Manual Control Research Articles

Robotic-Assisted Endovascular Treatment for Transplant Renal Artery Stenosis: A Feasibility Study.

To describe a single-center experience with robotic-assisted endovascular treatment for transplant renal artery stenosis. This is a single-center, retrospective, feasibility study of 4 consecutive cases of robotic-assisted endovascular surgery for transplant renal artery stenosis from October 2021 to August 2022. All lesions were identified, and stenting was performed with no complications. Conversion to manual control was not necessary. The mean fluoroscopy time was 25.25min (range 12-60.9). A control Doppler ultrasound was routinely performed, demonstrating no residual lesions in all cases. There was no reintervention during the follow-up period. The operator learning curve was felt to be acceptable. Robotic-assisted endovascular treatment is a feasible technique for transplant renal artery stenosis.

Railway contactless checkout process with identification assisted by gait recognition

With business process optimization, technological advancement, equipment capability enhancement, and other means, the Railway Passenger Service Department in China is consistently working to improve the efficiency and convenience of passenger entry and exit procedures at railway stations. Concerning passengers’ checkout, not only conventional identification approaches based on manual control, identification card, and magnetic thermal paper ticket are supported, but also a recent contactless identification process based on face recognition is applied in some stations. To further improve the contactless identification ability for checkout, an advanced contactless checkout process based on gait-augmented face recognition is innovatively put forward, in which a weakly-supervised body segmentation network named Dwsegnet and an improved GaitSet model are proposed. Through comparison with various models, the effectiveness of both Dwsegnet and the improved GaitSet is validated. Specifically, the contactless identification rate of gait-augmented face recognition is improved by 2.31% when compared to single-modal face recognition, which demonstrates the superiority of the proposed process.

VASCilia (Vision Analysis StereoCilia): A Napari Plugin for Deep Learning-Based 3D Analysis of Cochlear Hair Cell Stereocilia Bundles.

Cochlear hair cell stereocilia bundles are key organelles required for normal hearing. Often, deafness mutations cause aberrant stereocilia heights or morphology that are visually apparent but challenging to quantify. Actin-based structures, stereocilia are easily and most often labeled with phalloidin then imaged with 3D confocal microscopy. Unfortunately, phalloidin non-specifically labels all the actin in the tissue and cells and therefore results in a challenging segmentation task wherein the stereocilia phalloidin signal must be separated from the rest of the tissue. This can require many hours of manual human effort for each 3D confocal image stack. Currently, there are no existing software pipelines that provide an end-to-end automated solution for 3D stereocilia bundle instance segmentation. Here we introduce VASCilia, a Napari plugin designed to automatically generate 3D instance segmentation and analysis of 3D confocal images of cochlear hair cell stereocilia bundles stained with phalloidin. This plugin combines user-friendly manual controls with advanced deep learning-based features to streamline analyses. With VASCilia, users can begin their analysis by loading image stacks. The software automatically preprocesses these samples and displays them in Napari. At this stage, users can select their desired range of z-slices, adjust their orientation, and initiate 3D instance segmentation. After segmentation, users can remove any undesired regions and obtain measurements including volume, centroids, and surface area. VASCilia introduces unique features that measures bundle heights, determines their orientation with respect to planar polarity axis, and quantifies the fluorescence intensity within each bundle. The plugin is also equipped with trained deep learning models that differentiate between inner hair cells and outer hair cells and predicts their tonotopic position within the cochlea spiral. Additionally, the plugin includes a training section that allows other laboratories to fine-tune our model with their own data, provides responsive mechanisms for manual corrections through event-handlers that check user actions, and allows users to share their analyses by uploading a pickle file containing all intermediate results. We believe this software will become a valuable resource for the cochlea research community, which has traditionally lacked specialized deep learning-based tools for obtaining high-throughput image quantitation. Furthermore, we plan to release our code along with a manually annotated dataset that includes approximately 55 3D stacks featuring instance segmentation. This dataset comprises a total of 1,870 instances of hair cells, distributed between 410 inner hair cells and 1,460 outer hair cells, all annotated in 3D. As the first open-source dataset of its kind, we aim to establish a foundational resource for constructing a comprehensive atlas of cochlea hair cell images. Together, this open-source tool will greatly accelerate the analysis of stereocilia bundles and demonstrates the power of deep learning-based algorithms for challenging segmentation tasks in biological imaging research. Ultimately, this initiative will support the development of foundational models adaptable to various species, markers, and imaging scales to advance and accelerate research within the cochlea research community.

Field experiment examining condensation reduction methods based on underground parking lot types

ABSTRACT In this study, condensation reduction methods with high field applicability to various types of underground parking lots in apartment buildings were selected and tested. The selected methods included floor-insulating drainage plates, induction fans with heating wires, and an automatic control ventilation system based on indoor and outdoor temperatures and humidity. The effectiveness of these methods was evaluated via real-world field experiments. The results indicated that while the insulating drainage plates initially blocked the cold, their long-term effectiveness matched that of normal drainage plates (difference 0.1°C). The induction fan with a heating wire showed average increases of 0.8°C and 2.7°C in wall surface temperature after one and three months, compared to normal induction fans. The absolute humidity of the automatic control ventilation decreased by 1.8 g/kg after 10 h of operation during rainy weather, whereas the manual control ventilation system increased by 0.6 g/kg under the same conditions. This study offers insights into the most effective application of these methods for various parking lot configurations.

Development of an integrated energy management system for off-grid solar applications with advanced solar forecasting, time-of-use tariffs, and direct load control

Effectively managing and maximizing the integration of renewable energy sources is essential for a sustainable power grid due to the stochastic and intermittent nature of renewable energy generation. This study develops a comprehensive Integrated Energy Management System incorporating supply-demand side management in the form of time-of-use credit, direct load control, and generator control to enhance photovoltaic utilization in off-grid applications. A novel three-step solar energy forecasting approach is proposed in this paper, utilizing low-level data fusion and regression models to predict next-day photovoltaic generation with improved accuracy, and a rule-based decision algorithm is developed to correct forecast errors and manage loads dynamically. A techno-economic analysis covering a 20-year duration is carried out for scenarios with and without the integrated energy management system; three configurations are investigated for supplying an off-grid residential home, including diesel generator, diesel generator/photovoltaic system, and diesel generator/photovoltaic system/integrated energy management system. Results reveal that the hybrid configuration with integrated energy management system achieved 44 % and 46 % reductions in costs and carbon dioxide emissions compared to the diesel generator alone, and 8 % and 9 % compared to the diesel generator/photovoltaic setup respectively. The Integrated Energy Management System further enhanced photovoltaic utilisation rate by over 113 % when compared to the diesel generator/photovoltaic system. Further evaluations include customer behaviour impacts, demonstrating that a fully automated system with 100 % compliance significantly outperforms systems with manual customer control, highlighting the detrimental effect of overrides on the efficiency of direct load control. The flexibility of the Integrated Energy Management System framework allows potential adaptation for on-grid applications, showcasing its utility in diverse operational contexts.

Design and Implementation of Electrochromic Smart Windows with Self-Driven Thermoelectric Power Generation.

Electrochromic smart windows can achieve controllable modulation of color and transmittance under an external electric field with active light and thermal control capabilities, which helps reduce energy consumption caused by building cooling and heating. However, electrochromic smart windows often rely on external power circuits, which greatly affects the independence and portability of smart windows. Based on this, an electrochromic smart window driven by temperature-difference power generation was designed and implemented. This smart window provides automatic and manual control of the reversible cycle of electrochromic glass from light blue to dark blue according to user requirements and changes in the surrounding environment, achieving adaptive adjustment of visual comfort and reducing energy consumption. The infrared radiation rejection (from 780 to 2500 nm) of the electrochromic smart window is as high as 77.3%, and its transmittance (from 380 to 780 nm) fluctuates between 39.2% and 56.4% with changes in working state. Furthermore, the temperature in the indoor simulation device with electrochromic glass as the window was 15 °C lower than that with ordinary glass as the window after heating with a 250 W Philips infrared lamp for ten minutes. After 2000 cycles of testing, the performance of the smart window was basically maintained at its initial values, and it has broad application prospects in buildings, vehicles, and high-speed rail systems.

Autonomous robotic drone system for mapping forest interiors

Abstract. During the last decade, the use of drones in forest monitoring and remote sensing has become highly popular. While most of the monitoring tasks take place in high altitudes and open air, in the last few years drones have also gained interest in under-canopy data collection. However, flying under the forest canopy is a complex task since the drone can not use Global Navigation Satellite Systems (GNSS) for positioning and it has to continually avoid obstacles, such as trees, branches, and rocks, on its path. For that reason, drone-based data collection under the forest canopy is still mainly based on manual control by human pilots. Autonomous flying in GNSS-denied obstacle-rich environment has been an actively researched topic in the field of robotics during the last years and various open-sourced methods have been published in the literature. However, most of the research is done purely from the point-of-view of robotics and only a few studies have been published in the boundary of forest sciences and robotics aiming to take steps towards autonomous forest data collection. In this study, a prototype of an autonomous under-canopy drone is developed and implemented utilizing state-of-the-art open-source methods. The prototype is utilizing the EGO-Planner-v2 trajectory planner for autonomous obstacle avoidance and VINS-Fusion for Visual-inertial-odometry based GNSS-free pose estimation. The flying performance of the prototype is evaluated by performing multiple test flights with real hardware in two different boreal forest test plots with medium and difficult densities. Furthermore, the first results of the forest data collecting performance are obtained by post-processing the data collected with a low-cost stereo camera during one test flight to a 3D point cloud and by performing diameter breast at height (DBH) estimation. In the medium-density forest, all seven test flights were successful, but in the difficult test forest, one of eight test flights failed. The RMSE of the DBH estimation was 3.86 cm (12.98 %).

Geometric features and traffic dynamic analysis on 4-leg intersections

AbstractWith the escalating density of vehicles converging at road intersections, the surge in road accidents, traffic conflicts, and traffic congestion has emerged as a pressing concern. This research paper addresses these challenges by employing MC (manual control) techniques to mitigate encroachment issues at three selected intersections. These intersections were identified through a comprehensive analysis of the Ranking-based Instance Selection (RIS), enabling the design of suitable measures to facilitate smooth traffic flow and minimize the occurrence of crashes. In order to gather pertinent data, the study incorporates various parameters such as traffic volume, peak flow rate (PFR), traffic conflicts, accidents, and intersection inventory. Through the implementation of our proposed approaches, which involve both MC techniques and signalized operation, a supreme level of service (LOS) is attainable. Notably, our findings demonstrate a remarkable reduction in the volume-to-capacity ratio (v/c ratio) of up to 0.62. This paper thus serves as a significant contribution to the field of traffic management, offering practical insights for optimizing intersection design and effectively addressing the challenges posed by increased traffic density.

Automated control of Impella maintains optimal left ventricular unloading during periods of unstable hemodynamics and prevents myocardial damage in acute myocardial infarction

BackgroundLeft ventricular (LV) unloading by Impella, an intravascular microaxial pump, has been shown to exert dramatic cardioprotective effects in acute clinical settings of cardiovascular diseases. Total Impella support (no native LV ejection) is far more efficient in reducing LV energetic demand than partial Impella support, but the manual control of pump speed to maintain stable LV unloading is difficult and impractical. We aimed to develop an Automatic IMpella Optimal Unloading System (AIMOUS), which controls Impella pump speed to maintain LV unloading degree using closed-feedback control. We validated the AIMOUS performance in an animal model. MethodsIn dogs, we identified the transfer function from pump speed to LV systolic pressure (LVSP) under total support conditions (n = 5). Using the transfer function, we designed the feedback controller of AIMOUS to keep LVSP at 40 mmHg and examined its performance by volume perturbations (n = 9). Lastly, AIMOUS was applied in the acute phase of ischemia-reperfusion in dogs. Four weeks after ischemia-reperfusion, we assessed LV function and infarct size (n = 10). ResultsAIMOUS maintained constant LVSP, thereby ensuring a stable LV unloading condition regardless of volume withdrawal or infusion (±8 ml/kg from baseline). AIMOUS in the acute phase of ischemia-reperfusion markedly improved LV function and reduced infarct size (No Impella support: 13.9 ± 1.3 vs. AIMOUS: 5.7 ± 1.9%, P < 0.05). ConclusionsAIMOUS is capable of maintaining optimal LV unloading during periods of unstable hemodynamics. Automated control of Impella pump speed in the acute phase of ischemia-reperfusion significantly reduced infarct size and prevented subsequent worsening of LV function.

Broadband intelligent programmable metasurface with polarization-modulated self-adaptive electromagnetic functionality switching

Programmable metasurfaces have received a great deal of attention due to their ability to dynamically manipulate electromagnetic (EM) waves. Despite the rapid growth, most of the existing metasurfaces require manual control to switch among different functionalities, which poses severe limitations on practical applications. Here, we put forth an intelligent metasurface that has self-adaptive EM functionality switching in broadband without human participation. It is equipped with polarization discrimination antennas (PDAs) and feedback components to automatically adjust functionalities for the different incident polarization information. The PDA module can first perceive the polarization of incident EM waves, e.g., linear or circular polarization, and then provide the feedback signal to the controlling platform for switching the EM functionality. As exemplary demonstrations, a series of functionalities in the 9–22 GHz band has been realized, including beam scanning for x-polarization, specular reflection for y-polarization, diffuse scattering for left-handed circular polarization (LCP), and vortex beam generation for right-handed circular polarization (RCP) waves. Experiments verify the good self-adaptive reaction capability of the intelligent metasurface and are in good agreement with the designs. Our strategy provides an avenue toward future unmanned devices that are consistent with the ambient environment.

Voice interactive information metasurface system for simultaneous wireless information transmission and power transfer

Intelligent voice interaction offers a flexible and powerful way to connect individuals with smart devices beyond our expectations. The real-time nature of voice communication enables smart devices to comprehend the user language, execute the corresponding instructions, and facilitate seamless communications, transforming our lives in unprecedented ways. Owing to self-adaptive and reprogrammable functionalities, information metasurface (IMS) opens up a new avenue for smart home and smart cities. To further enhance the intelligence of IMS, we propose an IMS system via intelligent voice interaction and information processing. The voice interaction enables the efficient remote control on the IMS in a flexible, convenient, touchless manner. Leveraging speech recognition, speech synthesis, target detection, and communication technologies, the IMS system achieves automatic beam manipulation capabilities for wireless information transmissions and wireless power transfers. The IMS system is designed to operate in two distinct modes: instruction mode, wherein the user instructs the operations, and autonomous mode, wherein the automatic detections govern the actions, in which seamless mode switching through the voice commands is supported. Users can flexibly achieve precise control over the functions of the intelligent metasurface system through voice interaction at a distance, without the need for close-range manual touch control, which greatly simplifies the operation difficulty and is particularly suitable for remote control and complex application scenarios. A series of experiments, including wireless video transmissions and wireless power transfers are conducted to demonstrate the flexibility and convenience of the IMS system. The incorporation of intelligent voice interaction technology with the IMS presents a novel paradigm for the applications of programmable and information metasurfaces.

Combining optimization and dynamic movement primitives for planning energy optimal forestry crane motions

Forestry cranes are an important tool for safe and efficient timber harvesting with forestry machines. However, their complex manual control often led to inefficiencies and excessive energy usage, due to the many joysticks and buttons that must be used in a precise sequence to perform efficient movements. To address this, the industry is increasingly turning to partial automation, making manual control more intuitive for the operator and, consequently, achieving improvements in energy efficiency. This article introduces a novel approach to energy-optimal motion planning that can be used along with a feedback control system to automate crane motions, taking over portions of the operator’s work. Our method combines dynamic movement primitives (DMPs) and an energy-optimization algorithm. DMPs is a machine learning technique for motion planning based on human demonstrations, while the optimization algorithm exploits the crane’s redundancy to find energy-optimal trajectories. Simulation results show that DMPs can replicate human-like controlled motions with a 25% reduction in energy consumption. However, our energy optimization algorithm shows improvements of over 40%, providing substantial energy savings and a promising pathway towards environmentally friendly partially automated machines.

IoT based Smart Surveillance with Landmine Detection and Alerts

The research uses a Node MCU microcontroller and Internet of Things (IoT) technology to create a robot for handheld applications. The robot has a night vision camera that connects to the internet, allowing it to take photos and videos from a distance, even at night. It also has a metal detector to find landmines and an infrared (IR) sensor to detect obstacles and prevent accidents. These sensors immediately notify and alert users through LCD screens, messages, and buzzers. The research aims to develop a robot with increased accessibility and user friendliness by offering simple manual and operator controls for robotic inspection. The high-definition camera provides clear photos and videos for detailed analysis of any suspects, helping to make informed decisions quickly and improve situational awareness. On detecting a landmine, the robot alerts the user through the email and displays the information on the LCD screen, enhancing the safety at checkpoints. The IR sensor helps the robot avoid obstacles by alerting the user and sounding a buzzer when something is detected at a close distance. This feature allows the robot to navigate complex environments safely. Overall the robot is designed to provide a smart surveillance by offering an immediate threat response to handle the security concerns in various situations.

Performance Evaluation of Signalized Intersection- A Case study of Prithivi Chowk, Pokhara

Prithivi Chowk, a heavily trafficked signalized intersection in Pokhara, Nepal, currently relies on manual traffic control, leading to significant issues during peak hours. This study employs the Highway Capacity Manual (HCM) methodology to evaluate the intersection's performance, aiming to address congestion and recommend improvements. Data collected from July 10-12, 2023, during peak hours, indicated traffic volumes of 6369.5 PCU/h in the morning and 6275.5 PCU/h in the evening, demonstrating over-saturation. The analysis included 12 vehicular and 4 pedestrian directions, with peak hour factors of 0.942 and 0.954, respectively.Initial assessments of three lane configurations and signal phasing plans revealed all phase plans exceeded capacity, resulting in a Level of Service (LOS) 'F'. Subsequent evaluations involved geometric enhancements, such as adding lanes. Increasing lanes in the Eastbound Through, Northbound Right Turn, and Westbound Right Turn directions significantly improved performance, achieving LOS 'D' in the third iteration of Phase Plan I. Optimal signal timings were designed using the Webster method, resulting in cycle lengths of 123 seconds for the morning peak and 116 seconds for the evening peak. The study highlights critical challenges and proposes targeted measures to enhance traffic flow at Prithivi Chowk, offering valuable contributions to transportation engineering and urban planning in Nepal. These findings emphasize the need for systematic traffic management improvements to mitigate congestion and enhance intersection performance.

Design of AI-Powered Hybrid Control Algorithm of Robot Vehicle for Enhanced Driving Performance

This paper presents a hybrid control of robot vehicle using behavior cloning algorithm with PID control for enhanced driving performance. The behavior cloning algorithm based on convolutional neural network (CNN) has been implemented to the autonomous driving algorithm of a robot vehicle by implementing supervised learning approach. The algorithm mimics human driving behaviors for autonomous driving by obtaining and learning manual(human) control data. In order to implement the algorithm on the real system, a low-cost camera sensor and controllers with actuators are used so that it achieves robust driving performance. With a successfully developed and validated CNN based behavior cloning model, a classic PID control algorithm has been combined as a lateral controller to propose AI-powered hybrid control architecture which improves the performance of trajectory tracking in various environments using proposed behavior cloning algorithm. The performance of the proposed algorithm has been evaluated via robot vehicle tests. It has been shown from the simulation studies and vehicle tests that the proposed algorithm enhances driving performance.

A Situated Inspection of Autonomous Vehicle Acceptance – A Population Study in Virtual Reality

The successful integration of Autonomous Vehicles (AVs) into daily life is influenced by various individual and contextual factors. This study examines key factors affecting acceptance and trust in AVs by assessing how interactive experiences influence user perceptions and adoption. Utilizing a Virtual Reality (VR) environment to simulate real-world driving conditions, the research investigates the impact of different levels of vehicle interaction—manual control, semi-autonomous, fully autonomous, and taxi-riding modes—on user and passenger trust, anxiety, perceived ease of use, and overall acceptance. A dual-method approach was employed, combining objective data collection through eye-tracking, which measures engagement and vigilance, with subjective user feedback gathered via the Autonomous Vehicle Acceptance Model (AVAM) questionnaire. The findings reveal a complex relationship between user control and trust in AV systems. While semi-autonomous conditions that incorporate user participation in decision-making enhance trust and intention to use, fully autonomous conditions influence trust but not intention to use significantly. However, semi- autonomous conditions also elicited higher anxiety levels compared to fully autonomous modes, suggesting a trade-off between interaction and comfort. Engagement levels, as indicated by visual metrics such as gaze patterns, were found to be critical for understanding user acceptance. These results highlight the importance of considering both subjective perceptions and objective behaviors in developing AV interfaces. The study contributes significant insights into the cognitive underpinnings of AV acceptance and underscores the need for strategies that both enhance user trust and minimize anxiety by finding an optimal balance between automation and user control. Further research into this balance is recommended to better accommodate the evolving nature of user trust and the cognitive complexities associated with semi-autonomous systems.

Smart Occupancy Lighting System

This project introduces a sophisticated system designed to streamline lighting management in seminar halls, effectively addressing the diverse needs of both stage activities and audience engagement. Leveraging the Arduino Uno R3 microcontroller, alongside infrared (IR) sensors, a toggle switch, jumper wires, and an LED display, this system offers a comprehensive solution for dynamically adjusting lighting levels based on real-time occupancy data. By integrating sensor technology at strategic entry and exit points within the seminar hall, the system accurately detects the flow of attendees, enabling precise control over both stage and audience lights. The innovative use of a toggle switch provides manual control over stage lighting, granting presenters and event organizers the flexibility to tailor the lighting environment to suit specific presentation styles and event dynamics. Through seamless automation and intuitive user interface design, this system enhances energy efficiency, optimizes attendee comfort, and facilitates smooth event management processes. This abstract provides a succinct overview of the project's objectives, components, and anticipated benefits, setting the stage for a detailed exploration of its design, implementation, and potential applications in seminar hall environments.

АРХІТЕКТУРА СИСТЕМИ АВТОНОМНОГО КЕРУВАННЯ ДЛЯ FPV-ДРОНІВ

Drone technologies and robotic systems, that can be used to optimize or replace human labor in different areas, are continuing to develop and expand their area of application. Computational hardware, that can be used as an extension to basic drone hardware, is becoming more powerful for a smaller price and size. Autonomous drone control provides a practical solution for issues that are present when a drone is controlled manually by an operator. There are limitations that exclude the possibility of operator control and some solutions are much less effective if there is no autonomous algorithm. A large distance, radio interference or a huge amount of data to be analyzed are not as critical if a drone is capable to make decisions autonomously without an operator. Because of the features of a basic amateur FPV-drone its flight controller does not operate enough computing power to execute complex algorithms such as object detection, object tracking, calculation of a proper trajectory etc. In cases like this a companion computer should be used. Depending on an area of application, drone control algorithms may vary. Flight controllers’ firmware has some embedded functionality with relatively simple algorithms like pre-defined flight routes that use GCS-coordinates. In the case when more complex behavior is required (image processing, AI integration or autonomous decision making with manual controls), such firmware provides an ability to accept various drone commands that correspond to the MAVLink communication protocol. This work investigates important details of companion computers integration with flight controllers with examples and describes hardware and software aspects of implementation of autonomous drone controls. The research also provides solutions to problems that can be possible when implementing such a system.

Automatic Control of Polymer Dosage Using Floc Images in Sludge Dewatering Plant

This study introduces a novel method for estimating floc conditions in sludge dewatering plants by employing image analysis and automatic control of polymer dosage. While previous research has focused on drinking water treatment plants, few reports address polymer dosage optimization using image analysis in sludge dewatering plants. The challenge lies in the high sludge dry solids hindering individual floc recognition due to overlap. The study aims to estimate floc conditions by focusing on gap areas between flocs and implementing automatic polymer dosage control accordingly. The proposed method uses images from an Internet Protocol camera and semantic segmentation to identify the floc gap area. For validation of the estimation method, variations of over and under polymer dosage scenarios were investigated and compared with commonly used floc area methods. The findings indicate that the gap area estimation effectively reproduces the theory of polymer cohesion. Automatic polymer dosage control based on this method demonstrates stable operation in both scenarios. Notably, automatic control outperformed manual operation during continuous operation, resulting in a significant reduction in polymer dosage and a notable increase in heating efficiency compared to manual control. This study presents an efficient approach to optimize polymer dosage in sludge dewatering plants, utilizing image analysis for real-time monitoring and control. By focusing on the gap area between flocs, the method enhances accuracy in estimating floc conditions, thereby improving overall dewatering efficiency. The findings highlight the practical benefits of implementing automatic control systems in sludge treatment plants, potentially reducing costs and environmental impact.

Evaluation of Biomechanical and Mental Workload During Human-Robot Collaborative Pollination Task.

The purpose of this study is to identify the potential biomechanical and cognitive workload effects induced by human robot collaborative pollination task, how additional cues and reliability of the robot influence these effects and whether interacting with the robot influences the participant's anxiety and attitude towards robots. Human-Robot Collaboration (HRC) could be used to alleviate pollinator shortages and robot performance issues. However, the effects of HRC for this setting have not been investigated. Sixteen participants were recruited. Four HRC modes, no cue, with cue, unreliable, and manual control were included. Three categories of dependent variables were measured: (1) spine kinematics (L5/S1, L1/T12, and T1/C7), (2) pupillary activation data, and (3) subjective measures such as perceived workload, robot-related anxiety, and negative attitudes towards robotics. HRC reduced anxiety towards the cobot, decreased joint angles and angular velocity for the L5/S1 and L1/T12 joints, and reduced pupil dilation, with the "with cue" mode producing the lowest values. However, unreliability was detrimental to these gains. In addition, HRC resulted in a higher flexion angle for the neck (i.e., T1/C7). HRC reduced the physical and mental workload during the simulated pollination task. Benefits of the additional cue were minimal compared to no cues. The increased joint angle in the neck and unreliability affecting lower and mid back joint angles and workload requires further investigation. These findings could be used to inform design decisions for HRC frameworks for agricultural applications that are cognizant of the different effects induced by HRC.