities without inter-agent communication, leveraging only local sensory inputs and individual decision-making.

Overview

This research explores a scenario where robots, considered "silent agents," make independent decisions based solely on their sensors to form communities. The lack of communication capabilities among robots circumvents potential vulnerabilities such as hijacking or spoofing. The paper demonstrates that these silent agents can achieve self-organized behavior and form robust communities without predefined notions about the number of communities, their size, or specific membership, purely using environmental sensory inputs.

Methodology

The authors propose a decentralized, communication-free algorithm based on local parameters for each robot. Each robot uses onboard sensors to comprehend its environment, detect nearby robots, and dynamically adjust goals to converge into communities. The robots are programmed to navigate towards goals determined by the positions of their neighbors, which they identify using a range sensor with a specified field of view (FoV). If a robot reaches a goal and meets the predefined conditions for community size, it stops. Otherwise, it seeks a new goal.

Analytical Insights

Key results discussed in the paper include:

• Necessary Conditions for Synergy: A minimum condition for robots to synergize is that the goal distance ($D_g$) must be greater than the safe distance ($D_s$) to avoid collisions.
• Collinearity Avoidance: The algorithm ensures robots do not stop in collinear arrangements if the sensing range ($D_m$) is sufficiently larger than the goal distance ($D_g$).
• Community Bound: The algorithm inherently limits the maximum number of communities based on the swarm size ($S$) and minimum community size ($M$), supporting more compact formations.

Experimental Results

The paper presents both simulation and real-world experimental results to validate the algorithm:

• Simulations: Simulations carried out in Gazebo show the formation of communities from initial random positions. Different sensing ranges and boundary areas are tested to understand their effects on synergy time, with results indicating that these variables do not linearly affect system performance due to dynamic goal allocation and swarm sizes.
• Experiments: Real-world experiments using Turtlebot3 burgers confirm the algorithm's effectiveness, demonstrating that robots without initial visibility of each other or entering an existing community can successfully form stable communities without collisions.

Comparative Analysis

The proposed algorithm is compared with the population dynamics model and the k-means clustering algorithm. The silent approach shows comparative performance in synergy time and is advantageous due to its independence from communication, which enhances security and robustness.

Implications and Future Work

The research opens substantial avenues for practical applications in environments where communication is constrained or risky, such as underwater exploration or military operations. The demonstration of untraceable community formation through multiple simulation trials underscores the unique security benefits of the method.

Moving forward, expanding this approach to more complex scenarios involving obstacles and larger swarm sizes, while maintaining low synergy times and improving scalability, could significantly enhance practical deployments. Furthermore, extending silent methodologies to other swarm behaviors like aggregation and pattern formation in communication-denied environments could broaden the utility of such systems.

Conclusion

The algorithm developed in this paper provides a novel, effective approach to forming robot communities without communication, demonstrating robustness, scalability, and untraceability features that are critical in secure and constrained environments. As such, it represents a significant contribution to the field of swarm robotics.