Reactive shepherding along a dynamic path.

Shepherding, the task of guiding a herd of autonomous individuals in a desired direction, is an essential skill employed in the herding of animals, crowd control, and evacuation operations. Integrating shepherding capabilities into robots holds promise to perform such tasks with increased efficiency and reduced labor costs. To date, robotic shepherds have only been designed to steer a herd towards a predetermined goal location without constraints on the trajectory. However, the tasks of a sheepdog encompass not only steering the herd but also (i) maintaining the herd within a designated area and (ii) averting dangers, obstacles, or undesirable terrain such as newly sown land. We present a decentralized control algorithm for multi-robot shepherding designed to guide a group of animals along a specified path delineated by two boundaries. The algorithm incorporates the additional objective of preserving the group within these boundaries. Simulation results reveal that, especially in sections of the path with sharp turns and a small distance between the boundaries, the group exhibits a tendency to deviate beyond the prescribed margin. Additionally, our findings emphasize the algorithm's sensitivity to the ratio of robot-group sizes and the magnitude of the group's velocity.

Swarm Metaverse for Multi-Level Autonomy Using Digital Twins.

Robot swarms are becoming popular in domains that require spatial coordination. Effective human control over swarm members is pivotal for ensuring swarm behaviours align with the dynamic needs of the system. Several techniques have been proposed for scalable human-swarm interaction. However, these techniques were mostly developed in simple simulation environments without guidance on how to scale them up to the real world. This paper addresses this research gap by proposing a metaverse for scalable control of robot swarms and an adaptive framework for different levels of autonomy. In the metaverse, the physical/real world of a swarm symbiotically blends with a virtual world formed from digital twins representing each swarm member and logical control agents. The proposed metaverse drastically decreases swarm control complexity due to human reliance on only a few virtual agents, with each agent dynamically actuating on a sub-swarm. The utility of the metaverse is demonstrated by a case study where humans controlled a swarm of uncrewed ground vehicles (UGVs) using gestural communication, and via a single virtual uncrewed aerial vehicle (UAV). The results show that humans could successfully control the swarm under two different levels of autonomy, while task performance increases as autonomy increases.

Steering herds away from dangers in dynamic environments.

Shepherding, the task of guiding a herd of autonomous individuals in a desired direction, is an essential skill to herd animals, enable crowd control and rescue from danger. Equipping robots with the capability of shepherding would allow performing such tasks with increased efficiency and reduced labour costs. So far, only single-robot or centralized multi-robot solutions have been proposed. The former is unable to observe dangers at any place surrounding the herd, and the latter does not generalize to unconstrained environments. Therefore, we propose a decentralized control algorithm for multi-robot shepherding, where the robots maintain a caging pattern around the herd to detect potential nearby dangers. When danger is detected, part of the robot swarm positions itself in order to repel the herd towards a safer region. We study the performance of our algorithm for different collective motion models of the herd. We task the robots to shepherd a herd to safety in two dynamic scenarios: (i) to avoid dangerous patches appearing over time and (ii) to remain inside a safe circular enclosure. Simulations show that the robots are always successful in shepherding when the herd remains cohesive, and enough robots are deployed.

Iterative shepherding control for agents with heterogeneous responsivity.

In the context of the theory of multi-agent systems, the shepherding problem refers to designing the dynamics of a herding agent, called a sheepdog, so that a given flock of agents, called sheep, is guided into a goal region. Although several effective methodologies and algorithms have been proposed in the last decade for the shepherding problem under various formulations, little research has been directed to the practically important case in which the flock contains sheep agents unresponsive to the sheepdog agent. To fill in this gap, we propose a sheepdog algorithm for guiding unresponsive sheep in this paper. In the algorithm, the sheepdog iteratively applies an existing shepherding algorithm, the farthest-agent targeting algorithm, while dynamically switching its destination. This procedure achieves the incremental growth of a controllable flock, which finally enables the sheepdog to guide the entire flock into the goal region. Furthermore, we illustrate by numerical simulations that the proposed algorithm can outperform the farthest-agent targeting algorithm.

Wildlife-friendly farming recouples grazing regimes to stimulate recovery in semi-arid rangelands.

While rangeland ecosystems are globally important for livestock production, they also support diverse wildlife assemblages and are crucial for biodiversity conservation. As rangelands around the world have become increasingly degraded and fragmented, rethinking farming practice in these landscapes is vital for achieving conservation goals, rangeland recovery, and food security. An example is reinstating livestock shepherding, which aims to recouple grazing regimes to vegetation conditioned to semi-arid climates and improve productivity by reducing overgrazing and rewiring past ecological functions. Tracking the large-scale ecosystem responses to shifts in land management in such sparsely vegetated environments have so far proven elusive. Therefore, our goal was to develop a remote tracking method capable of detecting vegetation changes and environmental responses on rangeland farms engaging in contrasting farming practices in South Africa: wildlife friendly farming (WFF) implementing livestock shepherding with wildlife protection, or rotational grazing livestock farming with wildlife removal. To do so, we ground-truthed Sentinel-2 satellite imagery using drone imagery and machine learning methods to trace historical vegetation change on four farms over a four-year period. First, we successfully classified land cover maps cover using drone footage and modelled vegetation cover using satellite vegetation indices, achieving 93.4% accuracy (к = 0.901) and an r-squared of 0.862 (RMSE = 0.058) respectively. We then used this model to compare the WFF farm to three neighbouring rotational grazing farms, finding that satellite-derived vegetation productivity was greater and responded more strongly to rainfall events on the WFF farm. Furthermore, vegetation cover and grass cover, patch size, and aggregation were greater on the WFF farm when classified using drone data. Overall, we found that remotely assessing regional environmental benefits from contrasting farming practices in rangeland ecosystems could aid further adoption of wildlife-friendly practices and help to assess the generality of this case study.

Does Functionality Condition the Population Structure and Genetic Diversity of Endangered Dog Breeds under Island Territorial Isolation?

Despite the undefinition of the origins of Ca de Rater (CR) and Ca de Bestiar (CB) dogs, references to these endangered autochthonous breeds highlighted their ratting/pet and shepherding/guard skills for centuries. Genealogical historical records were traced back to founders. Founder number in the reference population (146 and 53 for CR and CB, respectively), historical and reference maximum generations traced (eight and seven for CR and CB, respectively), and historical average number of complete generations (1.04 for both breeds) were determined. Structure assessment revealed the existence of subpopulations regarding criteria such as breeders (75 and 17), breeder location (32 and eight), owners (368 and 198), and owner location (73 and 51) for CR and CB, respectively. Average inbreeding (F) within breed subpopulations ranged from 0.27-1.20% for CB breeders and the rest of subpopulation criteria for both breeds, respectively, except for CB owners and owner location. F ranged from 0.27-1.41% for CB historical population and CR current population, respectively. The study of genetic diversity revealed a relatively similar genetic background between subpopulations. Average coancestry between and within breeds suggested a similar evolutionary process. However, Mann-Whitney U test determined significant differences for diversity parameters (F, ΔR, coancestry, nonrandom mating degree, maximum, complete, and equivalent generations, ΔF, and genetic conservation index) between breeds and their functionalities. Conclusively, functionality in dog breeds may determine the genetic diversity evolution of endangered breeds, even when these share the same geographic isolation conditions.

Hopf Bifurcations in Complex Multiagent Activity: The Signature of Discrete to Rhythmic Behavioral Transitions.

Most human actions are composed of two fundamental movement types, discrete and rhythmic movements. These movement types, or primitives, are analogous to the two elemental behaviors of nonlinear dynamical systems, namely, fixed-point and limit cycle behavior, respectively. Furthermore, there is now a growing body of research demonstrating how various human actions and behaviors can be effectively modeled and understood using a small set of low-dimensional, fixed-point and limit cycle dynamical systems (differential equations). Here, we provide an overview of these dynamical motorprimitives and detail recent research demonstrating how these dynamical primitives can be used to model the task dynamics of complex multiagent behavior. More specifically, we review how a task-dynamic model of multiagent shepherding behavior, composed of rudimentary fixed-point and limit cycle dynamical primitives, can not only effectively model the behavior of cooperating human co-actors, but also reveals how the discovery and intentional use of optimal behavioral coordination during task learning is marked by a spontaneous, self-organized transition between fixed-point and limit cycle dynamics (i.e., via a Hopf bifurcation).

Electroencephalographic Workload Indicators During Teleoperation of an Unmanned Aerial Vehicle Shepherding a Swarm of Unmanned Ground Vehicles in Contested Environments.

Background: Although many electroencephalographic (EEG) indicators have been proposed in the literature, it is unclear which of the power bands and various indices are best as indicators of mental workload. Spectral powers (Theta, Alpha, and Beta) and ratios (Beta/(Alpha + Theta), Theta/Alpha, Theta/Beta) were identified in the literature as prominent indicators of cognitive workload. Objective: The aim of the present study is to identify a set of EEG indicators that can be used for the objective assessment of cognitive workload in a multitasking setting and as a foundational step toward a human-autonomy augmented cognition system. Methods: The participants' perceived workload was modulated during a teleoperation task involving an unmanned aerial vehicle (UAV) shepherding a swarm of unmanned ground vehicles (UGVs). Three sources of data were recorded from sixteen participants (n = 16): heart rate (HR), EEG, and subjective indicators of the perceived workload using the Air Traffic Workload Input Technique (ATWIT). Results: The HR data predicted the scores from ATWIT. Nineteen common EEG features offered a discriminatory power of the four workload setups with high classification accuracy (82.23%), exhibiting a higher sensitivity than ATWIT and HR. Conclusion: The identified set of features represents EEG indicators for the objective assessment of cognitive workload across subjects. These common indicators could be used for augmented intelligence in human-autonomy teaming scenarios, and form the basis for our work on designing a closed-loop augmented cognition system for human-swarm teaming.

Shepherding: A concept analysis.

The purpose of this analysis was to explore the concept of shepherding as it applies to nursing. Shepherding is a term used in chaplaincy literature to describe the guidance and protection a chaplain offers. While this term may be appropriate to the relational work of nurses, it has yet to be defined in the nursing literature. Walker and Avant's eight-step method for concept development was utilized. An electronic search using "shepherd/shepherding and nursing or health or inpatient" was performed in eight databases related to nursing, medicine, health, social sciences, and chaplaincy. Scholarly articles, in English, with substantive references to shepherding were included. Four defining attributes related to shepherding were identified: relational interaction, safety and comfort, reciprocal guidance, and transformation through possibilities and actualization. The operational definition of shepherding was thus identified as a relational interaction between the nurse and a patient or family during a transformational crisis. The nurse accompanies the patient, ensuring safety, and comfort and, through reciprocal guidance, the nurse supports the patient to understand possibilities and actualize possibilities based on the patient's preferences.

Comportamiento ingestivo diurno y desempeño productivo de corderos suplementados con semilla de algodón y maíz molido / Daytime Ingestion Behavior and Performance in the Production of Lambs Supplemented with Cotton Seed and Ground Corn (GC) / Comportamento ingestivo diurno e desempenho produtivo de cordeiros suplementados com semente de algodão e molho moído

Resumen El estudio del comportamiento ingestivo es importante para conocer el desempeño de los animales y es especialmente imprescindible cuando se evalúan diferentes regímenes de alimentación. El objetivo del presente estudio fue evaluar la conducta ingestiva diurna y la respuesta productiva de corderos criollos suplementados con semilla de algodón (SA) y maíz molido (MM). Se emplearon 16 corderos criollos machos enteros con peso inicial de 16 ± 2 kg, los cuales fueron distribuidos aleatoriamente en cuatro tratamientos experimentales consistentes en: T0 = Pastoreo, T1 = Pastoreo + 25 % SA + 75 % MM, T2 = Pastoreo + 50 SA + 50 % MM y T3 = Pastoreo + 75% SA + 25 % MM. En los forrajes se determinó la composición nutricional y en los animales el consumo de materia seca a través de marcadores externos e internos, el comportamiento ingestivo y el desempeño productivo. El consumo de materia seca total presentó diferencias estadísticas (p < 0.05), con menor consumo para los animales del tratamiento control. Asimismo, se detectaron diferencias estadísticas (p < 0.05) entre los tratamientos para las actividades de pastoreo, rumia, consumo de agua y caminata para las variables número de bocados por minuto y total. El peso final y la ganancia de peso obtenida por los animales que recibieron suplementación fueron superiores en un 28.4 % y 50.3 %, respectivamente. La suplementación con SA y MM promovió un mayor consumo de nutrientes y, en consecuencia, mejoró el desempeño productivo de los corderos.

Abstract The study of the ingestion behavior is important to know the animal production performance and is a must when evaluating different feeding regimes. This work aims to evaluate the daytime ingestion behavior and the productive answer in criollo lambs receiving supplement with cotton seeds (CS) and ground corn (GC). Sixteen complete male criollo lambs were used having a starting weight of 16 ± 2 Kg. They were distributed randomly into four experimental treatments consisting in: T0 = shepherding, T1 = shepherding + 25% CS + 75% GC, T2 = shepherding + 50% CS + 50% GC, and T3 = shepherding + 75% CS + 25% GC. Using both external and internal markers, the nutritional composition of the forages and the dry mass consumption in the animals were determined, i.e., the ingestion behavior and the productive performance. The total dry mass consumption showed statistical differences (p < 0.05), with lower consumption among the control animals. Likewise, statistical differences were detected (p < 0.05) between the treatments for shepherding activities, cud, water consumption and walking regarding variables like mouthfuls per minute and total ingestion. The final weight and the weight gain in the animals receiving supplements were higher in 28.4 % and 50.3 %, respectively. The CS and GC supplements led to a greater consumption of nutrients and, therefore, improved the ovine production performance.

Resumo O estudo do comportamento ingestivo é importante para conhecer o desempenho dos animais e é especialmente essencial quando se avaliar diferentes regimes alimentares. O objetivo do presente estudo foi avaliar a conduta ingestiva diurna e a resposta produtiva de cordeiros crioulos suplementados com semente de algodão (SA) e molho moído (MM). Foram usados 16 cordeiros crioulos machos inteiros com peso inicial de 16 ± 2 kg, os quais foram distribuídos aleatoriamente em quatro tratamentos experimentais consistentes em: T0 = Pastejo, T1 = Pastejo + 25% SA + 75% MM, T2 = Pastejo + 50% SA + 50% MM e T3 = Pastejo + 75% SA + 25% MM. Nas forrageiras determinou-se a composição nutricional e nos animais o consumo de matéria seca através de marcadores externos e internos, o comportamento ingestivo e o desempenho produtivo. O consumo total de matéria seca apresentou diferenças estatísticas (p < 0.05), com menor consumo nos animais do tratamento controle. Mesmo, detectaram-se diferenças estatísticas (p < 0.05) entre os tratamentos para as atividades de pastoreio, ruminação, consumo de agua e caminhada para a variável número de lanches por minuto e total. O peso final e o ganho de peso obtido pelos animais que receberam suplementação foram superiores em 28.4% e 50.3%, respectivamente. A suplementação com SA e MM promoveu maior consumo de nutrientes e consequentemente, melhorou o desempenho produtivo dos cordeiros.

Human social motor solutions for human-machine interaction in dynamical task contexts.

Multiagent activity is commonplace in everyday life and can improve the behavioral efficiency of task performance and learning. Thus, augmenting social contexts with the use of interactive virtual and robotic agents is of great interest across health, sport, and industry domains. However, the effectiveness of human-machine interaction (HMI) to effectively train humans for future social encounters depends on the ability of artificial agents to respond to human coactors in a natural, human-like manner. One way to achieve effective HMI is by developing dynamical models utilizing dynamical motor primitives (DMPs) of human multiagent coordination that not only capture the behavioral dynamics of successful human performance but also, provide a tractable control architecture for computerized agents. Previous research has demonstrated how DMPs can successfully capture human-like dynamics of simple nonsocial, single-actor movements. However, it is unclear whether DMPs can be used to model more complex multiagent task scenarios. This study tested this human-centered approach to HMI using a complex dyadic shepherding task, in which pairs of coacting agents had to work together to corral and contain small herds of virtual sheep. Human-human and human-artificial agent dyads were tested across two different task contexts. The results revealed (i) that the performance of human-human dyads was equivalent to those composed of a human and the artificial agent and (ii) that, using a "Turing-like" methodology, most participants in the HMI condition were unaware that they were working alongside an artificial agent, further validating the isomorphism of human and artificial agent behavior.

Robot Collection and Transport of Objects: A Biomimetic Process.

Animals as diverse as ants and humans are faced with the tasks of collecting, transporting or herding objects. Sheepdogs do this daily when they collect, herd, and maneuver flocks of sheep. Here, we adapt a shepherding algorithm inspired by sheepdogs to collect and transport objects using a robot. Our approach produces an effective robot collection process that autonomously adapts to changing environmental conditions and is robust to noise from various sources. We suggest that this biomimetic process could be implemented into suitable robots to perform collection and transport tasks that might include - for example - cleaning up objects in the environment, keeping animals away from sensitive areas or collecting and herding animals to a specific location. Furthermore, the feedback controlled interactions between the robot and objects which we study can be used to interrogate and understand the local and global interactions of real animal groups, thus offering a novel methodology of value to researchers studying collective animal behavior.

Autonomous Shepherding Behaviors of Multiple Target Steering Robots.

This paper presents a distributed coordination methodology for multi-robot systems, based on nearest-neighbor interactions. Among many interesting tasks that may be performed using swarm robots, we propose a biologically-inspired control law for a shepherding task, whereby a group of external agents drives another group of agents to a desired location. First, we generated sheep-like robots that act like a flock. We assume that each agent is capable of measuring the relative location and velocity to each of its neighbors within a limited sensing area. Then, we designed a control strategy for shepherd-like robots that have information regarding where to go and a steering ability to control the flock, according to the robots' position relative to the flock. We define several independent behavior rules; each agent calculates to what extent it will move by summarizing each rule. The flocking sheep agents detect the steering agents and try to avoid them; this tendency leads to movement of the flock. Each steering agent only needs to focus on guiding the nearest flocking agent to the desired location. Without centralized coordination, multiple steering agents produce an arc formation to control the flock effectively. In addition, we propose a new rule for collecting behavior, whereby a scattered flock or multiple flocks are consolidated. From simulation results with multiple robots, we show that each robot performs actions for the shepherding behavior, and only a few steering agents are needed to control the whole flock. The results are displayed in maps that trace the paths of the flock and steering robots. Performance is evaluated via time cost and path accuracy to demonstrate the effectiveness of this approach.