Autonomous Tracking of Honey Bee Behaviors over Long-term Periods with Cooperating Robots

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00376345" target="_blank" >RIV/68407700:21230/24:00376345 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1126/scirobotics.adn6848" target="_blank" >https://doi.org/10.1126/scirobotics.adn6848</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1126/scirobotics.adn6848" target="_blank" >10.1126/scirobotics.adn6848</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Autonomous Tracking of Honey Bee Behaviors over Long-term Periods with Cooperating Robots

Popis výsledku v původním jazyce

Digital and mechatronic methods, paired with artificial intelligence and machine learning, are game-changing technologies in behavioral science. The central element of the most important pollinator species (honeybees) is the colony’s queen. The behavioral strategies of these ecologically important organisms are under-researched, due to the complexity of honeybees’ self-regulation and the difficulties of studying queens in their natural colony context. We created an autonomous robotic observation and behavioral analysis system aimed at 24/7 observation of the queen and her interactions with worker bees and comb cells, generating unique behavioral datasets of unprecedented length and quality. Significant key performance indicators of the queen and her social embedding in the colony were gathered by this tailored but also versatile robotic system. Data collected over 24-hour and 30-day periods demonstrate our system’s capability to extract key performance indicators on different system levels: Microscopic, mesoscopic, and macroscopic data are collected in parallel. Additionally, interactions between various agents are also observed and quantified. Long-term continuous observations yield high amounts of high-quality data when performed by an autonomous robot, going significantly beyond feasibly obtainable results of human observation methods or stationary camera systems. This allows a deep understanding of the innermost mechanisms of honeybees’ swarm-intelligent self-regulation as well as studying other ocial insect colonies, biocoenoses and ecosystems in novel ways. Social insects are keystone species in all ecosystems, thus understanding them better will be valuable to monitor, interpret, protect and even to restore our fragile ecosystems globally.

Název v anglickém jazyce

Autonomous Tracking of Honey Bee Behaviors over Long-term Periods with Cooperating Robots

Popis výsledku anglicky

Digital and mechatronic methods, paired with artificial intelligence and machine learning, are game-changing technologies in behavioral science. The central element of the most important pollinator species (honeybees) is the colony’s queen. The behavioral strategies of these ecologically important organisms are under-researched, due to the complexity of honeybees’ self-regulation and the difficulties of studying queens in their natural colony context. We created an autonomous robotic observation and behavioral analysis system aimed at 24/7 observation of the queen and her interactions with worker bees and comb cells, generating unique behavioral datasets of unprecedented length and quality. Significant key performance indicators of the queen and her social embedding in the colony were gathered by this tailored but also versatile robotic system. Data collected over 24-hour and 30-day periods demonstrate our system’s capability to extract key performance indicators on different system levels: Microscopic, mesoscopic, and macroscopic data are collected in parallel. Additionally, interactions between various agents are also observed and quantified. Long-term continuous observations yield high amounts of high-quality data when performed by an autonomous robot, going significantly beyond feasibly obtainable results of human observation methods or stationary camera systems. This allows a deep understanding of the innermost mechanisms of honeybees’ swarm-intelligent self-regulation as well as studying other ocial insect colonies, biocoenoses and ecosystems in novel ways. Social insects are keystone species in all ecosystems, thus understanding them better will be valuable to monitor, interpret, protect and even to restore our fragile ecosystems globally.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EH22_008%2F0004590" target="_blank" >EH22_008/0004590: Robotika a pokročilá průmyslová výroba</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Science Robotics

ISSN

2470-9476

e-ISSN

—

Svazek periodika

9

Číslo periodika v rámci svazku

95

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

16

Strana od-do

—

Kód UT WoS článku

001334145900001

EID výsledku v databázi Scopus

2-s2.0-85206693190