Neuroevolution of Decentralized Decision-Making in N-Bead Swimmers Leads to Scalable and Robust Collective Locomotion (2407.09438v2)

Abstract: Many microorganisms swim by performing larger non-reciprocal shape deformations that are initiated locally by molecular motors. However, it remains unclear how decentralized shape control determines the movement of the entire organism. Here, we investigate how efficient locomotion emerges from coordinated yet simple and decentralized decision-making of the body parts using neuroevolution techniques. Our approach allows us to investigate optimal locomotion policies for increasingly large microswimmer bodies, with emerging long-wavelength body shape deformations corresponding to surprisingly efficient swimming gaits. The obtained decentralized policies are robust and tolerant concerning morphological changes or defects and can be applied to artificial microswimmers for cargo transport or drug delivery applications without further optimization "out of the box". Our work is of relevance to understanding and developing robust navigation strategies of biological and artificial microswimmers and, in a broader context, for understanding emergent levels of individuality and the role of collective intelligence in Artificial Life.

• The paper demonstrates that decentralized decision-making via neuroevolution yields efficient swimming in N-bead microswimmers, achieving up to 1.5% hydrodynamic efficiency.
• It utilizes ANN-based control and evolutionary algorithms to coordinate individual bead movements in a low Reynolds number regime.
• The findings highlight potential applications in bio-inspired robotics, paving the way for robust synthetic microswimmers in drug delivery and environmental sensing.

Emergent Navigation Policies through Decentralized Decision-Making in Microswimmers

The paper "Emergent Navigation Policies through Decentralized Decision-Making in Microswimmers" by Benedikt Hartl, Michael Levin, and Andreas Zöttl presents a comprehensive paper on the locomotion of microscale swimmers, exploring how decentralized decision-making can yield efficient swimming strategies. Through the use of neuroevolution techniques, the authors have developed a framework that allows individual components of a microswimmer to coordinate autonomously to achieve effective propulsion.

Technical Overview

The paper is centered on a model of microswimmers consisting of multiple spherical beads connected linearly, known as the Najafi-Golestanian (NG) swimmer model. The microswimmers operate in the low Reynolds number regime where viscous forces dominate inertial forces, making non-reciprocal movements crucial for achieving locomotion. Instead of relying on a centralized control system, this research investigates the potential of decentralized control, where each bead functions as an independent agent with local perception and decision-making capabilities.

The researchers used Artificial Neural Networks (ANNs) to simulate the decision-making processes of each bead. These ANNs are trained using evolutionary algorithms to optimize the coordination of the beads' movements, allowing the entire microswimmer to achieve efficient locomotion. The paper explores two types of force regularization strategies for maintaining the net zero-force condition required by the physics of the low Reynolds number environment. These strategies determine how proposed forces by individual beads translate into actual forces that drive the swimmer.

Results and Discussion

The results demonstrate that decentralized decision-making can achieve high-efficiency swimming strategies, with the simulation of up to 100-bead systems showcasing effective locomotion patterns. The efficiency of these strategies is quantified by the hydrodynamic efficiency, with type B microswimmers achieving efficiencies up to 1.5%, a notable figure comparable to real biological microorganisms.

Further analysis reveals that long-wavelength collective motions are more efficient than short-wavelength, high-frequency body deformations. The paper identifies emergent behaviors where large-scale orchestrated body contractions lead to faster propulsion, leveraging the collective intelligence of the microswimmers. This finding is consistent with observations in biological systems, where large-scale coordination often results in robust and adaptive behaviors.

Implications and Future Directions

The implications of these findings are significant for the field of bio-inspired robotics and synthetic biology. The demonstrated robustness and adaptability of decentralized control systems make this approach particularly appealing for the development of artificial microswimmers designed for applications such as targeted drug delivery and environmental sensing.

The ability of these microswimmers to adapt to morphologic changes, such as carrying additional cargo beads without requiring retraining, underscores the potential of decentralized systems in environments where flexibility and resilience are critical. Future work could focus on expanding the applicability of this framework to more complex swimmer morphologies and exploring other forms of collective intelligence in micro-robotic systems.

In summary, this paper contributes a valuable perspective on the potential of decentralized decision-making frameworks in microscalers, providing insights into both fundamental biological processes and practical applications in robotic design. The research opens avenues for developing highly adaptable and efficient microscale navigation systems, heralding advancements in biotechnology and synthetic organism design.