Swarm behaviors offer scalability and robustness to failure through a decentralized and distributed design. When designing coherent group motion as in swarm flocking, virtual potential functions are a widely used mechanism to ensure the aforementioned properties. However, arbitrating through different virtual potential sources in real-time has proven to be difficult. Such arbitration is often affected by fine tuning of the control parameters used to select among the different sources and by manually set cut-offs used to achieve a balance between stability and velocity. A reliance on parameter tuning makes these methods not ideal for field operations of aerial drones which are characterized by fast non-linear dynamics hindering the stability of potential functions designed for slower dynamics. A situation that is further exacerbated by parameters that are fine-tuned in the lab is often not appropriate to achieve satisfying performances on the field. In this work, we investigate the problem of dynamic tuning of local interactions in a swarm of aerial vehicles with the objective of tackling the stability-velocity trade-off. We let the focal agent autonomously and adaptively decide which source of local information to prioritize and at which degree-for example, which neighbor interaction or goal direction. The main novelty of the proposed method lies in a Gaussian kernel used to regulate the importance of each element in the swarm scheme. Each agent in the swarm relies on such a mechanism at every algorithmic iteration and uses it to tune the final output velocities. We show that the presented approach can achieve cohesive flocking while at the same time navigating through a set of way-points at speed. In addition, the proposed method allows to achieve other desired field properties such as automatic group splitting and joining over long distances. The aforementioned properties have been empirically proven by an extensive set of simulated and field experiments, in communication-full and communication-less scenarios. Moreover, the presented approach has been proven to be robust to failures, intermittent communication, and noisy perceptions.

• Klíčová slova
• field experiments and simulations, flocking, swarm (methodology), swarm robot control, unmanned aerial vehicle,
• Publikační typ
• časopisecké články MeSH

This work introduces the Advanced Multi-Objective Salp Swarm Algorithm Exploration Technique (AMET), which is a novel optimization framework designed to enhance the efficiency and robustness of multi-robot exploration. AMET combines the deterministic structure of Coordinated Multi-Robot Exploration (CME) with the adaptive search capabilities of the Multi-Objective Salp Swarm Algorithm (MSSA) to achieve a balanced trade-off between exploration efficiency and mapping accuracy. To validate its effectiveness, AMET is compared to both multi-objective and single-objective exploration strategies, including CME combined with Multi-Objective Grey Wolf Optimizer (CME-MGWO), Multi-Objective Ant Colony Optimization (CME-MACO), Multi-Objective Dragonfly Algorithm (CME-MODA), and the single-objective CME with traditional Salp Swarm Algorithm (CME-SSA). The evaluation focuses on four critical performance metrics: runtime efficiency, exploration area coverage, mission completion resilience, and the reduction of redundant exploration. Experimental results across multiple case studies demonstrate that AMET consistently outperforms both single-objective and multi-objective counterparts, achieving superior area coverage, reduced computational overhead, and enhanced exploration coordination. These findings highlight the potential of AMET as a scalable and efficient approach for robotic exploration, providing a foundation for future advancements in multi-robot systems. The proposed method opens new possibilities for applications in search-and-rescue operations, planetary surface exploration, and large-scale environmental monitoring.

• Klíčová slova
• Area coverage, Artificial Intelligence, Autonomous robotic systems, Computational efficiency, Multi-objective optimization, Multi-robot exploration, Salp swarm algorithm,
• Publikační typ
• časopisecké články MeSH

This article proposes Persistence Administered Collective Navigation (PACNav) as an approach for achieving the decentralized collective navigation of unmanned aerial vehicle (UAV) swarms. The technique is based on the flocking and collective navigation behavior observed in natural swarms, such as cattle herds, bird flocks, and even large groups of humans. As global and concurrent information of all swarm members is not available in natural swarms, these systems use local observations to achieve the desired behavior. Similarly, PACNav relies only on local observations of the relative positions of UAVs, making it suitable for large swarms deprived of communication capabilities and external localization systems. We introduce the novel concepts ofpath persistenceandpath similaritythat allow each swarm member to analyze the motion of other members in order to determine its own future motion. PACNav is based on two main principles: (a) UAVs with little variation in motion direction have highpath persistence, and are considered by other UAVs to be reliable leaders; (b) groups of UAVs that move in a similar direction have highpath similarity, and such groups are assumed to contain a reliable leader. The proposed approach also embeds a reactive collision avoidance mechanism to avoid collisions with swarm members and environmental obstacles. This collision avoidance ensures safety while reducing deviations from the assigned path. Along with several simulated experiments, we present a real-world experiment in a natural forest, showcasing the validity and effectiveness of the proposed collective navigation approach in challenging environments. The source code is released as open-source, making it possible to replicate the obtained results and facilitate the continuation of research by the community.

• Klíčová slova
• decentralized control, multi-robot systems, relative localization, swarm robotics, unmanned aerial vehicles,
• MeSH
• bezpilotní létající prostředky MeSH
• komunikace MeSH
• letadla * MeSH
• lidé MeSH
• robotika * MeSH
• skot MeSH
• zvířata MeSH
• Check Tag
• lidé MeSH
• skot MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

The multibody dynamics of a system of chemical swarm robots in a porous environment is investigated. The chemical swarm robots are modeled as brownian particles capable of delivering an encapsulated chemical payload toward a given target location and releasing it in response to an external stimulus. The presence of chemical signals (chemo-attractant) in the system plays a crucial role in coordinating the collective movement of the particles via chemotaxis. For a number of applications, such as distributed chemical processing and targeted drug delivery, the understanding of factors that govern the collective behavior of the particles, especially their ability to localize a given target, is of immense importance. A hybrid modeling methodology based on the combination of the brownian dynamics method and diffusion problem coupled through the chemotaxis phenomena is used to analyze the impact of a varying signaling threshold and the strength of chemotaxis on the ability of the chemical robots to fulfill their target localization mission. The results demonstrate that the selected performance criteria (the localization half time and the success rate) can be improved when an appropriate signaling process is chosen. Furthermore, for an optimum target localization strategy, the topological complexity of the porous environment needs to be reflected.

• MeSH
• biologické modely * MeSH
• chemotaktické faktory farmakologie   MeSH
• chemotaxe účinky léků   MeSH
• poréznost MeSH
• stochastické procesy MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• chemotaktické faktory MeSH

This article presents a unique framework for deploying decentralized and infrastructure-independent swarms of homogeneous aerial vehicles in the real world without explicit communication. This is a requirement in swarm research, which anticipates that global knowledge and communication will not scale well with the number of robots. The system architecture proposed in this article employs the UVDAR technique to directly perceive the local neighborhood for direct mutual localization of swarm members. The technique allows for decentralization and high scalability of swarm systems, such as can be observed in fish schools, bird flocks, or cattle herds. The bio-inspired swarming model that has been developed is suited for real-world deployment of large particle groups in outdoor and indoor environments with obstacles. The collective behavior of the model emerges from a set of local rules based on direct observation of the neighborhood using onboard sensors only. The model is scalable, requires only local perception of agents and the environment, and requires no communication among the agents. Apart from simulated scenarios, the performance and usability of the entire framework is analyzed in several real-world experiments with a fully-decentralized swarm of UAV deployed in outdoor conditions. To the best of our knowledge, these experiments are the first deployment of decentralized bio-inspired compact swarms of UAV without the use of a communication network or shared absolute localization. The entire system is available as open-source at https://github.com/ctu-mrs.

• Klíčová slova
• Distributed Control, Relative Localization, Swarm Robotics, Unmanned Aerial Vehicle,
• Publikační typ
• časopisecké články MeSH

Hybrid biological robots (biobots) prepared from living cells are at the forefront of micro-/nanomotor research due to their biocompatibility and versatility toward multiple applications. However, their precise maneuverability is essential for practical applications. Magnetotactic bacteria are hybrid biobots that produce magnetosome magnetite crystals, which are more stable than synthesized magnetite and can orient along the direction of earth's magnetic field. Herein, we used Magnetospirillum magneticum strain AMB-1 (M. magneticum AMB-1) for the effective removal of chlorpyrifos (an organophosphate pesticide) in various aqueous solutions by naturally binding with organic matter. Precision control of M. magneticum AMB-1 was achieved by applying a magnetic field. Under a programed clockwise magnetic field, M. magneticum AMB-1 exhibit swarm behavior and move in a circular direction. Consequently, we foresee that M. magneticum AMB-1 can be applied in various environments to remove and retrieve pollutants by directional control magnetic actuation.

• Klíčová slova
• magnetic actuation, magnetotactic bacteria, micromotors, microrobots, nanorobots,
• MeSH
• Bacteria metabolismus   MeSH
• bakteriální proteiny metabolismus   MeSH
• dekontaminace MeSH
• Magnetospirillum * metabolismus   MeSH
• oxid železnato-železitý * MeSH
• robotika metody   MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• bakteriální proteiny MeSH
• oxid železnato-železitý * MeSH

Digital and mechatronic methods, paired with artificial intelligence and machine learning, are transformative technologies in behavioral science and biology. The central element of the most important pollinator species-honey bees-is the colony's queen. Because honey bee self-regulation is complex and studying queens in their natural colony context is difficult, the behavioral strategies of these organisms have not been widely studied. We created an autonomous robotic observation and behavioral analysis system aimed at continuous observation of the queen and her interactions with worker bees and comb cells, generating behavioral datasets of exceptional length and quality. Key behavioral metrics of the queen and her social embedding within the colony were gathered using our robotic system. Data were collected continuously for 24 hours a day over a period of 30 days, demonstrating our system's capability to extract key behavioral metrics at microscopic, mesoscopic, and macroscopic system levels. Additionally, interactions among the queen, worker bees, and brood were observed and quantified. Long-term continuous observations performed by the robot yielded large amounts of high-definition video data that are beyond the observation capabilities of humans or stationary cameras. Our robotic system can enable a deeper understanding of the innermost mechanisms of honey bees' swarm-intelligent self-regulation. Moreover, it offers the possibility to study other social insect colonies, biocoenoses, and ecosystems in an automated manner. Social insects are keystone species in all terrestrial ecosystems; thus, developing a better understanding of their behaviors will be invaluable for the protection and even the restoration of our fragile ecosystems globally.

• MeSH
• audiovizuální záznam MeSH
• chování zvířat * MeSH
• design vybavení MeSH
• robotika * přístrojové vybavení   MeSH
• sociální chování * MeSH
• strojové učení MeSH
• umělá inteligence MeSH
• včely fyziologie   MeSH
• zvířata MeSH
• Check Tag
• ženské pohlaví MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Micro- and nanoplastic pollution is pervasive worldwide, infiltrating drinking water and food chains, accumulating in the human body, and posing serious threats to public health and ecosystems. Despite these urgent challenges, effective strategies to curb the widespread presence of micro- and nanoplastics have not yet been sufficiently developed. Here, we present magnetically driven living bacterial microrobots that exhibit a nature-inspired three-dimensional (3D) swarming motion, allowing the dynamic capture and retrieval of aquatic micro- and nanoplastics originating from various commercial products. By combining autonomous propulsion with magnetically guided navigation, we enabled the multimodal swarming manipulation of magnetotactic bacteria-based living microrobots (MTB biobots). The actuation of a rotating magnetic field induces a fish schooling-like 3D swarming navigation, allowing the active capture of micro- and nanoplastics, which are then retrieved from the contaminated water by magnetic separation. Our results show that the 3D magnetic swarming of MTB biobots synergistically enhances the removal efficiencies of both model and real-world microplastics, demonstrating their practical potential in water treatment technologies. Overall, plastic-seeking living bacterial microrobots and their swarm manipulation offer a straightforward and environmentally friendly approach to micro- and nanoplastic treatment, providing a biomachinery-based solution to mitigate the pressing microplastic pollution crisis.

• Klíčová slova
• biohybrid microrobots, magnetically driven, magnetotactic bacteria, microplastics, nanoplastics, swarming behavior, water purification,
• MeSH
• chemické látky znečišťující vodu * izolace a purifikace   chemie   MeSH
• čištění vody * metody   MeSH
• magnetické pole MeSH
• mikroplasty * izolace a purifikace   chemie   MeSH
• robotika * MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• chemické látky znečišťující vodu * MeSH
• mikroplasty * MeSH

Honey bees live in colonies of thousands of individuals, that not only need to collaborate with each other but also to interact intensively with their ecosystem. A small group of robots operating in a honey bee colony and interacting with the queen bee, a central colony element, has the potential to change the collective behavior of the entire colony and thus also improve its interaction with the surrounding ecosystem. Such a system can be used to study and understand many elements of bee behavior within hives that have not been adequately researched. We discuss here the applicability of this technology for ecosystem protection: A novel paradigm of a minimally invasive form of conservation through "Ecosystem Hacking". We discuss the necessary requirements for such technology and show experimental data on the dynamics of the natural queen's court, initial designs of biomimetic robotic surrogates of court bees, and a multi-agent model of the queen bee court system. Our model is intended to serve as an AI-enhanceable coordination software for future robotic court bee surrogates and as a hardware controller for generating nature-like behavior patterns for such a robotic ensemble. It is the first step towards a team of robots working in a bio-compatible way to study honey bees and to increase their pollination performance, thus achieving a stabilizing effect at the ecosystem level.

• Klíčová slova
• ecosystem hacking, honeybees, micro-robotics, queen behavior, swarm robotics,
• Publikační typ
• časopisecké články MeSH

The forefront of micro- and nanorobot research involves the development of smart swimming micromachines emulating the complexity of natural systems, such as the swarming and collective behaviors typically observed in animals and microorganisms, for efficient task execution. This study introduces magnetically controlled microrobots that possess polymeric sequestrant "hands" decorating a magnetic core. Under the influence of external magnetic fields, the functionalized magnetic beads dynamically self-assemble from individual microparticles into well-defined rotating planes of diverse dimensions, allowing modulation of their propulsion speed, and exhibiting a collective motion. These mobile microrobotic swarms can actively capture free-swimming bacteria and dispersed microplastics "on-the-fly", thereby cleaning aquatic environments. Unlike conventional methods, these microrobots can be collected from the complex media and can release the captured contaminants in a second vessel in a controllable manner, that is, using ultrasound, offering a sustainable solution for repeated use in decontamination processes. Additionally, the residual water is subjected to UV irradiation to eliminate any remaining bacteria, providing a comprehensive cleaning solution. In summary, this study shows a swarming microrobot design for water decontamination processes.

• Klíčová slova
• collective motion, magnetically driven, micromotors, microplastics, self-assembly, swarming behavior, water purification,
• MeSH
• Bacteria izolace a purifikace   MeSH
• Escherichia coli izolace a purifikace   MeSH
• magnetické pole MeSH
• mikroplasty * chemie   MeSH
• polymery chemie   MeSH
• robotika * přístrojové vybavení   MeSH
• velikost částic MeSH
• voda chemie   MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH