Robot Applications Research Articles

Robotics, environmental regulation, and agricultural carbon emissions: an examination of the environmental Kuznets curve theory and moderating effects

IntroductionReducing carbon emissions from agriculture is essential to ensuring food security and human prosperity. As a country with approximately 20% of the global population, China has begun actively practicing the low-carbon agricultural development conception. Against the backdrop of disruptive technologies that continue to be integrated into various industries, the massive application of agricultural robots has opened the way to intelligent agriculture. This paper tries to answer whether there is some non-linear nexus between the application of agricultural robots and agricultural carbon emissions in China. As an essential tool for carbon emission reduction in China, does environmental regulation moderate the nexus between agricultural robot applications and agricultural carbon emissions? If so, how does this effect manifest itself?MethodsThis work takes China as an example by collecting macro-regional panel data from 30 provinces from 2006 to 2019. The environmental Kuznets curve theory is extended to agricultural carbon emissions, and we carried out empirical tests utilizing the panel fixed effects model and the moderating effects model.ResultsThis study verifies the inverted U-shaped nexus between agricultural robotics applications and agricultural carbon emissions in Chinese provinces, i.e., the agricultural carbon emissions (ACE)-Kuznets curve holds. The higher the level of formal environmental regulation, the larger the peak of the ACE-Kuznets curve and the more the inflection point is pushed back. The higher the level of informal environmental regulation, the lower the peak of the ACE-Kuznets curve and the later the inflection point.DiscussionThe findings in this paper represent the first exploration of the environmental Kuznets curve in agricultural carbon emissions. It is noteworthy that the moderating effect of formal environmental regulation does not lower the peak of the curve as we expect. This appearance is attributed to the reality that China is still in a phase of rising agricultural carbon emissions, which is exacerbated by the overlapping positive effects of agricultural robotics applications and formal environmental regulations. Informal environmental regulation is more effective than formal environmental regulation in reducing agricultural carbon emissions at this stage.

Task-Oriented Self-Imitation Learning for Robotic Autonomous Skill Acquisition

The inferior sample efficiency of reinforcement learning (RL) and the requirement for high-quality demonstrations in imitation learning (IL) will hinder their application in real-world robots. To address this challenge, a novel self-evolution framework, named task-oriented self-imitation learning (TOSIL), is proposed. To circumvent external demonstrations, the top-K self-generated trajectories are chosen as expert data from both per-episode exploration and long-term return perspectives. Each transition is assigned a guide reward, which is formulated by these trajectories. The guide rewards update as the agent evolves, encouraging good exploration behaviors. This methodology guarantees that the agent explores in the direction relevant to the task, improving sample efficiency and asymptotic performance. The experimental results on locomotion and manipulation tasks indicate that the proposed framework outperforms other state-of-the-art RL methods. Furthermore, the integration of suboptimal trajectories has the potential to improve the sample efficiency while maintaining performance. This is a significant advancement in autonomous skill acquisition for robots.

The Application of Industrial Robots in Automated Production Lines

Elaborate on the definition, structure, and characteristics of industrial robots, their application in automated production lines, and provide suggestions for balancing and optimizing industrial robots in automated production lines.

Influencing Factors as well as Implementation Path of Synergistic Development of Digitalization and Greening in Manufacturing Industry: Analysis from a Configuration Perspective

<p class="MsoNormal" style="text-align: justify;"><span lang="EN-US" style="font-family: 'times new roman', times, serif; font-size: 14pt;">The manufacturing sector must incorporate digitalization and greening as crucial strategies to attain high level of development in the context of the digital economy, given its significant role in quality of environment. Most of existing studies explore net effect of a single or several factors on the digitalization or greening in the manufacturing industry. Limited investigation has been done to integrate various factors impacting the connection and synchronized advancement of digitalization and greening within the manufacturing. Factors affecting the synergy of digitalization and green change within manufacturing are recognized in this article, and a theoretical framework is established to analyze. FsQCA tool is also put into use to survey configurational cases, which contains synergy degree of green digitalization from 30 provinces’ manufacturing in China. Results show that: (1) Between 2007 and 2020, the development level of digital-green integration in the manufacturing industry shows an upward trend. There are three types of collaborative modes, namely, "industrial robot driven" "market development driven" and "market development driven - industrial robot driven", with each mode consisting of at least one to four equivalent pathways. (2) The marketization level and application of industrial robots are the important external and internal pulling forces that drive the synergy degree of digitalization and green change within manufacturing industry, respectively.(3) The influencing factors of the collaborative mode choice of digital-green development within manufacturing industry are complementary, among which, the degree of marketization is similar to the role of industrial robots in the configuration, which are capable to weaken the negative impact from insufficient government R&D investment support under specific conditions; (4) The non-high collaborative state of digital green in manufacturing industry is mainly due to the low level of marketization, openness, and economic development, as well as the low application rate of industrial robots and software products. This study enriches the theoretical understanding on digitalization and green change of manufacturing industry. Meanwhile, it also provides insights to advance the integrated and synergistic advancement of digital green transformation in manufacturing practice.</span></p>

Semantic segmentation of thermal defects in belt conveyor idlers using thermal image augmentation and U-Net-based convolutional neural networks

The belt conveyor (BC) is the main means of horizontal transportation of bulk materials at mining sites. The sudden fault in BC modules may cause unexpected stops in production lines. With the increasing number of applications of inspection mobile robots in condition monitoring (CM) of industrial infrastructure in hazardous environments, in this article we introduce an image processing pipeline for automatic segmentation of thermal defects in thermal images captured from BC idlers using a mobile robot. This study follows the fact that CM of idler temperature is an important task for preventing sudden breakdowns in BC system networks. We compared the performance of three different types of U-Net-based convolutional neural network architectures for the identification of thermal anomalies using a small number of hand-labeled thermal images. Experiments on the test data set showed that the attention residual U-Net with binary cross entropy as the loss function handled the semantic segmentation problem better than our previous research and other studied U-Net variations.

Investigating Suitable Combinations of Dynamic Models and Control Techniques for Offline Reinforcement Learning Based Navigation: Application of Universal Omni-Wheeled Robots

Abstract Omnidirectional locomotion provides wheeled mobile robots (WMR) with better maneuverability and flexibility, which enhances their energy efficiency and dexterity. Universal omni-wheels are one of the best categories of wheels that can be used to develop a WMR (Amarasiri et al., 2022, “Robust Dynamic Modeling and Trajectory Tracking Controller of a Universal Omni-Wheeled Mobile Robot,” ASME Letters Dyn. Sys. Control., 2(4), p. 040902. 10.1115/1.4055690). We study dynamic modeling and controllers for mobile robots to train in a reinforcement learning (RL)-based navigation algorithm. RL tasks require copious amounts of learning iteration episodes, which makes training very time consuming. The choice of dynamic model and controller has a significant impact on training time. In this paper, we compare a traditional Kane’s equations model to a non-holonomic canonical momenta model (Barhorst, 2019, “Generalized Momenta in Constrained Non-Holonomic Systems—Another Perspective on the Canonical Equations of Motion,” Int. J. Non-Linear Mech., 113, pp. 128–145.). We implemented four controllers: proportional integral derivative, linear quadratic regulator with integral action, pole placement, and a full nonlinear sliding mode controller. We summarize the pros and cons of each of the modeling techniques, and control laws implemented. The outcomes of our analysis will improve RL training time for path generation in unstructured environments.

Current study on PID control method with optimized BP neural network based on particle swarm optimization

At present, robots show great application value in various industries, and the progress of the industry constantly puts forward higher requirements for the performance of robots, and robot control is an important part of robot application. It is difficult for the traditional PID controller to implement online tuning when it is faced with actual objects. Consequently, the neural network algorithm has been included into robot control methods in recent years; the PID controller based on BP neural network is the subject of this research. This study presents the current state of research, the basic concept behind BP neural networks, how they are used in controller design, and how to optimize the PID parameters of BP neural networks using a particle swarm optimization method. To increase system stability, the neural network is coupled with a PID controller. The adaptive learning capability of the neural network is utilized to modify PID control parameters online in real time. Targeting the trouble that it slips into a local minimum easily during the BP-PID self-learning process and the refined PSO algorithm is used to improve it. It makes sure that the BP-PID system converges to the global optimal solution. The proposed method can effectively improve the system control accuracy and control stability.

Kinematic Analysis and Application to Control Logic Development for RHex Robot Locomotion.

This study explores the kinematic model of the popular RHex hexapod robots which have garnered considerable interest for their locomotion capabilities. We study the influence of tripod trajectory parameters on the RHex robot's movement, aiming to craft a precise kinematic model that enhances walking mechanisms. This model serves as a cornerstone for refining robot control strategies, enabling tailored performance enhancements or specific motion patterns. Validation conducted on a bespoke test bed confirms the model's efficacy in predicting spatial movements, albeit with minor deviations due to motor load variations and control system dynamics. In particular, the derived kinematic framework offers valuable insights for advancing control logic, particularly navigating in flat terrains, thereby broadening the RHex robot's application spectrum.

A novel methodology for vision-based path planning and obstacle avoidance in mobile robot applications

ABSTRACT The availability of overhead views of the environments around mobile robots always present useful information provided that these views are correctly perceived. In this paper, a novel approach for vision-based path planning and obstacle avoidance for mobile robots is presented. The approach uses visual perception integrated with a proposed field-based path-planning algorithm to overcome some common path-planning issues such as local minima, problematic destinations, and following long paths around obstacles. An exponential angle field is generated around each obstacle that deviates from the robot’s orientation as it directs towards the destination. Obstacle field is activated or deactivated based on a proposed collision prediction algorithm. To satisfy robot convergence to the goal point, the obstacle field parameters are determined using the Lyapunov stability criterion. The method also proposes a search algorithm for proper exit in the case of robot and/or goal point trapping. Both simulation and experiments are used in validating the algorithm. The results show the effectiveness of the proposed algorithm in converging towards target points, avoiding collision with stationary obstacles and overcoming famous path-planning problems.

Research on maintenance-free design and low friction characteristics of industrial robot sliding components

With the continuous progress of industrial automation, the application of industrial robots in various fields has become increasingly common, and ensuring their efficient and stable operation and reducing maintenance costs is crucial. This study used powder metallurgy technology, combined with multiple sintering-rolling processes and oil-immersed vacuum assistance, to successfully prepare Fe-Cu-Ni-Sn-graphite oil-immersed self-lubricating composite materials to meet this demand. In-depth microstructure and wear surface studies revealed that this porous oil-immersed self-lubricating composite material exhibits long-term low friction and high wear resistance and reduces the friction coefficient by 40% compared to untreated samples after a specific composite process. In addition, the material exhibits excellent friction performance in the high-temperature pin-on-disc friction and wear test machine. Even after continuous sliding for 24 hours, its friction coefficient remains low and stable. The study also found that its lubrication mechanism may be attributed to solid-liquid synergistic lubrication, thanks to the appearance of oil-graphite mixtures around the wear track. This design ensures high rigidity and reduced frictional loss, providing a strong reference for the design and optimization of sliding components of industrial robots, and is highly suitable for the widespread application of industrial robots.

Autonomous Navigation System for a Differential Drive Mobile Robot

Abstract Mobile robotics is a rapidly expanding field of research because of its large number of applications. Autonomous mobile robots (AMRs) are used in multiple areas, such as industrial automation, logistics and warehouse management, museums, hospitals and restaurant assistance, space and ocean exploration, and many others. In this work, a navigation system for a low-cost mobile robot is proposed. It relies on state-of-the-art software suited for designing automated systems, robot applications, and computer vision algorithms. The robot is driven by two wheels powered by Beckhoff motors, controlled using Beckhoff’s TwinCAT 3 automation software in an industrial computer. A separate small-sized Raspberry Pi computer handles the environment perception by processing the information acquired by a Lidar module and a webcam and the localization and planning through the Robot Operating System (ROS). Communication between the two computers yields a complete robot navigation system. This system is tested and subsequently tuned to achieve good performance. Experimental tests show that the navigation system is globally effective, and some limitations related to the robot’s design and its navigation subsystem are discussed. The approach proposed in this work can be extended, adjusted, and used in other types of mobile robots.

Real-time navigation of mecanum wheel-based mobile robot in a dynamic environment

Path planning and control of a mobile robot, in a dynamic environment, has been an important research topic for many years. In this paper an algorithm for autonomous motion of a mobile robot is proposed, with mecanum wheels, to reach a goal while avoiding obstacles through the shortest path in a dynamic environment. The proposed method uses a hybrid A⁎ and a velocity obstacle algorithms for path planning and obstacle avoidance. The A⁎ algorithm is implemented to explore the shortest path from starting position to the goal while avoiding all the static obstacles. However, in real time applications the dynamic obstacles need to be avoided, therefore, for such a case velocity obstacle algorithm is unified with the A⁎ algorithm. Initially, the proposed algorithm is verified through simulations. Then it is implemented using experimental setup in real time environment using single and multiple static obstacles as well as on a dynamic obstacle. It can be observed that the robot reaches the goal, effectively by avoiding static and dynamic obstacles. Moreover, the performance of the proposed work is evaluated through qualitative comparison between proposed method and recently published work, showing that the proposed algorithm is gives better features than existing work. In the end, the possible application of mobile robot having mecanum wheels with proposed path planning method is also given in the paper.

Research on DEM geomorphic factor terrain recognition algorithm using probabilistic neural networks based on tactile systems

The accurate recognition of unknown terrain by robots plays a vital role in completing walking tasks. Haptic-based terrain recognition methods have their own advantages over visual, audio, and vibration-based terrain recognition methods. With the increasing complexity of robot application scenarios, there are still many technical difficulties in using tactile systems to recognize terrain. For real-time terrain detection technology, how to improve the accuracy of terrain recognition and shorten recognition time based on tactile systems is an urgent problem to be solved. In this paper, the digital elevation model (DEM) geomorphic factor terrain recognition algorithm using probabilistic neural networks (DGFTRA-PNN) based on tactile force-feedback system is proposed to solve this problem. First, the tactile force-feedback system is mounted on the foot ends of the hexapod robot to collect terrain data. Second, the feasibility of using DEM geomorphic factors as terrain features to identify terrain was demonstrated. Third, DGFTRA-PNNs are established based on the different walking gait of the hexapod robot, achieving terrain recognition based on DEM feature values. Finally, the accuracy and speed of DGFTRA-PNN were verified through field experiments. Compared with the terrain recognition algorithm based on back propagation neural network (BP) and extreme learning machine (ELM) algorithm, the DGFTRA-PNN has advantages in terms of terrain recognition accuracy and recognition time: the average recognition accuracy for various types of terrain is 96%, and the calculation time is 0.04 seconds.

The Role of Artificial Intelligence on Emerging Technologies & Society

Abstract: Artificial Intelligence (AI) is one of the current emerging technologies. In the history of computing AI has been in the similar role earlier - almost every decade since the 1950s, when the programming language Lisp was invented and used to implement self modifying applications. The second time that AI was described as one of the frontier technologies was in the 1970s, when Expert Systems (ES)were developed Currently in the 2010s, AI is again on the frontier in the form of (self-)learning systems manifesting in robot applications, smart hubs, intelligent data analytic s, etc. Artificial intelligence (AI) is an important technology that supports daily social life and economic activities. It contributes greatly to the sustainable growth of Indian’s economy and solves various social problems. Artificial intelligence refers to the ability of a computer or a computer-enabled robotic system to process information and produce outcomes in a manner similar to the thought process of humans in learning, decision making and solving problems (Intelligence, 2017). By extension, the goal of AI systems is to develop systems capable of tacking complex problems in ways similar to human logic and reasoning.

Probing with Each Step: How a Walking Crab-like Robot Classifies Buried Cylinders in Sand with Hall-Effect Sensors.

Shallow underwater environments around the world are contaminated with unexploded ordnances (UXOs). Current state-of-the-art methods for UXO detection and localization use remote sensing systems. Furthermore, human divers are often tasked with confirming UXO existence and retrieval which poses health and safety hazards. In this paper, we describe the application of a crab robot with leg-embedded Hall effect-based sensors to detect and distinguish between UXOs and non-magnetic objects partially buried in sand. The sensors consist of Hall-effect magnetometers and permanent magnets embedded in load bearing compliant segments. The magnetometers are sensitive to magnetic objects in close proximity to the legs and their movement relative to embedded magnets, allowing for both proximity and force-related feedback in dynamically obtained measurements. A dataset of three-axis measurements is collected as the robot steps near and over different UXOs and UXO-like objects, and a convolutional neural network is trained on time domain inputs and evaluated by 5-fold cross validation. Additionally, we propose a novel method for interpreting the importance of measurements in the time domain for the trained classifier. The results demonstrate the potential for accurate and efficient UXO and non-UXO discrimination in the field.

The role of robots in logistics

PurposeThe purpose of this paper is to illustrate the growing role of robots in the logistics industry.Design/methodology/approachFollowing an introduction, which identifies key challenges facing the industry, this paper discusses robotic applications in warehouses, followed by sections covering transportation and delivery and conclusions.FindingsThe logistics industry faces a number of challenges that drive technological and operational changes. Robots are already playing a role within the warehouse sector and more complex applications have recently arisen from developments in artificial intelligence-enabled vision technology. In the transportation sector, autonomous trucks are being developed and trialled by leading manufacturers. Many major logistics companies are involved and limited services are underway. Last-mile delivery applications are growing rapidly, and trials, pilot schemes and commercial services are underway in Europe, the USA and the Far East. The Chinese market is particularly buoyant, and in 2019, a delivery robot was launched that operates on public roads, based on Level-4 autonomous driving technology. The drone delivery sector has been slower to develop, in part due to regulatory constraints, but services are now being operated by drone manufacturers, retailers and logistics providers.Originality/valueThis paper provides details of existing and future applications of robots in the logistics industry.

Development of Method for the Measurement of Dermal Exposure on a Lifelike Cow Dummy to Assess the Risk of Using a Urease Inhibitor (Atmowell®) to Reduce NH3 Emissions in Cattle Farming

Up to 58% of NH3 emissions can be reduced through the use of the urease inhibitor Atmowell® in cattle farming. Automated manure scraper and drag hose systems should be used to apply urease inhibitors (UIs) to stable floors. Proof of safe use is also needed. A risk assessment of the urease inhibitor PPDA was conducted utilising estimated and calculated exposure data along with established and verified model calculation tools. Risk assessment models can be improved using measured dermal exposure data. The fluorescent dye pyranine and a Tyvek® collector were used to measure exposure. Tyvek® was attached to a lifelike cow dummy. Regarding the drag hose system, two worst-case scenarios were considered: over the cow and passing the cow. Regarding the robot application system, a 0 m frontal scenario was considered. The over the cow scenario exhibited the highest dermal exposure, with 1.5 mg of PPDA/cow per application run. The robot scenario exhibited the smallest amount, with 0.8 mg of PPDA/cow per application run. The risk of using PPDA was already excluded by model calculation tools in a risk assessment. Compared with the estimated and calculated dermal exposure values, the measured dermal exposure levels were reduced by a factor of two to six.

Biomimetic Structure and Surface for Grasping Tasks.

Under water, on land, or in the air, creatures use a variety of grasping methods to hunt, avoid predators, or carry food. Numerous studies have been conducted to construct a bionic surface for grasping tasks. This paper reviews the typical biomimetic structures and surfaces (wedge-shaped surface, suction cup surface and thorn claw surface) for grasping scenarios. Initially, progress in gecko-inspired wedge-shaped adhesive surfaces is reviewed, encompassing the underlying mechanisms that involve tuning the contact area and peeling behavior. The applications of grippers utilizing this adhesive technology are also discussed. Subsequently, the suction force mechanisms and applications of surfaces inspired by octopus and remora suction cups are outlined. Moreover, this paper introduces the applications of robots incorporating the principles of beetle-inspired and bird-inspired thorn claw structures. Lastly, inspired by remoras' adhesive discs, a composite biomimetic adhesive surface is proposed. It integrates features from wedge-shaped, suction cup, and claw thorn surfaces, potentially surpassing the adaptability of basic bioinspired surfaces. This surface construction method offers a potential avenue to enhance adhesion capabilities with superior adaptability to surface roughness and curvature.

Therapeutic robots for post-stroke rehabilitation.

Stroke is a prevalent, severe, and disabling health-care issue on a global scale, inevitably leading to motor and cognitive deficits. It has become one of the most significant challenges in China, resulting in substantial social and economic burdens. In addition to the medication and surgical interventions during the acute phase, rehabilitation treatment plays a crucial role in stroke care. Robotic technology takes distinct advantages over traditional physical therapy, occupational therapy, and speech therapy, and is increasingly gaining popularity in post-stroke rehabilitation. The use of rehabilitation robots not only alleviates the workload of healthcare professionals but also enhances the prognosis for specific stroke patients. This review presents a concise overview of the application of therapeutic robots in post-stroke rehabilitation, with particular emphasis on the recovery of motor and cognitive function.

Flexible Grippers with Programmable Three‐Dimensional Magnetization and Motions

Programmable magnetic soft grippers are highly desirable for diverse applications in drug delivery, object manipulation, and soft robotics. However, current magnetic programming grippers need to be driven by multiple physical fields, which are complicated to operate and single in motion. Here, a method for patterning hard magnetic microparticles in an elastomer matrix is reported. Based on digital light processing (DLP), this method uses controlled reorientation of magnetic particles and selective exposure to ultraviolet (UV) light to encode magnetic particles in polydimethylsiloxane (PDMS) materials with arbitrary three‐dimensional (3D) orientation. The combination of vertical and horizontal magnetic fields can produce different forces, causing different deformation modes of the grippers. Flexible grippers can be fabricated from a single precursor in one process and produce various deformation and motion forms when a single magnetic field is applied. Moreover, the gripper has the advantages of simple manipulation, fast response, and flexible movement, which is of great significance in the application of biological devices and soft robots.