Synchronization and swarming are canonical manifestations of self-organization, observable across scales from cellular processes to animal flocks. This study investigates the collective dynamics of a novel agent-based model where individuals exhibit both spatial mobility and internal, two-mode stochastic oscillatory states. By introducing a local, distance-dependent coupling between the agents' spatial configuration and their internal state transitions, we establish a mutual feedback loop that drives complex pattern formation. Through large-scale numerical simulations, we identify seven distinct morphological configurations, ranging from stationary \textit{Filled-disk} states to highly disordered \textit{Intense-motion} regimes. By performing a rigorous quantitative analysis of the rotational energy and radial dispersion, we transcend simple morphological classification and demonstrate that the system organizes into discrete, quantized topological attractors. We derive a macroscopic scaling law, $Ω\propto r^{-1/2}$, which proves that the emerging rotating states are not rigid-body rotations, but rather composite differential vortex structures characterized by spontaneous chiral symmetry breaking. Our results suggest that these stable, quantized dynamical states are fundamental features of systems governed by bidirectional spatial-phase feedback, offering a robust framework for designing autonomous, decentralized robotic swarms.
Ayush M. Patil, Atharv S. Pakmode, Rishi Jain, Rupali D. Rode · 5 authors
Swarm robotics is a broad area of study that looks at the collective behavior of many autonomous robots to complete challenging tasks. Swarm robotics attempts to take advantage of the power of decentralization and self-organization to achieve adaptability, scalability, and robustness in robotic systems by taking inspiration from the collective behavior of natural swarms, such as ants, bees, and schools of fish.
In Ho Kim, Subi Choi, Jieun Lee, Jiyoung Jung · 11 authors
Artificial muscles are indispensable components for next-generation robotics capable of mimicking sophisticated movements of living systems. However, an optimal combination of actuation parameters, including strain, stress, energy density and high mechanical strength, is required for their practical applications. Here we report mammalian-skeletal-muscle-inspired single fibres and bundles with large and strong contractive actuation. The use of exfoliated graphene fillers within a uniaxial liquid crystalline matrix enables photothermal actuation with large work capacity and rapid response. Moreover, the reversible percolation of graphene fillers induced by the thermodynamic conformational transition of mesoscale structures can be in situ monitored by electrical switching. Such a dynamic percolation behaviour effectively strengthens the mechanical properties of the actuator fibres, particularly in the contracted actuation state, enabling mammalian-muscle-like reliable reversible actuation. Taking advantage of a mechanically compliant fibre structure, smart actuators are readily integrated into strong bundles as well as high-power soft robotics with light-driven remote control.
Achieving efficient and reliable self-organization in groups of autonomous robots is a fundamental challenge in swarm robotics. Even simple states of collective motion, such as group translation or rotation, require nontrivial algorithms, sensors, and actuators to be achieved in real-world scenarios. We study here the capabilities and limitations in controlling experimental robot swarms of a decentralized control algorithm that only requires information on the positions of neighboring agents, and not on their headings. Using swarms of e-Puck robots, we implement this algorithm in experiments and show its ability to converge to self-organized collective translation or rotation, starting from a state with random orientations. Through a simple analytical calculation, we also unveil an essential limitation of the algorithm that produces small persistent oscillations of the aligned state, related to its marginal stability. By comparing predictions and measurements, we compute the experimental noise distributions of the linear and angular robot speeds, showing that they are well described by Gaussian functions. We then implement simulations that model this noise by adding Gaussian random variables with the experimentally measured standard deviations. These simulations are performed for multiple parameter combinations and compared to experiments, showing that they provide good predictions for the expected speed and robustness of the self-organizing dynamics.
Engineers are torn between an attitude of strong design and dreams of autonomous devices. They want full mastery of their artifacts while wishing these were much more adaptive or “intelligent.” Today, while we must still spoon-feed (program, repair, upgrade) our most sophisticated computer and robotic systems, insatiable demand for novelty has created an escalation in system size and complexity. In this context, the tradition of rigid top-down planning and implementation in every detail has become unsustainable. Natural complex systems, large sets of elements interacting locally and producing nontrivial collective behaviors, offer a powerful alternative and source of innovative ideas. Going beyond metaheuristic disciplines based on “neurons” (machine learning), “genes” (genetic algorithms), or “ants” (ant colony optimization), this article highlights a new avenue of bioinspired engineering that simulates the growth of fine-grained multicellular organisms. It presents a brief overview of morphogenetic engineering and one of its instances, embryomorphic engineering, which are two fields that explore the decentralized self-organization of artificial complex morphologies and behaviors. MapDevo3D, an embryomorphic engineering model of developmental animats in a 3D virtual physics world, is described in more detail. Bodies are composed of several hundreds of cells, giving them a quasi-continuous texture close to the tenets of “soft robotics.” Motion results from local muscle twitching without a central nervous system. Altogether, the challenge is not to build a system directly but find the rules that its components must follow to build it for us.
Toshio Fukuda, Go Iritani, T. Ueyama, Fumihito Arai
This paper deals with a self-organizing robotic system that consists of a number of autonomous robots. We describe the idea of society of robots, which is based on decentralized autonomous systems such as human being or insects. As one of the self-organizing robotic systems, cellular robotic system (CEBOT) was proposed by T. Fukuda. The cellular robotic system is one of the distributed robotic systems, which is composed of a number of autonomous robotic units called cells. The cell is a fundamental unit that has a simple function. To carry out given tasks, the cells connect each other and cooperate mutually. This paper denotes the concept of the cellular robotic system (CEBOT), the social organization of robotic systems, and evolution of group systems.