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.

Design and verification of a parallel elastic robotic leg.

This paper presents the design and experimental verification of a parallel elastic robotic leg mechanism that aims to capture the dynamics of the linear mass-spring-damper model. The mechanism utilizes a wrapping cam mechanism to linearize the non-linear force resulting from the elongation of the parallel elastic element. Firstly, we explain the desired dynamics of the mass-spring-damper model, including the impact transitions, and the design of the wrapping cam mechanism.

A Minimally Invasive Approach Towards "Ecosystem Hacking" With Honeybees.

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.

Interaction network effects on position- and velocity-based models of collective motion.

We study how the structure of the interaction network affects self-organized collective motion in two minimal models of self-propelled agents: the Vicsek model and the Active-Elastic (AE) model. We perform simulations with topologies that interpolate between a nearest-neighbour network and random networks with different degree distributions to analyse the relationship between the interaction topology and the resilience to noise of the ordered state. For the Vicsek case, we find that a higher fraction of random connections with homogeneous or power-law degree distribution increases the critical noise, and thus the resilience to noise, as expected due to small-world effects.

Anticipatory effect of execution on observation: an approach using ExoPinch finger robot.

Background/aim: This study aims to explore the mirror neuron system (MNS) involvement using mu (8–12 Hz)/beta (15–25 Hz) band suppression in an action observation-execution paradigm.

Materials And Methods: Electrophysiological (EEG) data from 16 electrodes were recorded while 8 participants observed video clips of a hand squeezing a spring. Specifically, the effect of anticipated execution on observation was studied.

Analysis of mirror neuron system activation during action observation alone and action observation with motor imagery tasks.

This study aimed to explore the relationship between action observation (AO)-related corticomotor excitability changes and phases of observed action and to explore the effects of pure AO and concurrent AO and motor imagery (MI) state on corticomotor excitability using TMS. It was also investigated whether the mirror neuron system activity is muscle-specific. Fourteen healthy volunteers were enrolled in the study.

Evolution of Self-Organized Task Specialization in Robot Swarms.

Division of labor is ubiquitous in biological systems, as evidenced by various forms of complex task specialization observed in both animal societies and multicellular organisms. Although clearly adaptive, the way in which division of labor first evolved remains enigmatic, as it requires the simultaneous co-occurrence of several complex traits to achieve the required degree of coordination. Recently, evolutionary swarm robotics has emerged as an excellent test bed to study the evolution of coordinated group-level behavior.

Elasticity-based mechanism for the collective motion of self-propelled particles with springlike interactions: a model system for natural and artificial swarms.

We introduce an elasticity-based mechanism that drives active particles to self-organize by cascading self-propulsion energy towards lower-energy modes. We illustrate it on a simple model of self-propelled agents linked by linear springs that reach a collectively rotating or translating state without requiring aligning interactions. We develop an active elastic sheet theory, complementary to the prevailing active fluid theories, and find analytical stability conditions for the ordered state.