This paper, the cooperative tracking control problem of second-order nonlinear multi-agent systems with leaders under DoS attacks is studied. The distributed control protocol and impulsive controller are designed based on the neighborhood state. Combined with graph theory, stability theory and Lyapunov method, the sufficient conditions for the system to achieve cooperative tracking are derived, including the constraints of control gain, attack frequency and attack duration. This method corrects the error by pulse control at the moment of attack, and effectively suppresses the performance degradation during the attack. Finally, the stability of the system under DoS attack is verified by theoretical analysis.
Following an environmentally constrained path in a timely fashion can be crucial for swarms in scenarios such as disaster response or emergency evacuation. In such situations, swarms must rapidly follow potentially challenging paths while not losing cohesion. We benchmark a robust, decentralized gradient-following behavior against varying path sinuosity, and swarm size. The swarm races to reach a minimum distance in a fixed time budget. We measure the success rate and the mean completion time. Our findings show that the algorithm allows the swarm to successfully reach the target line in the allotted time as long as the swarm size is relatively small. Furthermore, we find that the algorithm handles low and medium degrees of sinuosity well but struggles with high sinuosity, where the turns become too sharp. This work is the first to study swarm racing in constrained environments, revealing that “more is not always better”: larger swarms hinder rapid path traversal. This paves the way for future research on scale-invariant racing collective behaviors.
Fluid-like volumetric collective motion in drone swarms with navigation, obstacle avoidance, and formation maintenance remains challenging. Existing potential-field-based approaches often lack stability guarantees and are largely restricted to two-dimensional formations. This paper proposes Active Elastic Matter, an extension of the Active Elastic Sheet method that (i) generalizes the elastic interaction model to volumetric 3D formations and (ii) incorporates estimated relative velocities and accelerations using an Extended Kalman Filter. These predictive interaction terms improve stability and robustness in dynamic environments. Simulation results demonstrate that Active Elastic Matter enables 3D formations to navigate narrow passages and dynamic obstacles while maintaining higher order and lower entropy compared to Active Elastic Sheet. Real-world experiments with Crazyflie drones further validate improved stability during narrow passage navigation.
The tendency to favour members of one’s own group, also known as in-group bias, is ubiquitous in social mammals from rats to humans. This preference for in-group members (which often translates into aggression against out-group members) plays a major role in mammalian social life, and governs how individuals and groups perceive and behave toward others. Understanding how such social dynamics emerge, evolve, and impact behaviour is crucial for evolutionary theory and for the design of more mammal-like agents. Yet current swarm robots are typically extremely social and cooperative, and do not differentiate between individual robots. Here, we introduce two cognitive and social layers to a swarm engaged in a foraging task and a communication game: robot individuation (a partner-specific memory) and evolving sociality (a partner-specific tendency to interact based on previous interactions). We test whether social asymmetries such as in-group bias can spontaneously emerge from repeated local interactions without implicit group markers or pre-assigned identity tags, and study its strength and stability over varying conditions: cognitive (memory size and degree of decay), social (group size and innate sociality) and environmental (spread of resources). We show that implementing these two mammal-like features leads to the spontaneous emergence of in-group bias, which is robust across ecologies. These results shed light on the evolution of social asymmetries, and pave the way for modelling the complex social dynamics of mammals.
Tumblenauts are a swarm of minimalist, bacteria-inspired robots designed for collaborative inspection of pressurized microgravity habitats such as the International Space Station. Unlike current intra-vehicular robots that rely on complex, actuator-dense mechanisms for precise motion, the Tumblenauts use a stochastic run-and-tumble locomotion inspired by bacterial motility. This unique locomotion paradigm enables a simpler design, improves scalability, and greatly reduces actuation requirements, making the Tumblenauts among the smallest and least actuator-dense robots built for microgravity. In this paper, we present the design of the Tumblenaut, describe how it achieves run-and-tumble locomotion, and characterize its motion dynamics using Earth-based microgravity testbeds. Furthermore, using a data-driven simulation, we demonstrate how the Tumblenauts can perform a diverse set of inspection tasks by leveraging collective behavior. Specifically, we show that the swarm can collaboratively map environments, achieve directed navigation through a chemotaxis-inspired control mechanism, and make global inspection classification decisions by sharing information. As a new generation of space habitats is launched into orbit, we envision swarms of Tumblenauts run-and-tumbling within them, providing continuous monitoring and supporting the long-term sustainability of these stations.
