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1. Consensus, cooperative learning, and flocking for multiagent predator avoidance

   https://doi.org/10.1177/1729881420960342  

   Young, Zachary; La, Hung Manh (September 2020, International Journal of Advanced Robotic Systems)

   null (Ed.)

   Multiagent coordination is highly desirable with many uses in a variety of tasks. In nature, the phenomenon of coordinated flocking is highly common with applications related to defending or escaping from predators. In this article, a hybrid multiagent system that integrates consensus, cooperative learning, and flocking control to determine the direction of attacking predators and learns to flock away from them in a coordinated manner is proposed. This system is entirely distributed requiring only communication between neighboring agents. The fusion of consensus and collaborative reinforcement learning allows agents to cooperatively learn in a variety of multiagent coordination tasks, but this article focuses on flocking away from attacking predators. The results of the flocking show that the agents are able to effectively flock to a target without collision with each other or obstacles. Multiple reinforcement learning methods are evaluated for the task with cooperative learning utilizing function approximation for state-space reduction performing the best. The results of the proposed consensus algorithm show that it provides quick and accurate transmission of information between agents in the flock. Simulations are conducted to show and validate the proposed hybrid system in both one and two predator environments, resulting in an efficient cooperative learning behavior. In the future, the system of using consensus to determine the state and reinforcement learning to learn the states can be applied to additional multiagent tasks. 

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2. Asynchronous Quasi-Consensus of Heterogeneous Multiagent Systems With Nonuniform Input Delays

   https://doi.org/10.1109/TSMC.2018.2834823  

   Wang, Zhengxin; He, Haibo (October 2018, IEEE Transactions on Systems, Man, and Cybernetics: Systems)

   This paper analyzes the consensus problem in heterogenous nonlinear multiagent systems. The multiagent systems not only have nonidentical nonlinear dynamics for all agents, but also have different network topologies for position and velocity interactions. An asynchronous sampled-data control without any input delays is first proposed, the information of each agent is only sampled at its own sampling instants and need not be sampled at other sampling instants. Then, quasi-consensus in heterogenous multiagent systems is proved by Lyapunov stability theory. When asynchronous sampled-data control has nonuniform input delays, sufficient conditions for quasi-consensus in heterogenous multiagent systems are further obtained. The upper bound of quasi-consensus errors is estimated. Finally, numerical simulations are provided to verify the effectiveness of theoretical results. 

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3. Human Crowds as Social Networks: Collective Dynamics of Consensus and Polarization

   https://doi.org/10.1177/17456916231186406  

   Warren, William_H; Falandays, J_Benjamin; Yoshida, Kei; Wirth, Trenton_D; Free, Brian_A (August 2023, Perspectives on Psychological Science)

   A ubiquitous type of collective behavior and decision-making is the coordinated motion of bird flocks, fish schools, and human crowds. Collective decisions to move in the same direction, turn right or left, or split into subgroups arise in a self-organized fashion from local interactions between individuals without central plans or designated leaders. Strikingly similar phenomena of consensus (collective motion), clustering (subgroup formation), and bipolarization (splitting into extreme groups) are also observed in opinion formation. As we developed models of crowd dynamics and analyzed crowd networks, we found ourselves going down the same path as models of opinion dynamics in social networks. In this article, we draw out the parallels between human crowds and social networks. We show that models of crowd dynamics and opinion dynamics have a similar mathematical form and generate analogous phenomena in multiagent simulations. We suggest that they can be unified by a common collective dynamics, which may be extended to other psychological collectives. Models of collective dynamics thus offer a means to account for collective behavior and collective decisions without appealing to a priori mental structures. 

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4. Manipulation of Single Cells Using a Ferromagnetic Nanorod Cluster Actuated by Weak AC Magnetic Fields

   https://doi.org/10.1002/adbi.201800246  

   Zhu, Lu; Huang, Weijie; Yang, Fengchang; Yin, Lei; Liang, Shenxuan; Zhao, Wujun; Mao, Leidong; Yu, Xiaozhong; Qiao, Rui; Zhao, Yiping (October 2018, Advanced Biosystems)

   Abstract A unique noncontact single cell manipulation technique based on the actuation of magnetic nanorods (MNRs) or clusters (MCs) by nonuniform alternating magnetic fields (nuAMFs) is demonstrated. Compared to the actuation of MNRs/MCs by conventional magnetophoresis, the motion of MNRs/MCs actuated by nuAMFs can be tuned by additional parameters including the shape of MNRs/MCs and the frequency of the applied magnetic fields. The manipulation of a single cell by an actuated MNR/MC are divided into five stages, i.e., approaching, pushing, carrying, dragging, and releasing. The interactions between the MNR/MC and the cell in these stages are investigated in detail both experimentally and numerically. Other applications of cell manipulation, such as concentrating cells at target locations and accumulating MNRs/MCs onto a single cell, are also demonstrated. The single cell manipulation system is simple, low‐cost, and low‐power consumption, and helps advance the state‐of‐the‐art of single‐particle manipulation. 

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5. Distributed Consensus on Manifolds using the Riemannian Center of Mass

   Spencer Kraisler, Shahriar Talebi (July 2023, Control Technology and Applications)

   In this paper, we develop a distributed consensus algorithm for agents whose states evolve on a manifold. This algorithm is complementary to traditional consensus, predominantly developed for systems with dynamics on vector spaces. We provide theoretical convergence guarantees for the proposed manifold consensus provided that agents are initialized within a geodesically convex (g-convex) set. This required condition on initialization is not restrictive as g-convex sets may be comparatively “large” for relevant Riemannian manifolds. Our approach to manifold consensus builds upon the notion of Riemannian Center of Mass (RCM) and the intrinsic structure of the manifold to avoid projections in the ambient space. We first show that on a g-convex ball, all states coincide if and only if each agent’s state is the RCM of its neighbors’ states. This observation facilitates our convergence guarantee to the consensus submanifold. Finally, we provide simulation results that exemplify the linear convergence rate of the proposed algorithm and illustrates its statistical properties over randomly generated problem instances. 

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