Robotic Handling Research Articles

Computer Vision as a Tool to Support Quality Control and Robotic Handling of Fruit: A Case Study

Computer Vision as a Tool to Support Quality Control and Robotic Handling of Fruit: A Case Study

A miniaturized feedstocks-to-fuels pipeline for screening the efficiency of deconstruction and microbial conversion of lignocellulosic biomass.

Sustainably grown biomass is a promising alternative to produce fuels and chemicals and reduce the dependency on fossil energy sources. However, the efficient conversion of lignocellulosic biomass into biofuels and bioproducts often requires extensive testing of components and reaction conditions used in the pretreatment, saccharification, and bioconversion steps. This restriction can result in a significant and unwieldy number of combinations of biomass types, solvents, microbial strains, and operational parameters that need to be characterized, turning these efforts into a daunting and time-consuming task. Here we developed a high-throughput feedstocks-to-fuels screening platform to address these challenges. The result is a miniaturized semi-automated platform that leverages the capabilities of a solid handling robot, a liquid handling robot, analytical instruments, and a centralized data repository, adapted to operate as an ionic-liquid-based biomass conversion pipeline. The pipeline was tested by using sorghum as feedstock, the biocompatible ionic liquid cholinium phosphate as pretreatment solvent, a "one-pot" process configuration that does not require ionic liquid removal after pretreatment, and an engineered strain of the yeast Rhodosporidium toruloides that produces the jet-fuel precursor bisabolene as a conversion microbe. By the simultaneous processing of 48 samples, we show that this configuration and reaction conditions result in sugar yields (~70%) and bisabolene titers (~1500 mg/L) that are comparable to the efficiencies observed at larger scales but require only a fraction of the time. We expect that this Feedstocks-to-Fuels pipeline will become an effective tool to screen thousands of bioenergy crop and feedstock samples and assist process optimization efforts and the development of predictive deconstruction approaches.

Development and Application of Digital Twin Control in Flexible Manufacturing Systems

Flexible manufacturing systems (FMS) are highly adaptable production systems capable of producing a wide range of products in varying quantities. While this flexibility caters to evolving market demands, it also introduces complex scheduling and control challenges, making it difficult to optimize productivity, quality, and energy efficiency. This paper explores the application of digital twin technology to tackle these challenges and enhance FMS optimization and control. A digital twin, constructed by integrating simulation models, data acquisition, and machine learning algorithms, was employed to replicate the behavior of a real-world FMS. This digital twin enabled real-time dynamic optimization and adaptive control of manufacturing operations, facilitating informed decision making and proactive adjustments to optimize resource utilization and process efficiency. Computational experiments were conducted to evaluate the digital twin implementation on an FMS equipped with robotic material handling, CNC machines, and automated inspection. Results demonstrated that the digital twin significantly improved FMS performance. Productivity was enhanced by 14.53% compared to conventional methods, energy consumption was reduced by 13.9%, and quality was increased by 15.8% through intelligent machine coordination. The dynamic optimization and closed-loop control capabilities of the digital twin significantly improved overall equipment effectiveness. This research highlights the transformative potential of digital twins in smart manufacturing systems, paving the way for enhanced productivity, energy efficiency, and defect reduction. The digital twin paradigm offers valuable capabilities in modeling, prediction, optimization, and control, laying the foundation for next-generation FMS.

Joint buffetings suppression and trajectory tracking by fuzzy-based super-twist sliding mode control: Application to a fast parallel robot for PnP operations

This paper deals with the suppression of robotic joint buffetings for enhanced precision of trajectory tracking by means of fuzzy-based super-twist sliding mode control, which is illustrated with a fast pick-and-place parallel robot for material handling. Prior to control design, the joint motions and torques are measure to identify the dynamic parameters of the robot with recursive least square method, which is experimentally validated for the further model-based control design. In order to suppress the joint buffetings and to improve the tracking accuracy, the control law by integrating fuzzy algorithm and second-order sliding mode control is designed, where the control performance is evaluated by observing the joint dynamics, compared to the classical computed torque control and fuzzy sliding mode variable structure control. The experimental results and comparative study show the effectiveness of the developed control scheme, regarding the joint buffetings suppression and trajectory tracking precision. The main contribution lies in the integrated fuzzy algorithm and second-order sliding mode control for the model-based control design, with acceptable computational cost and trajectory tracking accuracy, from the perspective of actual engineering application.

基于势场蚁群算法的农业仓库物流搬运机器人路径优化研究

In the layout of modern agricultural warehouse logistics handling industry, it is an inevitable way to realize industrial upgrading by replacing people with mobile robots. Aiming at the problems that the existing obstacle avoidance control algorithm of agricultural handling robot is easy to fall into local optimal solution, and the operation process of agricultural warehouse logistics handling robot is prone to collision, the obstacle avoidance control of agricultural warehouse logistics handling robot is studied, and a control algorithm based on improved potential field ant colony is proposed. The moving trajectory of the agricultural warehouse logistics handling robot during the handling process is studied, and the spatial kinematics equation of the robot is given. The ant colony algorithm is used to optimize the classical artificial potential field algorithm to improve the global optimization ability and balance the interaction between gravity and repulsion. In the aspect of local area obstacle avoidance of agricultural storage and handling robots, the artificial potential field is optimized twice based on the strategy gradient algorithm. By analyzing the probability of the next action command, the randomness of the travel path selection when multiple robots work at the same time is improved. After testing, the path of the proposed control algorithm is the shortest, and under the condition of complex path planning, the number of collisions between robots is also significantly less than that of the traditional obstacle avoidance control algorithm. The practical application can meet the needs of improving the efficiency of warehouse logistics management.

Unlocking maintenance-architecting STEP for maintenance and realizing remountable magnet joints.

The architecture of the Spherical Tokamak for Energy Production (STEP) has been developed to enable a hybrid maintenance approach using ports in the vacuum vessel for a limited list of tasks that must be performed shortly after shutdown, and larger openings to simplify and speed up major refits. Robotic handling systems in zero-human entry facilities will prevent workers from being exposed to the most hazardous environments. While the approach is largely grounded in existing technologies, the scale and environment of STEP will require significant technology development. Notably, programmes have been established to develop service connections and in-vessel robotic technologies. The engineering integration of the maintenance strategy into the tokamak remains a priority, as does ongoing work to simplify and reduce the cost of the buildings required to facilitate maintenance. Remountable magnet joints are critical to ensuring life-limited magnet components can be replaced during the STEP lifetime and realizing the STEP maintenance strategy. It is a high-risk endeavour owing to the low technology maturity of the potential solutions and owing to the tough and intertwined technical challenges and constraints imposed by both the fundamental physics and the STEP requirements and architecture. An integrated design approach has been taken to balance many competing factors and integrate with interfacing systems, and a multi-faceted technology development programme has been established to address technical risk and to inform, verify and validate the STEP remountable magnet design. This article is part of the theme issue 'Delivering Fusion Energy - The Spherical Tokamak for Energy Production (STEP)'.

A droplet robotic system enabled by electret-induced polarization on droplet

Robotics for scientific research are evolving from grasping macro-scale solid materials to directly actuating micro-scale liquid samples. However, current liquid actuation mechanisms often restrict operable liquid types or compromise the activity of biochemical samples by introducing interfering mediums. Here, we propose a robotic liquid handling system enabled by a novel droplet actuation mechanism, termed electret-induced polarization on droplet (EPD). EPD enables all-liquid actuation in principle and experimentally exhibits generality for actuating various inorganic/organic liquids with relative permittivity ranging from 2.25 to 84.2 and volume from 500 nL to 1 mL. Moreover, EPD is capable of actuating various biochemical samples without compromising their activities, including various body fluids, living cells, and proteins. A robotic system is also coupled with the EPD mechanism to enable full automation. EPD’s high adaptability with liquid types and biochemical samples thus promotes the automation of liquid-based scientific experiments across multiple disciplines.

Chlorophyll content estimation and ripeness detection in tomato fruit based on NDVI from dual wavelength LiDAR point cloud data

Tomato (Solanum lycopersicum L.) is a climacteric fruit exhibiting the ripening pattern with change in colour from green to red. Normalized difference vegetation index (NDVI) derived from spectral-optical analysis has been previously employed in ripeness analysis of various fruits based on its strong correlation to ripening-related chlorophyll content. In this study, light detection and ranging (LiDAR) laser scanner was applied to gain 3D tomato point clouds aimed at estimating the spatially resolved chlorophyll content and ripeness class of tomato fruit. Freshly harvested tomatoes, capturing six ripeness classes (mature green, breaker, turning, pink, light red, and red) according to USDA colour standard, were analysed using a linear conveyor mounted sensor system consisting of two LiDAR units measuring position and return signal strength intensity at 660 nm and 905 nm. Fruit point clouds were pre-processed including geometric correction considering the curvature of each tomato sample, removing highest intensity areas at the specular highlighted spots, calibration of intensity values using standard black and white colour coated boards, and calibration on attenuation of the opaque samples. Particularly, for gaining the attenuation coefficient (μ), 9 opaque synthetic models with known attenuation were used. Obtained μ660 and μ905 were merged to gain NDVILiDAR. Chemically analysed chlorophyll content of tomato samples was correlated to μ660 and NDVILiDAR, whereas low correlation appeared for μ905 with a coefficient of determination (R2) of 0.58, 0.60, and 0.06, respectively. Regression models were used to estimate the total tomato chlorophyll content and the lowest root mean square error (RMSE) was found for μ660 (RMSE = 4.97 mg (100 g dry mass)−1) followed by NDVILiDAR (RMSE = 5.22 mg (100 g dry mass)−1), and μ905 (RMSE = 53.50 mg (100 g dry mass)−1). Histograms of NDVILiDAR and μ660 were extracted from each tomato point cloud and utilized to construct PLS-DA model for tomato ripening class prediction considering the spatial chlorophyll distribution. The overall accuracies for NDVILiDAR and μ660 were 70 % and 68 %, respectively, in leave-one-out cross-validation for visually defined colour classes. The LiDAR-based approach could support selective detection of ripe fruit in robots for harvesting and postharvest handling.

An Intelligent Human–Machine Interface Architecture for Long-Term Remote Robot Handling in Fusion Reactor Environments

This paper addresses the intricate challenge posed by remote handling (RH) operations in facilities with operational lifespans surpassing 30 years. The extended RH task horizon necessitates a forward-looking strategy to accommodate the continuous evolution of RH equipment. Confronted with diverse and evolving hardware interfaces, a critical requirement emerges for a flexible and adaptive software architecture based on changing situations and past experiences. The paper explores the inherent challenges associated with sustaining and upgrading RH equipment within an extended operational context. In response to this challenge, a groundbreaking, flexible, and maintainable human–machine interface (HMI) architecture named MAMIC is designed, guaranteeing seamless integration with a diverse range of RH equipment developed over the years. Embracing a modular and extensible design, the MAMIC architecture facilitates the effortless incorporation of new equipment without compromising system integrity. Moreover, by adopting this approach, nuclear facilities can proactively steer the evolution of RH equipment, guaranteeing sustained performance and compliance throughout the extended operational lifecycle. The proposed adaptive architecture provides a scalable and future-proof solution, addressing the dynamic landscape of remote handling technology for decades.

End-to-End lightweight Transformer-Based neural network for grasp detection towards fruit robotic handling

Robotic picking and placing are common operations for fruits and vegetables in grading, sorting or packaging systems. However, due to the diverse shapes and irregular surfaces of fruits and vegetables, improper handling during the picking process can result in detachment or damage. To ensure the correct grasping positions, it is necessary to design targeted neural network algorithms for achieving intelligent sorting. Therefore, this study focuses on 20 common fruit and vegetable agricultural products to develop a deep learning-based grasping detection algorithm model. By combining local features from convolutional neural networks with global features from Transformers, a lightweight end-to-end fruit and vegetable grasping detection network, MDETR, is constructed. Experimental results demonstrate that the MDETR algorithm not only achieves high accuracy in fruit and vegetable grasping detection but also improves the speed of pose detection. The average time required for detecting a single image is approximately 29.6 ms, meeting real-time requirements. The algorithm achieves a pose detection accuracy rate of 96 %, enabling precise detection and positioning of fruit and vegetable poses and achieving fast and accurate picking and placing. Additionally, a Pybullet simulation platform is developed for conducting grasping experiments, where the MDETR model achieves a grasping success rate of 88.9 %. This validates the robustness and generalization capabilities of the proposed detection algorithm model, designed specifically for fruit and vegetable grasping tasks.

Thermal and Fast Neutron Measurement in the STU Mini Labyrinth Experiment

The Mini Labyrinth experiment is a neutron and gamma shielding experiment constructed at the Slovak University of Technology in Bratislava (STU). The STU Mini Labyrinth consists of NEUTRONSTOP shielding, blocks of moderators, various neutron sources, a graphite prism, and the detector handling robot. It was designed for research and education purposes, while several experiments are also available online or a hybrid form. There have been several versions of the Mini Labyrinth developed, while currently the V3 version is available. This paper presents the methodology to effectively perform thermal and fast neutron measurements using a PuBe neutron source. In the experiment presented in this paper also active and passive detectors were used, and moderator materials were investigated to slightly shift the neutron spectrum. As for active detectors, the SNM-11 boron coated corona detector was used. Among passive detectors the CR-39 track detectors were investigated. The measurements were carried out in two configurations and the results were evaluated by simulations using the SCALE6 system

Multimodal immersive digital twin platform for cyber–physical robot fleets in nuclear environments

AbstractThe nuclear energy sector can benefit from mobile robots for remote inspection and handling, reducing human exposure to radiation. Advances in cyber–physical systems have improved robotic platforms in this sector through digital twin (DT) technology. DTs enhance situational awareness for robot operators, crucial for safety in the nuclear energy sector, and their value is anticipated to increase with the growing complexity of cyber–physical systems. The primary motivation of this work is to rapidly develop and evaluate a robot fleet interface that accounts for these benefits in the context of nuclear environments. Here, we introduce a multimodal immersive DT platform for cyber–physical robot fleets based on the ROS‐Unity 3D framework. The system design enables fleet monitoring and management by integrating building information models, mission parameters, robot sensor data, and multimodal user interaction through traditional and virtual reality interfaces. A modified heuristic evaluation approach, which accounts for the positive and negative aspects of the interface, was introduced to accelerate the iterative design process of our DT platform. Robot operators from leading nuclear research institutions (Sellafield Ltd. and the Japan Atomic Energy Agency) performed a simulated robot inspection mission while providing valuable insights into the design elements of the cyber–physical system. The three usability themes that emerged and inspired our design recommendations for future developers include increasing the interface's flexibility, considering each robot's individuality, and adapting the platform to expand sensor visualization capabilities.

Intelligent Lighting System Using Color-Based Image Processing for Object Detection in Robotic Handling Applications

In applications reliant on image processing, the management of lighting holds significance for both precise object detection and efficient energy utilization. Conventionally, lighting control involves manual switching, timed activation or automated adjustment based on illuminance sensor readings. This research introduces an embedded system employing image processing methodologies for intelligent ambient lighting, focusing specifically on reference-color-based illumination for object detection and positioning within robotic handling scenarios. Evaluating the system’s efficacy entails analyzing the illuminance levels and power consumption through a tailored experimental setup. To minimize illuminance, the LED-based lighting system, controlled via pulse-width modulation (PWM), is calibrated using predetermined red, green, blue and yellow (RGBY) reference objects, obviating the need for external sensors. Experimental findings underscore the significance of color choice in detection accuracy, highlighting yellow as the optimal color requiring minimal illumination. Successful object detection based on color is demonstrated at an illuminance level of approximately 50 lx, accompanied by energy savings contingent upon ambient lighting conditions.

High-Scale 3D-Bioprinting Platform for the Automated Production of Vascularized Organs-on-a-Chip.

3D bioprinting possesses the potential to revolutionize contemporary methodologies for fabricating tissue models employed in pharmaceutical research and experimental investigations. This is enhanced by combining bioprinting with advanced organs-on-a-chip (OOCs), which includes a complex arrangement of multiple cell types representing organ-specific cells, connective tissue, and vasculature. However, both OOCs and bioprinting so far demand a high degree of manual intervention, thereby impeding efficiency and inhibiting scalability to meet technological requirements. Through the combination of drop-on-demand bioprinting with robotic handling of microfluidic chips, a print procedure is achieved that is proficient in managing three distinct tissue models on a chip within only a minute, as well as capable of consecutively processing numerous OOCs without manual intervention. This process rests upon the development of a post-printing sealable microfluidic chip, that is compatible with different types of 3D-bioprinters and easily connected to a perfusion system. The capabilities of the automized bioprint process are showcased through the creation of a multicellular and vascularized liver carcinoma model on the chip. The process achieves full vascularization and stable microvascular network formation over 14 days of culture time, with pronounced spheroidal cell growth and albumin secretion of HepG2 serving as a representative cell model.

Research on Running Performance Optimization of Four-Wheel-Driving Ackerman Chassis by the Combining Method of Quantitative Experiment with Dynamic Simulation

The wheeled chassis, which is the carrying device of the existing handling robot, is mostly only suitable for flat indoor environments and does not have the ability to work on outdoor rugged terrain, greatly limiting the development of chassis driven handling robots. On this basis, this paper innovatively designs a four-wheel-driving Ackerman chassis with strong vibration absorption and obstacle surmounting capabilities and conducts performance research and optimization on it through quantitative experiments and dynamic simulation. Firstly, based on the introduction of the working principle and structure of the four-wheel-driving Ackerman carrier chassis, a multi-sensor distributed dynamic performance test system is constructed through the analysis of the chassis performance evaluation index. Then, according to the quantitative operation experiment of the chassis, the vibration and acceleration characteristics of the chassis at different positions of the chassis, the amount of slip and straightness of the chassis under different running distance, and the operating characteristics of the chassis under different road conditions and different damping springs conditions were analyzed respectively, which verified the rationality of the chassis design. Finally, by constructing the chassis dynamics simulation model; the influence law of chassis structure; and performance parameters such as chassis wheelbase, guide rod structure, and parameters, wheel friction coefficient and assembly error on the dynamic characteristics of the chassis is studied, and the optimal structure of the four-wheel-driving Ackerman chassis is determined while it is verified based on the simulation results. The research shows that the four-wheel-driving Ackerman chassis has good vibration performance and stability and has strong adaptability to different roads. After optimization, the vibration performance, stability, amount of slip, and straightness of the chassis structure are significantly improved, and the straightness is reduced to 0.399%, which is suitable for precise carriage applications on the chassis. The research has important guiding significance for promoting the development and application of wheeled chassis.

Intelligent Logistics Handling Robot: Design, Control, and Recognition

Intelligent Logistics Handling Robot: Design, Control, and Recognition

A Design of Intelligent Handling Robot Based on AT89C52

A Design of Intelligent Handling Robot Based on AT89C52

CERN-MEDICIS: Operational indicators to support the production of new medical radionuclides by mass-separation

CERN-MEDICIS is an isotope mass separation facility dedicated to biomedical research located in a type A work sector, receiving on average 50% of the 1.4 GeV protons delivered by the Proton Synchrotron Booster (PSB). It was commissioned with Radioactive Ion Beams (RIB’s) in 2017. MEDICIS has operated for the past 5 years in batch mode, with targets irradiated in a station located at the HRS beam dump, and with external sources provided by MEDICIS cyclotrons and nuclear reactors partners, notably during the Long Shutdown (LS2). Additional features of the facility include the MELISSA laser ion source, radiochemistry on implanted radionuclides and an online gamma-ray spectroscopy implantation monitoring. In 2022, we introduced Key Performance Indicators (KPI’s) to monitor the operation of the facility for collected efficiencies, the optimisation of the radiological risks and evaluate impact of possible modifications of the station, paralleling for instance LHC’s integrated luminosity. Defined KPI’s cover aspects in the operation cycle, e.g. planning in CERN schedule, target irradiations, duration of the process, radiological risk mitigation, facility up-time, developments and maintenance. MEDICIS KPI’s can help distinguish which of the operation and infrastructure life cycle requires immediate intervention, developments or consolidation. Those are related to the irradiation stations and irradiation possibilities, the beamlines (parallel collections), target and ion sources (reliability), robot handling and infrastructure, or the separation process itself.

Development of an EOD Robot for the Arequipa Explosive Disposal Unit

The JVC 0.2 is an Explosive Ordnance Disposal (EOD) robot with tracks, a 5 Degrees of Freedom (DOF) arm, and a two-finger gripper developed by the National University of San Agustin (UNSA) and the Explosive Ordnance Disposal Unit of the Police of Arequipa (UDEX) in response to the technical needs of adequate weight for manual transport in compliance with the law 29,088 in force in Peru, urban multi-terrain locomotion skills, robotic arm control and handling of explosive objects with sizes and weights registered in the UDEX database. The objective of this document is to develop and test the robot for maneuverability, locomotion, and arm strength based on National Institute of Standards and Technology (NIST) standards adapted to the needs of UDEX to safeguard the lives of Explosive Ordnance Disposal Specialists (TEDAX). The test procedures were carried out to evaluate the robot’s capabilities in terms of maneuverability and were designed to time the transport of objects simulating explosives at different inclinations to a container. The tests showed us that the actuation time of the robotic arm was improved, that it can climb stairs with a maximum inclination of 20°, that the locomotion speed measured on flat ground was 10.94 cm/s and that the load capacity at the extended arm is 9 kg. Finally, we conclude that it is necessary to update the design of the robot to reduce the weight to meet the safety standards at work, increase the load capacity up to 10 Kg, and improve mobility; so that, it becomes of daily use by the TEDAX.

Trial of Robot Handling Practice with a Sorting Task

Trial of Robot Handling Practice with a Sorting Task