A honeybee colony is a complex and dynamic system that emerges out of the interactions of thousands of individuals within a seemingly chaotic and heterogeneous environment. At the figurative core of this system is the honeybee queen, responsible for the growth and reproduction of the eusocial superorganism. In this study, we examine the interaction between the queen and her surrounding environment by analyzing her movement patterns using mathematical models and computational approaches. We employed a visual tracking system to observe three queens of Apis mellifera within their colonies over a three-week period and analyzed sets of quality tracklets to provide observational evidence regarding the queens' motion-related decision-making. Contrary to expectations, we found that the queen's short-term motion characteristics-such as speed and turning-were remarkably invariant across distinct hive regions, suggesting a lack of direct environmental modulation at short timescales. Yet, long-term patterns showed structured and strategic behavior. Inter-stop distances followed a power-law distribution, and queens repeatedly revisited specific spatial zones over multi-day timescales. These results indicate a dual-scale movement strategy that is not captured by standard random walk models, highlighting internal state or memory-based navigation. Our findings suggest that queen movement is shaped by temporally layered processes that may support brood nest stability, efficient egg-laying, and colony cohesion.

• MeSH
• Behavior, Animal * physiology   MeSH
• Movement physiology   MeSH
• Social Behavior MeSH
• Bees physiology   MeSH
• Animals MeSH
• Check Tag
• Female MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH

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.

• Keywords
• bio-hybrid systems, collision avoidance, genetic optimization, multi-agent, path planning, probabilistic roadmap,
• Publication type
• Journal Article MeSH