Robotic Solutions Research Articles

Machine learning and IoT Based Robotic Solution for Home Wrist Rehabilitation through Motion Capture Enabled Architecture

The present study investigates the effectiveness of an integrated approach that combines machine learning, robotic rehabilitation, and traditional methods for wrist rehabilitation among the hemiparesis population. Subjects with robotic rehabilitation at the four-week intervention period showed the ball’s significant difference in the flexion and extension angles and demonstrated the better overall outcomes from the robotic intervention . The machine learning models, in particular, the artificial neural networks, provided the prediction of rehabilitation outcomes and demonstrated the high accuracy, high precision, high recall, and high F1 score. This study supports the idea of the beneficial effects of combining robotic rehabilitation with machine learning algorithms and supports the notion that the two benefits could bring high synergistic effects above their individual potentials. The report also contributes to the development of the rehabilitation science of wrists, given that few studies have explored the applications of artificial intelligence methods or robotic rehabilitation beyond the context of feasibility reports. The findings of the current research could contribute useful insights for the decision-making practices of therapists and healthcare providers, also given the reliance on large data contributed by the previous interventions. Future study could investigate the long-term results, the cost-effectiveness of the approaches, and assist in developing the potential results of customization for individual patients.

Circuit design solutions for underwater detection robots

In recent years, the exploration and utilisation of the environment has become in full swing with the advancement of technology. However, the environment has also suffered a certain degree of damage in the process. As a result of the increasingly serious environmental problems, various environmental issues have become hot spots in society. Among them, marine environmental problems have undoubtedly become one of the focuses of attention. Therefore, the robot design for underwater water quality detection has become an important issue. This paper will mainly focus on the design and research of the hardware circuit system of the underwater detection robot, and give the relevant design scheme. The underwater robot will achieve the following functions: floating suspension at a certain depth in the ocean; synchronously detecting the radiation intensity of x-rays and -rays underwater and transmitting the data to the ground to achieve real-time monitoring; maintaining the dynamic balance in the water. Therefore, this paper analyses the pressure sensor circuit, the inertial sensor circuit, the underwater x-ray and -ray detection probe circuit and the DC motor drive circuit, determines the interface relationship of each part, and at the same time, uses the Arduino mega 2560 as the control panel, and provide the appropriate voltage according to the needs of each circuit to achieve the hardware circuit design of the robot.

Solar Based Floor Cleaning Robot Using IOT

This project introduces a new type of robot, Solar-Powered Autonomous Floor Cleaning Robot in response to the increasing demand of sustainable and energy-efficient solutions in robotics. The robot is designed to move on its own indoors, outdoors and clean different types of floors while getting power from solar panels that are integrated into its body. By using this method, it tries to reduce the impact on the environment that comes with conventional cleaning methods as well as increase the efficiency of floor maintenance generally.The primary objective of the proposed robot is to address the challenge of maintaining optimal efficiency in solar energy harvesting by ensuring the cleanliness of solar panels in industrial environments. The robot incorporates autonomous navigation and cleaning capabilities, employing advanced sensors and artificial intelligence algorithms to detect and navigate around obstacles while efficiently cleaning the solar panel surfaces.The robot's design emphasizes modularity and scalability, allowing it to adapt to diverse industrial environments and solar panel configurations. Equipped with cleaning brushes and a water-efficient system, the robot ensures thorough cleaning without causing damage to the solar panels. Additionally, the integration of real-time monitoring capabilities enables remote tracking of cleaning operations and assessment of overall system performance.

Novel Adaptive Control for Flexible-Joint Robots With Unknown Measurement Sensitivity

For the existing tracking control schemes of flexible-joint robots, precise sensor measurement is an implicit premise. However, idealized sensors are difficult to achieve due to manufacturing technology or other external factors. To this end, this paper further investigates the tracking control problem for flexible-joint robots with unknown measurement sensitivity. Specifically, for such multi-input multi-output Euler-Lagrange systems with completely unknown system dynamics, a novel measurement values-based adaptive control method is proposed by fusing sensitivity information and system variables into Lyapunov function candidates, where the restriction on system states in other the approximation lemma-based results is removed, since unknown nonlinearities are scaled by the structural characteristics of system variables. Above all, even if there are measurement errors, satisfactory tracking performance can be obtained by adjusting the design parameters, which is proved by rigorous theoretical analysis. Finally, hardware experiments further verify the effectiveness of the proposed method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work is motivated by the trajectory tracking control problem for flexible-joint robots under imprecise sensor measurements. Due to manufacturing technology limitations and component aging, there is inevitably a deviation between the measured values of sensors and real values, and this problem may become more prominent as the working environment of flexible-joint robots tends to become more complex. To our knowledge, most of the existing solutions for flexible-joint robots are developed based on precise sensor measurements, and they may fail to achieve satisfactory performance when real state information is not available. Moreover, the prior knowledge about model parameters and measurement sensitivity is difficult or impossible to exactly obtain in practice, which seriously hinders the further application of control methods that are dependent on system dynamics. To this end, this paper proposes a novel tracking control scheme based on measurement information for flexible-joint robots with unknown measurement sensitivity, where the dependence on model information is eliminated with the elaborately constructed Lyapunov function candidates, and the real tracking error is still adjusted to an acceptable range even if there are measurement errors. Preliminary experiments on a flexible-joint robot developed by Quanser company demonstrate the feasibility and effectiveness of the proposed method. In future studies, designing an effective scheme to achieve direct preset tracking control is the focus of the work.

Research on LFD System of Humanoid Dual-Arm Robot

Although robots have been widely used in a variety of fields, the idea of enabling them to perform multiple tasks in the same way that humans do remains a difficulty. To solve this, we investigate the learning from demonstration (LFD) system with our independently designed symmetrical humanoid dual-arm robot. We present a novel action feature matching algorithm. This algorithm accurately transforms human demonstration data into task models that robots can directly execute, considerably improving LFD’s generalization capabilities. In our studies, we used motion capture cameras to capture human demonstration actions, which included combinations of simple actions (the action layer) and a succession of complicated operational tasks (the task layer). For the action layer data, we employed Gaussian mixture models (GMM) for processing and constructing an action primitive library. As for the task layer data, we created a “keyframe” segmentation method to transform this data into a series of action primitives and build another action primitive library. Guided by our algorithm, the robot successfully imitated complex human tasks. Results show its excellent task learning and execution, providing an effective solution for robots to learn from human demonstrations and significantly advancing robot technology.

Design, integration, and field evaluation of a robotic blossom thinning system for tree fruit crops

AbstractThe United States (US) apple industry relies heavily on semi‐skilled manual labor force for essential field operations such as training, pruning, blossom and green fruitlet thinning, and harvesting. Blossom thinning is one of the crucial crop‐load management practices to achieve desired crop load, fruit quality, and return bloom. While several techniques such as chemical and mechanical thinning are available for large‐scale blossom thinning, such approaches often yield unpredictable thinning results and may damage the canopy, spurs, and leaf tissue. Hence, growers still depend on laborious, labor‐intensive, and expensive manual hand blossom thinning for desired thinning outcomes. This research presents a robotic solution for precision blossom thinning in apple orchards using a deep learning‐based computer vision system, a six‐degrees‐of‐freedom UR5e robotic manipulator, and an electrically actuated miniature end‐effector. The integrated robotic system was evaluated in a commercial apple orchard which showed promising results for targeted and selective blossom thinning. Two thinning approaches, center and boundary thinning, were investigated to evaluate the system's ability to remove varying proportions of flowers from apple flower clusters. During boundary thinning, the end‐effector was actuated around the cluster boundary, while center thinning involved end‐effector actuation only at the cluster centroid for a fixed duration of 2 s. Field evaluation results showed that the boundary thinning approach thinned 67.2% of flowers from the targeted clusters with a cycle time of 9.0 s per cluster, whereas the center thinning approach thinned 59.4% of flowers with a cycle time of 7.2 s per cluster. Upon further improvement for commercial adoption, the proposed system could help address problems faced by apple growers with current hand, chemical, and mechanical blossom thinning approaches.

4D Printed Self‐Sustained Soft Crawling Machines Fueled by Constant Thermal Field

AbstractEnvironmentally fueled sustainable autonomous locomotion is an advanced intelligent behavior analogous to unconscious actions in biological organisms, such as heartbeat and peristalsis. In this study, a self‐sustaining oscillating soft robot is introduced with eccentric hinge structures inspired by the crawling locomotion of mollusks. A parameter‐encoded 4D printing method is utilized to pre‐program local strain of the eccentric hinge structure, and thus endows the soft robot with sustainable crawling motions under constant thermal field. Through the coupling design of substrate friction, eccentric hinge constitution, and the substrate temperature, the soft robot can achieve tunable crawling, rolling, and oscillating. Two applications, namely an optical chopper and a power generator are presented as proof of concepts. This work offers a viable solution for soft robots for applications in constrained environments such as the aerospace and medical fields.

Conceptualization and Topology Optimization of Ampheel: An Integration of Rolling Wheel and Turtle-Inspired Mechanism for Amphibious Mobile Robot

The primary distinguishing feature of mobile robots is the ability to traverse various environments, setting them apart in the realm of robotics. The mobility of a robot hinges primarily on its locomotion mechanism, which dictates how it moves. The existing unimodal mobile robots are limited to work within the environment for which they are designed for and hence lack a scope to adapt the change in the terrain especially when they put to work in a mixed environment like land and water. Many applications like land and underwater search/rescue, shore infrastructure inspection, coastal area defence and security, offshore energy harvesting, space exploration, etc. demand a mobile robot that can traverse in both terrestrial and aquatic environments with the help of dual or multimodal locomotion mechanism, something like an amphibious animal. Most of the available amphibious robotic solutions have different appendages for both the environment, need human intervention to changeover the mechanism for transition, require different driving system for land and water locomotion and have fragile structures that limit the manoeuvrability. The proposed conceptual design called “Ampheel” is a novel amphibious locomotion mechanism inspired by the biomechanics of freshwater turtles. Ampheel incorporates a rigid wheel, enabling the robot to move on land, integrating soft actuators within it which emulate the turtle's leg-like extensions and enable the aquatic locomotion. Unlike the existing amphibious robots, the Ampheel utilizes the rotational motion of itself as a common driving system for both the environments. This reduces the need of multiple driving systems and also simplifies the control system. Ampheel is designed for safe travelling on land considering the maximum payload of robot as 20 kg including self-weight. Topology optimization of Ampheel is also carried out using ANSYS software for reduction of weight. Additionally, a unique interfacing shaft is designed that transmits the required torque to Ampheel for rotation and also channelise the compressed air to soft pneumatic actuators for inflation during rotation of Ampheel in aquatic setting. The fabricated Ampheel assembly is experimentally checked for failure under the applicable loading condition and found safe.

Design and Characterization of Soft Fabric Omnidirectional Bending Actuators

Soft robots, inspired by biological adaptability, can excel where rigid robots may falter and offer flexibility and safety for complex, unpredictable environments. In this paper, we present the Omnidirectional Bending Actuator (OBA), a soft robotic actuation module which is fabricated from off-the-shelf materials with easy scalability and consists of three pneumatic chambers. Distinguished by its streamlined manufacturing process, the OBA is capable of bending in all directions with a high force-to-weight ratio, potentially addressing a notable research gap in knit fabric actuators with multi-degree-of-freedom capabilities. We will present the design and fabrication of the OBA, examine its motion and force capabilities, and demonstrate its capability for stiffness modulation and its ability to maintain set configurations under loads. The mass of the entire actuation module is 278 g, with a range of omnidirectional bending up to 90.80°, a maximum tolerable pressure of 862 kPa, and a bending payload (block force) of 10.99 N, resulting in a force-to-weight ratio of 39.53 N/kg. The OBA’s cost-effective and simple fabrication, compact and lightweight structure, and capability to withstand high pressures present it as an attractive actuation primitive for applications demanding efficient and versatile soft robotic solutions.

Power Consumption Analysis of a Prototype Lightweight Autonomous Electric Cargo Robot in Agricultural Field Operation Scenarios

The continuous growth of the urban electric vehicles market and the rapid progress of the electronics industry create positive prospects towards fostering the development of autonomous robotic solutions for covering critical production sectors. Agriculture can be seen as such, as its digital transformation is a promising necessity for protecting the environment, and for tackling the degradation of natural resources and increasing nutritional needs of the population on Earth. Many studies focus on the potential of agricultural robotic vehicles to perform operations of increased intelligence. In parallel, the study of the activity footprint of these vehicles can be the basis for supervising, detecting the malfunctions, scaling up, modeling, or optimizing the related operations. In this regard, this work, employing a prototype lightweight autonomous electric cargo vehicle, outlines a simple and cost-effective mechanism for a detailed robot’s power consumption logging. This process is conducted at a fine time granularity, allowing for detailed tracking. The study also discusses the robot’s energy performance across various typical agricultural field operation scenarios. In addition, a comparative analysis has been conducted to evaluate the performance of two different types of batteries for powering the robot for all the operation scenarios. Even non-expert users can conduct the field operation experiments, while directions are provided for the potential use of the data being collected. Given the linear relationship between the size and the consumption of electric robotic vehicles, the energy performance of the prototype agricultural cargo robot can serve as a basis for various studies in the area.

Towards Industrial Robots’ Maturity: An Italian Case Study

This paper aims to investigate the impact of industrial robotics, examining the process integration in a sample of +600 companies located in the Province of Brescia, an intensive industrial area in the North of Italy. Through a detailed economic investigation, this study analyses the adoption of robotic solutions in companies of varying sizes, using a survey and financial databases to investigate the most used types of robots, their applications, the impacts at the operational and personnel level, and the companies’ growth (sales, employees, other). The results highlight a significant presence of robotic solutions, particularly articulated robots, in the large companies involved. Robotics diffusion positively correlates with significant improvements in terms of productivity and quality. The introduction of robots is associated with increased corporate growth indicators, including staff expansion. Large companies demonstrate a superior ability to adapt to these technologies, supported by more significant financial resources and a wide range of internal competencies for managing robots. Furthermore, large companies proactively hire qualified personnel or initiate internal training courses. Small and medium-sized enterprises (SMEs), although currently less equipped with robotic technologies, exhibit a significant interest in future adoption, highlighting the opportunity for growth and innovation. The results suggest that integrating robotics in the manufacturing sector not only constitutes an effective means to enhance operational performance but also acts as a catalyst for developing human capital and strengthening the local economy.

Robotization of “in situ” acoustic measurements

Currently, “in situ” acoustic measurements require a high level of human involvement. Measurements are carried out by technicians or engineers, and most of the time, two people are required. MJM Acoustical Consultants Inc. has explored the possibility of using an autonomous and semi-autonomous robotic solution to assist in the measurement process. Developed in partnership with university Polytechnique Montreal, the research project consists of developing a software platform and hardware for a mobile robot capable of moving around, locating itself and performing measurements using a Type I sound level meter. The prototype generates its own local area network (LAN), making it possible to send commands through high-density partitions such as brick walls or concrete floors remotely from a laptop. Localization system such as a laser remote sensing system allows the robot to navigate around obstacles. Its low noise level when stationary makes it ideally suited to conduct common standard tests such as impact noise isolation, airborne sound attenuation between rooms and background noise level measurements. Through tests carried out on field sites, the solution developed constitutes an effective instrumentation to make acoustic measurements sessions more efficient with a reduced level of human operation.

Development of a robotic cluster for automated and scalable cell therapy manufacturing

Background aimsThe production of commercial autologous cell therapies such as chimeric antigen receptor T cells requires complex manual manufacturing processes. Skilled labor costs and challenges in manufacturing scale-out have contributed to high prices for these products. MethodsWe present a robotic system that uses industry-standard cell therapy manufacturing equipment to automate the steps involved in cell therapy manufacturing. The robotic cluster consists of a robotic arm and customized modules, allowing the robot to manipulate a variety of standard cell therapy instruments and materials such as incubators, bioreactors, and reagent bags. This system enables existing manual manufacturing processes to be rapidly adapted to robotic manufacturing, without having to adopt a completely new technology platform. Proof-of-concept for the robotic cluster's expansion module was demonstrated by expanding human CD8+ T cells. ResultsThe robotic cultures showed comparable cell yields, viability, and identity to those manually performed. In addition, the robotic system was able to maintain culture sterility. ConclusionsSuch modular robotic solutions may support scale-up and scale-out of cell therapies that are developed using classical manual methods in academic laboratories and biotechnology companies. This approach offers a pathway for overcoming manufacturing challenges associated with manual processes, ultimately contributing to the broader accessibility and affordability for personalized immunotherapies.

RoBUTCHER: A novel robotic meat factory cell platform

Automation is critically important for sustainability in meat production, where heavy reliance on human labour is a growing challenge. In this work, a novel robotic Meat Factory Cell (MFC) platform presents the opportunity for unconventional automation in pork meat processing, particularly abattoirs. Instead of following line-based approaches, which are the main option today, it uses robotics and Artificial Intelligence (AI) to perform complex cutting and manipulation operations on entire unchilled pork carcasses, with awareness of biological variation and deformation. The long-term goal of the MFC is to take a pork carcass as an input and produce seven primal outputs: hams, shoulders, saddle, belly and entire organ set. However, the MFC platform is under continuous development – therefore, this paper aims to demonstrate it through a specific use-case: shoulder removal. The system is evaluated based on data from testing and development sessions (June–November 2022), with a total of 34 attempted shoulder removals. Data regarding the MFCs’ ability to handle variation, in addition to success rate and process timing models are presented. Qualitative feedback from skilled butchers is also discussed. The authors propose that, as well as technical development of the platform, it is important to consider new ways of comparing unconventional systems with their conventional counterparts. Innovative manufacturing systems have more to offer than raw speed and volume; traits such as flexibility, robustness and scalability – particularly economic scalability – should play a prominent role. Future legislation and standards must also encourage innovation rather than hinder innovative robotics solutions.

Hyper‐Versatile Gripping: Synergizing Mechanical and Machine Intelligence of a Hybrid Robotic Gripper

Integration of robotic solutions in manufacturing sector is growing. However, it is still concentrated in certain industries (i.e., electronics and automotive) where standardization of product physical form is high. Current state‐of‐the‐art gripping solutions fall short when they need to accommodate items with high variability in physical form. This challenging scenario for automation can be found in a few industries (i.e., e‐commerce). Automation of pick‐and‐place processes in this area requires a more versatile gripping solution. To resolve this challenge, this article proposes a novel way to improve grip‐versatility by synergizing the mechanical and machine intelligence of a hybrid robotic gripper (HRG). Comparative analysis with commercial grippers shows that HRG can pick a more diverse range of items with success rate 94.78%. Visual perception‐based picking strategy is developed to automate the reconfiguration of HRG into a stable grasp pose for different objects. Using the proposed reconfigurable picking strategy, the efficacy of HRG in pick‐and‐place tasks is evaluated using three parameters—mean pick per hour (MPPH), successful execution over total attempts (SETA), and average cycle time (AVGCT). HRG can effectively pick items in cluttered workspace with MPPH of 98.54 ± 15.49, SETA of 0.93 ± 0.11, and AVGCT of 34.76 ± 3.31 s.

Nautilus: An autonomous surface vehicle with a multilayer software architecture for offshore inspection

AbstractThe increasing adoption of robotic solutions for inspection tasks in challenging environments is becoming increasingly prevalent, particularly in the offshore wind energy industry. This trend is driven by the critical need to safeguard the integrity and operational efficiency of offshore infrastructure. Consequently, the design of inspection vehicles must comply with rigorous requirements established by the offshore Operation and Maintenance (O&amp;M) industry. This work presents the design of an autonomous surface vehicle (ASV), named Nautilus, specifically tailored to withstand the demanding conditions of offshore O&amp;M scenarios. The design encompasses both hardware and software architectures, ensuring Nautilus's robustness and adaptability to the harsh maritime environment. It presents a compact hull capable of operating in moderate sea states (wave height up to 2.5 m), with a modular hardware and software architecture that is easily adapted to the mission requirements. It has a perception payload and communication system for edge and real‐time computing, communicates with a Shore Control Center and allows beyond visual line‐of‐sight operations. The Nautilus software architecture aims to provide the necessary flexibility for different mission requirements to offer a unified software architecture for O&amp;M operations. Nautilus's capabilities were validated through the professional testing process of the ATLANTIS Test Center, involving operations in both near‐real and real‐world environments. This validation process culminated in Nautilus's reaching a Technology Readiness Level 8 and became the first ASV to execute autonomous tasks at a floating offshore wind farm located in the Atlantic.

MARLIN: A cloud integrated robotic solution to support intralogistics in retail

In this paper, we present the service robot MARLIN and its integration with the K4R platform11K4R - Knowledge4Retail (https://knowledge4retail.org), a cloud system for complex AI applications in retail. At its core, this platform contains so-called semantic digital twins, a semantically annotated representation of the retail store. MARLIN continuously exchanges data with the K4R platform, improving the robot’s capabilities in perception, autonomous navigation, and task planning. We exploit these capabilities in a retail intralogistics scenario, specifically by assisting store employees in stocking shelves. We demonstrate that MARLIN is able to update the digital representation of the retail store, autonomously plan and execute replenishment missions, adapt to unforeseen changes in the environment, and interact with store employees. Experiments are conducted in simulation, in a laboratory environment, and in a real store. We also describe MARLIN’s capabilities in terms of obstacle detection and navigation in confined spaces.

Tackling complex thoracic surgical operations with robotic solutions: a narrative review.

The adoption of robotic surgery for general thoracic surgery has rapidly progressed over the last two decades from its application in basic operations to complex pathologies. As such, the purpose of this narrative review is to highlight the collective experience of tackling complex thoracic surgical operations with minimally invasive robotic solutions. Electronic searches of PubMed were conducted for each subtopic, using specific keywords and inclusion criteria. Once identified, the articles were screened through the abstract, introduction, results and conclusion for relevancy, and included based on a standard narrative review inclusion criteria. The role of the robotic approach has increased in thoracic outlet syndrome, chest wall resection, tracheobronchomalacia, airway and sleeve lung surgery, lobectomy after neoadjuvant therapy, complex segmentectomy, giant paraesophageal hernia repair, esophagectomy and esophageal enucleation, mediastinal masses and thymectomy and lung transplantation. Robotic surgery has several advantages when compared to video-assisted and open thoracoscopic surgery. These include better pain control and aesthetic outcome, improved handling of complex anatomy, enhanced access to lymph nodes, and faster recovery rates. Although it is associated with longer operative time, robotic surgery has comparable morbidity rates. The robotic approach to complex thoracic problems is safe, effective, and associated with improved patient outcomes. To encourage wider adoption of robotic technology, increased training and expanded research efforts are essential, alongside improved worldwide access to this technology.

A Robotic Solution to Peg in/out Hole Tasks with Latching Requirements

A Robotic Solution to Peg in/out Hole Tasks with Latching Requirements

Experimental validation of a constrained control architecture for a multi-robot bricklayer system

Robotics in construction is an emerging field that aims to automate various construction activities. Among the various innovative technologies for the construction sector, in this paper we focus on robotic solutions for the bricklaying task. In particular, we describe and explain in detail the implementation of a control framework for a recently introduced multi-robot bricklaying concept, specifically designed for laying activities with large and heavy blocks. The multi-robot system subject of this work is based on the collaboration of a robotic manipulator and a crane. The control architecture proposed to perform the construction task belongs to the Explicit Reference Governor (ERG) formalism. The ERG is a constrained control structure that enforces the constraints of the system and ensures that the robotic system operates correctly and safely. The efficiency of the proposed solution is confirmed by experimental validation on a custom-made crane and a KUKA LBR IIWA14R820 robotic arm.