A robotic honeycomb for interaction with a honeybee colony

Animal-robot interaction induces local enhancement in the Mediterranean fruit flyCeratitis capitataWiedemann

Animal-robot interaction (ARI) is an emerging field that uses biomimetic robots to replicate biological cues, enabling controlled studies of animal behavior. This study investigates the potential for ARI systems to induce local enhancement (e.g. where animals are attracted to areas based on the presence or actions of conspecifics) in the Mediterranean fruit fly,Ceratitis capitata(C. capitata), a major agricultural pest. We developed biomimetic agents that mimicC. capitatain morphology and color, to explore their ability to trigger local enhancement. The study employed three categories of artificial agents: full biomimetic agent (FBA), partial biomimetic agent (PBA) and non-biomimetic agent (NBA) in both motionless and moving states. Flies exposed to motionless FBAs showed a significant preference for areas containing these agents compared to areas with no agents. Similarly, moving FBAs also attracted more flies than stationary agents. Time spent in the release section before making a choice and the overall experiment duration were significantly shorter when conspecifics or moving FBAs were present, indicating thatC. capitatais highly responsive to biomimetic cues, particularly motion. These results suggest that ARI systems can be effective tools for understanding and manipulating local enhancement inC. capitata, offering new opportunities for sustainable pest control in agricultural contexts. Overall, this research demonstrates the potential of ARI as an innovative, sustainable approach to insect population control, with broad applications in both fundamental behavioral research and integrated pest management.

Novel automation, artificial intelligence, and biomimetic engineering advancements for insect studies and management

Entomology has seen remarkable advancements through the integration of robotics, artificial intelligence (AI), and biomimetic engineering. These technological innovations are revolutionizing how scientists study insect behavior, ecology, and management. Robotics and AI offer unprecedented precision and efficiency in monitoring and controlling insect populations. Biomimetics provides new ways to understand and replicate insect abilities in bioengineered systems. This mini-review highlights recent developments in these fields, focusing on key studies describing the transformative potential of these technologies. I explore their applications, benefits, and challenges, aiming at providing an overview of the current state and future directions in insect science and management.

Robots and animals teaming up in the wild to tackle ecosystem challenges

Interactively teaming up animals and robots could facilitate basic scientific research and address environmental and ecological crises.

Autonomous tracking of honey bee behaviors over long-term periods with cooperating robots

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.

Evolutionary optimization for risk-aware heterogeneous multi-agent path planning in uncertain environments

Cooperative multi-agent systems make it possible to employ miniature robots in order to perform different experiments for data collection in wide open areas to physical interactions with test subjects in confined environments such as a hive. This paper proposes a new multi-agent path-planning approach to determine a set of trajectories where the agents do not collide with each other or any obstacle. The proposed algorithm leverages a risk-aware probabilistic roadmap algorithm to generate a map, employs node classification to delineate exploration regions, and incorporates a customized genetic framework to address the combinatorial optimization, with the ultimate goal of computing safe trajectories for the team. Furthermore, the proposed planning algorithm makes the agents explore all subdomains in the workspace together as a formation to allow the team to perform different tasks or collect multiple datasets for reliable localization or hazard detection. The objective function for minimization includes two major parts, the traveling distance of all the agents in the entire mission and the probability of collisions between the agents or agents with obstacles. A sampling method is used to determine the objective function considering the agents' dynamic behavior influenced by environmental disturbances and uncertainties. The algorithm's performance is evaluated for different group sizes by using a simulation environment, and two different benchmark scenarios are introduced to compare the exploration behavior. The proposed optimization method establishes stable and convergent properties regardless of the group size.

Animal-robot interaction-an emerging field at the intersection of biology and robotics

The field of animal-robot and organism-robot interaction systems (ARIS, ORIS) is a currently rapidly emerging field in biorobotics. In this special issue we aim for providing a comprehensive overview of the cutting-edge advancements and pioneering breakthroughs within this scientific and engineering discipline. Therefore, we collected scientific articles that delineate and expound upon the complexity of these remarkable biohybrid systems. These configurations stand as engineered conduits, facilitating the accurate investigation and profound exploration of the multifaceted interactions between robotic devices and biological entities, including various fish species, honeybees and plants. Also the human factor plays a role in this collection, as we also include a philosophical perspective on such systems as well as an augmented reality setup that brings humans into the loop with living fish. Within our editorial purview, we categorize the scientific contributions based on their focal points, differentiating between examinations of singular agent-to-agent interactions, extensions to the social stratum, and further expansions to the intricate levels of swarm dynamics, colonies, populations, and ecosystems. Considering potential applications, we delve into the multifaceted domains wherein these biohybrid systems might be applied. This discourse culminates in a tentative glimpse into the future trajectories these technologies might traverse, elucidating their promising prospects for both scientific advancement and societal enrichment. In sum, this special issue aims at facilitating the convergence of diverse insights, at encapsulating the richness of the ARIS and ORIS domain, and at charting a course toward the untapped prospects lying at the nexus of biology and robotics.

The beehive of the future is a robot socially interacting with honeybees

A robotic beehive may unveil insights into honeybee collective behavior and sustain the colony in harsh weather.