Intelligent Robotic Systems Research Articles

A Remotely Controlled Transformable Soft Robot Based on Engineered Cardiac Tissue Construct.

Many living organisms undergo conspicuous or abrupt changes in body structure, which is often accompanied by a behavioral change. Inspired by the natural metamorphosis, robotic systems can be designed as reconfigurable to be multifunctional. Here, a tissue-engineered transformable robot is developed, which can be remotely controlled to assume different mechanical structures for switching locomotive function. The soft robot is actuated by a muscular tail fin that emulates the swimming of whales and works as a cellular engine powered by the synchronized contraction of striated cardiac microtissue constructs. For a transition of locomotive behavior, the robot can be optically triggered to transform from a spread to a retracted form, which effectively changes the bending stiffness of the tail fins, thus minimizing the propulsion output from the "tail fin" and effectively switching off the engine. With the unprecedented controllability and responsiveness, the transformable robot is implemented to work as a cargo carrier for programmed delivery of chemotherapeutic agents to selectively eradicate cancer cells. It is believed that the realization of the transformable concept paves a pathway for potential development of intelligent biohybrid robotic systems.

Dual-station intelligent welding robot system based on CCD

Welding is a manufacturing technique that joins metal or other thermoplastic materials in a heated, high-temperature, or high-pressure manner. Harmful dust and the light from welding can cause great harm to the human body. In addition, to improve welding quality and efficiency, intelligent welding has become an urgent need in the manufacturing industry. Herein, a dual-station intelligent welding strategy is designed based on an industrial charge-coupled device (CCD) visual detection system and welding control system. This solves the problem of expensive laser vision sensors and the poor detection effect of the visual system under the conditions of a short distance and strong light intensity. In the actual environment, there is the problem of radial distortion that affects the edge of the image, and the problem of the optical axis of the camera not being consistent with the installation plane. In this study, the camera coordinate system and hand–eye coordinate system are calibrated separately. The abovementioned problems are solved, and the images obtained by the CCD are acquired and processed in real-time. Image segmentation was performed using neighbourhood average filtering, iterative threshold segmentation, and local information extraction. The edge amplitude image was obtained by the Prewitt operator. Under the Hough transform method, the recognition of the weld seam and the extraction of weld feature points are realised. We designed an intelligent weld detection system that contains a friendly human–computer interface. Through numerous repeated experiments on circular and square workpieces, the control error of this intelligent welding system is within 0.2 mm, and the time of single seam feature extraction is 0.8 s.

Design and Optimization for a New Compliant Planar Spring of Upper Limb Assistive Device Using Hybrid Approach of RSM–FEM and MOGA

This study develops a new multi-objective optimization design for a compliant planar spring that is used for upper limb assistive device for disabled people. The structure of spring is designed by arranging the leaf springs in a zigzag shape to permit a large deformation. The spring is a key element of the gravity-balanced mechanism (GBM) in the upper limb assistive device, exoskeleton and upper limb assistive robot. Static equilibration of the GBM is utilized to eliminate the working effort of the disable forearm. The spring is therefore used to adjust the stiffness and to maintain the equilibrium condition. To improve static performances, an effective hybrid approach of the response surface method, finite element method, and multi-objective genetic algorithm is developed. In comparison with common types of surrogates, the Kriging metamodel was found as a prefer black-box to approximate the objective functions. Subsequently, the multi-objective genetic algorithm is adopted to find out the Pareto-optimal set for the spring. The sensitivity of each design variables is then analyzed, and their significant contributions are conducted using the analysis of variance. The predicted results give a good agreement with the validation ones. It eliminates the modeling errors of the analytical method for synthesis and analysis of compliant mechanisms. The results facilitate an optimization design for the intelligent robotics systems for the assistive devices.

ROS-Based Human Detection and Tracking from a Wireless Controlled Mobile Robot Using Kinect

Human detection and tracking is an important task in artificial intelligent robotic systems, which usually require a robust target detector to work in a variety of circumstances. In complex environments where a camera is installed on a wheeled mobile robot, this task becomes much more difficult. In this paper, we present a real-time remote-control system for human detection, tracking, security, and verification in such challenging environments. Such a system is useful for security monitoring, data collection, and experimental purpose. In the proposed system, a Kinect RGB-D camera from Microsoft is used as a visual sensing device for the design of human detection and tracking. We also implemented a remote-control system on a four-wheel mobile platform with a Robot Operating System (ROS). By combining these two designs, a wireless controlled mobile platform having the feature of real-time human monitoring can be realized to handle this task efficiently. Experimental results validate the performance of the proposed system for wirelessly controlling the mobile robot to track humans in a real-world environment.

Activation and Spreading Sequence for Spreading Activation Policy Selection Method in Transfer Reinforcement Learning

This paper proposes an automatic policy selection method using spreading activation theory based on psychological theory for transfer learning in reinforcement learning. Intel-ligent robot systems have recently been studied for practical applications such as home robot, communication robot, and warehouse robot. Learning algorithms are key to building useful robot systems important. For example, a robot can explore for optimal policy with trial and error using reinforcement learning. Moreover, transfer learning enables reuse of prior policy and is effective for environment adaptability. However, humans de-termine applicable methods in transfer learning. Policy selection method has been proposed for transfer learning in reinforcement learning using spreading activation model proposed in cognitive psychology. In this paper, novel activation function and spreading sequence is discussed for spreading policy selection method. Fur-ther computer simulations are used to examine the effectiveness of the proposed method for automatic policy selection in simplified shortest-path problem.

Practice of Social Implementation Robot Education through Development of Micro Intelligent Robot System

Practice of Social Implementation Robot Education through Development of Micro Intelligent Robot System

Numerical Study of Some Intelligent Robot Systems Governed by the Fractional Differential Equations

We provide a high order numerical methods for solving some intelligent Robot Systems, which are governed by the fractional nonlinear variable coefficient reaction diffusion equations with delay. The convergence of this numerical algorithm is discussed via the energy method. Besides, this algorithm can also be extended to solve the reaction diffusion equation with multi-delays. Finally, we carry on the numerical analysis, and the results of the numerical tests are coincide with the theoretical results.

Modal Decoupled Dynamics Feed‐Forward Active Force Control of Spatial Multi‐DOF Parallel Robotic Manipulator

According to the parallel mechanism theory, this paper proposes a novel intelligent robotic spine brace for the treatment of scoliosis. Nevertheless, this type of parallel mechanism has the following disadvantages: strong dynamic coupling in task space or joint space, adverse effect of system’s gravity, and lower response frequency in roll and pitch orientations, which seriously affect the performance of the system. In order to solve those boring problems, this paper presents a novel active force control structure, modal space dynamic feed‐forward (MSDF) force control strategy. Besides, this paper expresses the intelligent robotic brace system model including the dynamic and kinematic models and the electric actuator model with Kane strategy. The stability of the intelligent system with the novel control strategy is proved. In order to evaluate the performance of the presented MSDF force control method, this paper builds the parallel mechanism experimental platform. It can be seen from experimental results that the proposed motion control method solves these boring problems well.

Image processing-based intelligent robotic system for assistance of agricultural crops

Agriculture has been practiced in conventional ways for centuries and supported with mechanical systems in the last few decades. With the evolution of robotic equipment and sensors, the researchers are focusing on introducing smart farming. In this paper, we propose improved algorithms for infection detection in leaves and field classification targeting a heterogeneous robotic system. Image processing methods have been used on the leaves of the plants to calculate the infection percentage in crops and elementary machine learning algorithm k-means clustering for classifying the field. Classification of the agricultural field has been done for growing different types of crops in a mixed cropping technique which has an advantage over other farming procedures. Early detection of diseases can help in better preventive measures in the early stages. We have used 3,150 images of crop diseases for three different types of crops and by smartly incorporating some previously established techniques. The primary objective of this paper includes the qualitative analysis of infection detection algorithms and further elaboration for the possible application of the suggested work in smart farming.

Image processing-based intelligent robotic system for assistance of agricultural crops

Agriculture has been practiced in conventional ways for centuries and supported with mechanical systems in the last few decades. With the evolution of robotic equipment and sensors, the researchers are focusing on introducing smart farming. In this paper, we propose improved algorithms for infection detection in leaves and field classification targeting a heterogeneous robotic system. Image processing methods have been used on the leaves of the plants to calculate the infection percentage in crops and elementary machine learning algorithm k-means clustering for classifying the field. Classification of the agricultural field has been done for growing different types of crops in a mixed cropping technique which has an advantage over other farming procedures. Early detection of diseases can help in better preventive measures in the early stages. We have used 3,150 images of crop diseases for three different types of crops and by smartly incorporating some previously established techniques. The primary objective of this paper includes the qualitative analysis of infection detection algorithms and further elaboration for the possible application of the suggested work in smart farming.

Reliability analysis of martial arts arena robot systems based on fuzzy set theory

The reliability of the martial arts robot, which is a popular object of the intelligent robot competitions, is studied in this paper. Due to various uncertainties, the precise models of failure probabilities can’t be established. The fuzzy set theory is adopted to enable dynamic quantitative analysis of the system reliability, and the triangular fuzzy numbers are used to represent the failure possibility of basic events. To obtain the fuzzy failure probability importance of basic events, the fault mechanism of the martial arts arena system is studied. The intelligent robot system is evaluated by qualitative analysis and quantitative calculation. The results of this paper can provide theoretical analysis of the fault diagnosis and useful suggestion for maintenance of the martial arts robot.

EditorialMechatronics as challenge for higher competitiveness

Michal Kelemen, prof. Ing. PhD. (born in 1974) is a professor of the Department of Mechatronics at the Faculty of Mechanical Engineering at the Technical University of Kosice, Slovakia. He received M.S. degree in mechanical engineering from Technical University of Kosice, Slovakia in 1998 and Ph.D. degree in Mechatronics from Technical University of Kosice, Slovakia in 2002. He has been awarded in the 1998 “Price of the VolksBank” for the best M.S. graduate and 2007 Price “Scientist of the year”. His research interests include mechatronic systems, intelligent robotic systems, measurement of non-electric quantities, and microcomputer systems. He has authored more than 200 journal and conference papers on these topics.

Human Performance in Interpreting Robot-Generated 2d and 3d Maps

Robust mapping capabilities are a critical technology for intelligent robotic systems. They can (1) provide valuable information to human and robot teammates without requiring prior knowledge or experience and (2) enable other, higher-level behaviors, such as autonomous navigation and exploration. To maximize interpretability, a map must be coherent, accurate, and displayed in an intuitive fashion. However, maps inherently require a large amount of computational resources. Therefore, it is beneficial to determine the minimum amount of information that must be provided to a user to meet the specific mission requirements. The purpose of this study is to evaluate human performance on visual tasks using 2D and 3D maps generated from laser point cloud data. In a within-subjects study, 20 participants were tasked with locating and identifying objects, doorways, and windows in a two-story building. The characterizations made herein could ultimately influence how map data from robotic assets are shared and displayed.

Mind Control of a Robotic Arm With Visual Fusion Technology

This paper reports the development of an intelligent shared control system for a robotic manipulator that is commanded by the user's mind. The target objects are detected by a vision system and then displayed to the user in a video that shows them fused with flicking diamonds that are designed to excite electroencephalograph (EEG) signals at different frequency bands. Through the analysis of the invoked EEG signals, a brain–computer interface is developed to infer the exact object that is required by the user. These results are then transferred to the shared control system, which is enabled by visual servoing techniques to achieve accurate object manipulation. The task motion and self-motion (CTS) methods are coordinated to enhance the intelligence of the shared control system by equipping the robot with an autonomous obstacle avoidance function. Extensive experimental studies are performed to verify that the adaptive object tracking algorithm, the CTS method, and the least-squares method are helpful in improving the performance of the intelligent robotic system.

Artificial Intelligence, Automation, and Social Welfare: Some Ethical and Historical Perspectives on Technological Overstatement and Hyperbole

ABSTRACTThe potential societal impacts of automation using intelligent control and communications technologies have emerged as topics in recent writings and public policy initiatives. Constructed entities labelled as ‘thinking machines’ (such as IBM’s Watson as well as intelligent chatbot and robotic systems) have also played significant roles in this discourse. This paper provides an historical sequencing then analyses a selection of writings produced since the 1940s concerning economic and social issues involving artificial intelligence (AI) research and applications. The paper explores how overstatements and hyperbolic themes and concepts, often stemming from AI’s early periods (including from Herbert Simon), are being employed in characterisations of current AI approaches in apparently opportunistic attempts to provide rhetorical support for various large-scale business and societal initiatives. It also addresses the relative neglect of consideration of many of AI’s sociotechnical failures and discontinued approaches in recent examinations of automation and social welfare issues. The paper discusses the moral logic of AI researchers and developers providing reasonable and measured narratives in public discourse rather than hyperbole, efforts that can empower decision makers to make sounder judgments concerning the technology’s current and future applications as well as allocate rewards of the technology more equitably.

Review of grain threshing theory and technology

Threshing is the most important function of grain harvester. Grain loss and damage in harvesting are significantly related to threshing theory and technology. There are four kinds of threshing principles including impact, rubbing, combing and grinding. Four types of contact models between grain and threshing components have been constructed correspondently. Grain damage can be regarded as a function of peripheral velocity and contact pattern of impacting. Grain loss can be regarded as a function of contact pattern of rasp bars. Grain loss coming from cleaning and separation in the subsequent process of combing threshing was significantly decreased. Tangential and axial threshing technologies have been applied in grain threshing system widely. It showed that in the combined application, tangential rolls are used to accelerate grain flow, and axial rolls are used to increase threshing quality especially lower loss and damage. Conical concave may take the place of the traditional cylindrical one. With the development of sensor technology and communication technology, intelligent harvesting robot and automatic threshing system will be integrated together to improve grain quality and operation comfort. Keywords: grain, threshing, threshing theory, threshing technology, harvester, grain loss DOI: 10.25165/j.ijabe.20181103.3432 Citation: Fu J, Chen Z, Han L J, Ren L Q. Review of grain threshing theory and technology. Int J Agric & Biol Eng, 2018; 11(3): 12–20.

TTEthernet Transmission in Software‐Defined Distributed Robot Intelligent Control System

With the integration of new technologies such as smart technologies and cloud computing in the industrial Internet of Things, the complexity of industrial IoT applications is increasing. Real‐time performance and determinism are becoming serious challenges for system implementation in these Internet of Things systems, especially in critical security areas. This paper provides a framework for a software‐defined bus‐based intelligent robot system and designs scheduling algorithms to make TTEthernet play the role of scheduling in the framework. Through the framework, the non‐real‐time and uncertainties problem of distributed robotic systems can be solved. Moreover, a fragment strategy was proposed to solve the problem of large delay caused by Rate‐Constrained traffic. Experimental results indicate that the improved scheme based on fragmentation strategy proposed in this paper can improve the real‐time performance of RC traffic to a certain extent. Besides, this paper made a performance test and comparison experiments of the improved scheme in the simulation software to verify the feasibility of the improved scheme. The result showed that the delay of Rate‐Constrained traffic was reduced and the utilization rate of network was improved.

Bio-plausible simulation of three monoamine systems to replicate emotional phenomena in a machine

In this paper we present the validation of the three-dimensional model of emotions by Hugo Lövheim the “cube of emotion” via neurosimulation in the NEST. We also present the extension of original “cube of emotion” with the bridge to computational processes parameters. The neurosimulation is done via re-implementation of dopamine (DA), serotonin (5-HT) and noradrenaline (NA) subsystems of a rat brain to replicate 8 basic psycho-emotional states according to the “cube of emotion”. Results of neu-rosimulations indicate the incremental influence of DA and NA over computational resources of a psycho-emotional state while 5-HT decreases the computational resources used to calculate a psycho-emotional state. This way we indicate the feasibility of the bio-plausible re-implementation of psycho-emotional states in a computational system. This approach could be useful extension of decision making and load balancing components of modern artificial agents as well as intelligent robotic systems.

CERNTAURO: A Modular Architecture for Robotic Inspection and Telemanipulation in Harsh and Semi-Structured Environments

Intelligent robotic systems are becoming essential for industries, nuclear plants, and for harsh environments in general, such as the European Organization for Nuclear Research (CERN) particles accelerator complex and experiments. In order to increase safety and machine availability, robots can perform repetitive, unplanned, and dangerous tasks, which humans either prefer to avoid or are unable to carry out due to hazards, size constraints, or the extreme environments in which they take place. A novel robotic framework for autonomous inspections and supervised teleoperations in harsh environments is presented. The proposed framework covers all aspects of a robotic intervention, from the specification and operator training, the choice of the robot and its material in accordance with possible radiological contamination risks, to the realization of the intervention, including procedures and recovery scenarios. The robotic solution proposed in this paper is able to navigate autonomously, inspecting unknown environments in a safe way. A new real-time control system was implemented in order to guarantee a fast response to environmental changes and adaptation to different type of scenarios the robot may find in a semi-structured and hazardous environment. Components of the presented framework are: a novel bilateral master-slave control, a new robotic platform named CERNbot, and an advanced user-friendly multimodal human-robot interface, also used for the operators’ offline training, allowing technicians not expert in robot operation to perform inspection/maintenance tasks. The proposed system has been tested and validated with real robotic interventions in the CERN hazardous particle accelerator complex.

Cognition-Based Context-Aware Cloud Computing for Intelligent Robotic Systems in Mobile Education

At present, artificial intelligence (AI) has made considerable progress in recognition of speech, face, and emotion. Potential application to robots could bring significant improvement on intelligent robotic systems. However, limited resource on robots cannot satisfy the large-scale computation and storage that the AI recognition requires. Cloud provides an efficient way for robots, where they off-load the computation too. Therefore, we present a cognition-based context-aware cloud computing framework, which is designed to help robot’s sense environments including user’s emotions. Based on the recognized context information, robots could optimize their responses and improve the user’s experience on interaction. The framework contains a customizable context monitoring system on the mobile end to collect and process the data from the robot’s sensors. Besides, it integrates various AI recognition services in the cloud to extract the context facts by analyzing and understanding the data. Once the context data is extracted, the results are pushed back to mobile end for making a better decision in the next interactions. In this paper, we demonstrate and evaluate the framework by a real case, an educational mobile app for English learning. The results show that the proposed framework could significantly improve the interaction and intelligence of mobile robots.