Robot Control System Research Articles

Entertainment robots based on swarm intelligence algorithm applied in remote dance performances

With the continuous development of technology, the application of entertainment robots in the field of dance performances is receiving increasing attention. This article focuses on the application of entertainment robots based on swarm intelligence algorithms in remote dance performances, aiming to design an intelligent dance performance control system to achieve precise and smooth dance performances of robots. The study introduces the basic artificial fish swarm algorithm and combines it with mobile robot path planning, proposing a mobile robot path planning method based on swarm intelligence algorithm. By simulating the behavior of fish in schools, the optimization of path planning is achieved. The control system process of the entertainment robot intelligent dance performance control system includes preparation work before the dance performance, data transmission and dance posture control during the performance process, and post performance organization work. The experiment verified the effectiveness and reliability of the intelligent dance performance control system for entertainment robots. The experimental results show that the system can achieve precise, smooth, and diverse dance performances, providing a novel and interesting form of dance performance for the audience.

Adaptive fixed-time fuzzy fault-tolerant control for robotic manipulator with unknown friction and composite actuator faults

This paper exploits the issue of fixed-time (FT) tracking control (TC) for uncertain robotic manipulator systems (RMS) with composite actuator faults (CAFs), unknown disturbances and joint friction. The fuzzy logic system (FLS) algorithm is introduced to fit the unknown uncertainties including unmodeled parameters, CAFs and joint friction of the RMS, after which a relevant updating algorithm is employed to generate the unidentified boundary of the plant disturbance and FLS approximation error. Then a novel adaptive fuzzy sliding mode (SM) fault-tolerant controller is synthesized, which not only guarantees the boundedness of estimation errors of the involved adaptive parameters, but also actualizes the reachability of the predevised sliding surface. Furthermore, the proof that the system trajectories can achieve the FT convergence to the expected position signals, is then provided on account of the FT stability theory. Ultimately, simulation experiments exhibit the rationality based upon the current control algorithm.

Design and Implementation of Intelligent Robot Control System Integrating Computer Vision and Mechanical Engineering

With the rapid development of science and technology and industry, robot technology has become a key pillar in many fields, especially in industrial manufacturing, robots have been widely used in automated operations. However, in the face of the complex and changeable demands of modern industry, simple mechanized operation can no longer be satisfied, and intelligence has become the inevitable trend of robot technology development. By combining computer vision technology, this study provides robots with powerful environmental awareness and target recognition capabilities, thus achieving higher-level autonomous operation and decision-making. Aiming at the intelligent sorting system, this research adopts XYZ mechanical arm as the execution platform, equipped with TCS34725 color sensor to identify the color of articles, and integrates Hikvision industrial camera and NVIDIA Jetson TX2 image processor to capture and process image data, so as to achieve accurate positioning of articles. In the aspect of software design, this research realizes the function of accurately sorting different color items from the conveyor belt by combining color recognition algorithm and image processing technology with mechanical engineering control. The test results of the system show that the system has high accuracy and stability in color recognition and object positioning, and the trajectory accuracy of the manipulator meets the requirements, which verifies the realization of the research goal. The intelligent robot control system designed in this paper not only improves the level of industrial automation and reduces labor costs, but also broadens the application fields of robots in complex environments.

Adaptive fuzzy neural network-based finite time prescribed performance control for uncertain robotic systems with actuator saturation

Adaptive fuzzy neural network-based finite time prescribed performance control for uncertain robotic systems with actuator saturation

Development of an Artificial Vision for a Parallel Manipulator Using Machine-to-Machine Technologies.

This research focuses on developing an artificial vision system for a flexible delta robot manipulator and integrating it with machine-to-machine (M2M) communication to optimize real-time device interaction. This integration aims to increase the speed of the robotic system and improve its overall performance. The proposed combination of an artificial vision system with M2M communication can detect and recognize targets with high accuracy in real time within the limited space considered for positioning, further localization, and carrying out manufacturing processes such as assembly or sorting of parts. In this study, RGB images are used as input data for the MASK-R-CNN algorithm, and the results are processed according to the features of the delta robot arm prototype. The data obtained from MASK-R-CNN are adapted for use in the delta robot control system, considering its unique characteristics and positioning requirements. M2M technology enables the robot arm to react quickly to changes, such as moving objects or changes in their position, which is crucial for sorting and packing tasks. The system was tested under near real-world conditions to evaluate its performance and reliability.

Overview of Gesture Control Technologies

The dynamic development and rapid updating of information and communication technologies (ICT) has created a suitable environment for gesture control of modern computer and robot systems. It is noted that gestures are a necessary component in the communication process. At the same time, it is possible to control the equipment without contact with the support of methods that allow to select and recognize hand gestures. Sufficient work has been done and achievements have been made in the direction of automation and gesture control of technology. The study notes that such systems are more flexible and can easily adapt to people's changing needs. Building a human-machine interface speeds up the communication process and expands the user's capabilities. Gesture control technologies for computer and robotic systems have great potential. A comparative analysis of the parameters of gesture-controlled technologies is shown in the table.

Research on the operation and quality control of small rock hole shotcrete robot

A small rock shotcrete robot is applied in the deep tunnel dynamic disaster physical simulation system in order to simulate the final operation process of the tunnel project. The robot’s spraying operation and control include many kind of factors. In this paper, the main parameters affecting the spraying operation are analyzed by establishing the spraying model and coating growth model. For matching reasonably the spraying parameters, the matching equation of spraying quality and spraying gun parameter model and spraying feed speed has been derived. Based on the matching equation, the spraying gun structure and robot control system are designed, and the spraying distance and spraying gun movement velocity including horizontal and vertical driving have been controlled better. On the basis of theoretical analysis, a small rock hole shotcrete robot prototype has been developed and the spraying experiments have been finished. The experimental results showed that the model and equation established can guided the spraying quality control. With the increase of the injection elevation angle, the length of the long and short axes of the elliptic coating becomes larger, and the average thickness of the coating center is between 2.23 and 2.34 mm, which meets the design index of the injection robot. The rock hole shotcrete robot meet the design requirements.

Robotic platforms in gynaecological surgery: past, present, and future.

More than two decades ago, the advent of robotic laparoscopic surgery marked a significant milestone, featuring the introduction of the AESOP robotic endoscope control system and the ZEUS robotic surgery system. The latter, equipped with distinct arms for the laparoscope and surgical instruments, was designed to accommodate remote connections, enabling the practice of remote telesurgery as early as 2001. Subsequent technological progress has given rise to a range of options in today's market, encompassing multi-port and single-port systems, both rigid and flexible, across various price points, with further growth anticipated. This article serves as an indispensable guide for gynaecological surgeons with an interest in embracing robotic surgery. Drawing insights from the experience of the Strasbourg training centre for minimally invasive surgery (IRCAD), this article offers a comprehensive overview of existing robotic platforms in the market, as well as those in development. Robotic surgical systems not only streamline established operative methods but also broaden the scope of procedures, including intra- and transluminal surgeries. As integral components of the digital surgery ecosystem, these robotic systems actively contribute to the increasing integration and adoption of advanced technologies, such as artificial intelligence-based data analysis and support systems. Robotic surgery is increasingly being adopted in clinical practice. With the growing number of systems available on the marketplace, the primary challenge lies in identifying the optimal platform for each specific procedure and patient. The seamless integration of robotic systems with artificial intelligence, image-guided surgery, and telesurgery presents undeniable advantages, enhancing the precision and effectiveness of surgical interventions. This article provides a guide to the robotic platforms available on the market and those in development for gynaecologists interested in robotic surgery.

SunbYte: an autonomous pointing framework for low-cost robotic solar telescopes on high altitude balloons

The design and usability of a fully autonomous robotic control system (SunbYte - Sheffield University Balloon “lYfted” TElescope) for solar tracking and observational applications onboard high-altitude balloons are addressed here. The design is based on a six-step development plan balancing scientific objectives and practical engineering requirements. The high-altitude solar observational system includes low-cost components such as a Cassegrain-type telescope, stepper motors, harmonic drives, USB cameras and microprocessors. OpenCV installed from ROS (Robotic Operating System), python and C facilitated the collection, compression, and processing of housekeeping and scientific data. This processed data was then transmitted to the ground station through the launch vehicle’s telecommunication link. The SunbYte system allows the brightest spot in the sky, the sun, to be identified, and a telescope pointed towards it with high enough accuracy that a scientific camera can capture images. This paper gathers and presents the results from primarily two missions with the High-Altitude Student Platform (HASP, NASA Balloon Program office and LaSpace). Additionally, a discussion will be made comparing these with an earlier iteration flown with the German-Swedish “REXUS/BEXUS” programme coordinated by the European Space Agency. By capturing and analysing a series of tracking images with the location of the Sun at the calibrated centre, the system demonstrated the tracking capabilities on an unstable balloon during ascent. Housekeeping sensor data was collected to further analyse the thermal and mechanical performance. The low temperature increased friction in the drive train and reduced the responsiveness of the harmonic drive actuation system. This caused some issues which require further work in future missions, for example, with SunbYte 4 and its work when flying with the HEMERA ZPB (Zero Pressure Balloon) program.

Autonomous Alignment and Docking Control for a Self-Reconfigurable Modular Mobile Robotic System

This paper presents the path planning and motion control of a self-reconfigurable mobile robot system, focusing on module-to-module autonomous docking and alignment tasks. STORM, which stands for Self-configurable and Transformable Omni-Directional Robotic Modules, features a unique mode-switching ability and novel docking mechanism design. This enables the modules that make up STORM to dock with each other and form a variety configurations in or to perform a large array of tasks. The path planning and motion control presented here consists of two parallel schemes. A Lyapunov function-based precision controller is proposed to align the target docking mechanisms in a small range of the target position. Then, an optimization-based path planning algorithm is proposed to help find the fastest path and determine when to switch its locomotion mode in a much larger range. Both numerical simulations and real-world experiments were carried out to validate these proposed controllers.

A Framework of Grasp Detection and Operation for Quadruped Robot with a Manipulator

Quadruped robots equipped with manipulators need fast and precise grasping and detection algorithms for the transportation of disaster relief supplies. To address this, we developed a framework for these robots, comprising a Grasp Detection Controller (GDC), a Joint Trajectory Planner (JTP), a Leg Joint Controller (LJC), and a Manipulator Joint Controller (MJC). In the GDC, we proposed a lightweight grasp detection CNN based on DenseBlock called DES-LGCNN, which reduced algorithm complexity while maintaining accuracy by incorporating UP and DOWN modules with DenseBlock. For JTP, we optimized the model based on quadruped robot kinematics to enhance wrist camera visibility in dynamic environments. We integrated the network and model into our homemade robot control system and verified our framework through multiple experiments. First, we evaluated the accuracy of the grasp detection algorithm using the Cornell and Jacquard datasets. On the Jacquard dataset, we achieved a detection accuracy of 92.49% for grasp points within 6 ms. Second, we verified its visibility through simulation. Finally, we conducted dynamic scene experiments which consisted of a dynamic target scenario (DTS), a dynamic base scenario (DBS), and a dynamic target and base scenario (DTBS) using an SDU-150 physical robot. In all three scenarios, the object was successfully grasped. The results demonstrate the effectiveness of our framework in managing dynamic environments throughout task execution.

Development of a humanoid robot control system based on AR-BCI and SLAM navigation

Development of a humanoid robot control system based on AR-BCI and SLAM navigation

Adaptive neural network iterative learning control of long-stroke hybrid robots with initial errors and full state constraints

Research on initial errors and constraint restrictions is one of the main research directions in the field of control of uncertain robotic systems. An adaptive iterative learning control (AILC) method based on radial basis function (RBF) neural network is proposed to address the trajectory tracking problem of the long-stroke hybrid robot system with random initial errors and full state constraints. The RBF neural network is used to approximate the unknown nonlinear terms, and the network weights are updated using an iterative learning law that incorporates a projection mechanism. Additionally, a robust learning strategy is used to compensate for both the approximation error of the neural network and the external disturbances that vary with each iteration. To relax the requirement of traditional iterative learning control (ILC) for identical initial condition, an equivalent error function is constructed based on the time-varying boundary layer. The tangent-type barrier Lyapunov function (BLF) is designed to ensure that the joint position and speed of the robot system are bounded within a predetermined range. Through stability analysis based on barrier composite energy function (BCEF), it can be proved that the boundedness of all signals in the closed-loop system and the tracking error of the robot system will converge to an adjustable residual set asymptotically. Finally, through simulation experiments conducted on the MATLAB platform, the results demonstrate that the method overcomes the random initial errors of the system effectively, ensures that the system satisfies the full-state constraints, and realizes high-precision trajectory tracking.

Modeling of Cooperative Robotic Systems and Predictive Control Applied to Biped Robots and UAV-UGV Docking with Task Prioritization.

This paper studies a cooperative modeling framework to reduce the complexity in deriving the governing dynamical equations of complex systems composed of multiple bodies such as biped robots and unmanned aerial and ground vehicles. The approach also allows for an optimization-based trajectory generation for the complex system. This work also studies a fast-slow model predictive control strategy with task prioritization to perform docking maneuvers on cooperative systems. The method allows agents and a single agent to perform a docking maneuver. In addition, agents give different priorities to a specific subset of shared states. In this way, overall degrees of freedom to achieve the docking task are distributed among various subsets of the task space. The fast-slow model predictive control strategy uses non-linear and linear model predictive control formulations such that docking is handled as a non-linear problem until agents are close enough, where direct transcription is calculated using the Euler discretization method. During this phase, the trajectory generated is tracked with a linear model predictive controller and addresses the close proximity motion to complete docking. The trajectory generation and modeling is demonstrated on a biped robot, and the proposed MPC framework is illustrated in a case study, where a quadcopter docks on a non-holonomic rover using a leader-follower topology.

Condition Monitoring of a Cartesian Robot with a Mechanically Damaged Gear to Create a Fuzzy Logic Control and Diagnosis Algorithm

The detection of faults during an operational process constitutes a crucial objective within the framework of developing a control system to monitor the structure of industrial mechanisms. Even minor faults can give rise to significant consequences that require swift resolution. This research investigates the impact of overtension in the tooth belt transmission and heating of the screw transmission worm on the vibration signals in a robotic system. Utilizing FFT techniques, distinct frequency characteristics associated with different faults were identified. Overtension in the tooth belt transmission caused localized oscillations, addressed by adjusting the acceleration and deceleration speeds. Heating of the screw transmission worm led to widespread disturbances affecting servo stress and positioning accuracy. A fuzzy logic algorithm based on spectral analysis was proposed for adaptive control, considering the vibration’s frequency and amplitude. The simulation results demonstrated effective damage mitigation, reducing wear on the mechanical parts. The diagnostic approach, relying on limited data, emphasized the feasibility of identifying transmission damage, thereby minimizing maintenance costs. This research contributes a comprehensive and adaptive solution for robotic system diagnostics and control, with the proposed fuzzy logic algorithm showing promise for efficient signal processing and machine learning applications.

Intelligent robotic control system based on computer vision technology

Computer vision is a kind of simulation of biological vision using computers and related equipment. It is an important part of the field of artificial intelligence. Its research goal is to make computers have the ability to recognize three-dimensional environmental information through two-dimensional images. Computer vision is based on image processing technology, signal processing technology, probability statistical analysis, computational geometry, neural network, machine learning theory and computer information processing technology, through computer analysis and processing of visual information.The article explores the intersection of computer vision technology and robotic control, highlighting its importance in various fields such as industrial automation, healthcare, and environmental protection. Computer vision technology, which simulates human visual observation, plays a crucial role in enabling robots to perceive and understand their surroundings, leading to advancements in tasks like autonomous navigation, object recognition, and waste management. By integrating computer vision with robot control, robots gain the ability to interact intelligently with their environment, improving efficiency, quality, and environmental sustainability. The article also discusses methodologies for developing intelligent garbage sorting robots, emphasizing the application of computer vision image recognition, feature extraction, and reinforcement learning techniques. Overall, the integration of computer vision technology with robot control holds promise for enhancing human-computer interaction, intelligent manufacturing, and environmental protection efforts.

Modelling, Analysis and Comparison of Robot Energy Consumption for Three-Dimensional Concrete Printing Technology

The technology used for the 3D printing of buildings from concrete is currently a very relevant and developing topic and appears to be especially advantageous in terms of sustainable production. An important aspect of the sustainability assessment is the energy efficiency of the printing robots. Printing robots consume a significant amount of energy when printing. It is important to analyse this energy thoroughly and to be able to predict it in order to optimise the movement and control of printing robots to reduce energy consumption. In that paper, we analyse in detail the energy consumption of printing robots, which has not yet been thoroughly investigated in the context of 3D printing building applications. We present a methodology to develop an energy consumption model for a printing robot, specifically developed and optimized for this technology. Our methodology incorporates an innovative approach to determine reduced-efficiency maps, allowing for the inclusion of difficult-to-measure drive efficiency parameters in the model. This results in a comprehensive model of the energy consumption of the printing robot, reflecting its operating characteristics in a real-world environment. An open control system of the printing robot is used for the measurement of energy quantities, and specially developed software tools are introduced. We also present the first direct comparison of the energy consumption of different printing robots when following a uniform printing trajectory. The comparison is made based on the presented methodology to obtain and compare actual energy data from workplaces with printing robots. The methodology combines measured data with energy simulations from ABB RobotStudio, enabling energy comparisons between industrially articulated robots and real printing robots, including the ABB IRB4600, the gantry printing robot, and the printing robot. The experiments clearly demonstrate that the kinematic structure of printing robots significantly affects their energy consumption in 3D printing concrete. Based on the conducted methodologies and analyses, we identify key aspects of energy consumption of printing robots in 3D Construction Printing or 3D Concrete Printing (3DCP) technology. In doing so, we bring a new perspective and provide a basis for further research and development in this previously understudied area.

Broadening Bandwidth in a Semi-Active Vibration Absorption System Utilizing Stacked Polyvinyl Chloride Gel Actuators.

Plasticized polyvinyl chloride (PVC) gel is a new soft and smart material, whose potential in electroactive variable stiffness can be used for vibration control in soft robotic systems. In this paper, a new semi-active vibration absorber is developed by stacking PVC gel actuator units. The absorption bandwidth of a single PVC gel absorber covers the range of three natural frequencies (76.5 Hz, 95 Hz, 124 Hz) of a rectangular steel plate in vibration attenuation. The maximum reduction percentage in acceleration amplitude is 63%. With stacked PVC gel actuator units, the absorption bandwidth can be shifted and obviously broadened.

360 DEGREE DESTROYER FOR MILITARY

The "360 Degree Destroyer for Military" model operates by automatically detecting various threats such as missiles, tankers, guns, humans, and drones using a laptop camera. Detection is accomplished through the implementation of CNN algorithms. Instead of firing upon detection, the system utilizes a laser to pinpoint detected object and LED is used to indicate the threat level of the detected object, and this system also detects the land mine using metal sensor. Messages indicating the presence of detected objects are promptly sent to Telegram for immediate action. Additionally, the movement of the robot can be controlled manually via Bluetooth by establishing a communication link between a Bluetooth-enabled device (such as a smartphone or a computer) and the robot's control system. This link allows commands to be sent wirelessly from the device to the robot, enabling control over its movement. This manual control via Bluetooth can include commands to move forward, backward, turn left or right, stop, or perform other predefined actions. This innovative approach to threat detection and communication provides a flexible and efficient solution for defense strategies, enhancing situational awareness and response capabilities. Keywords--- Threat Detection, Land Mine, Missiles, Tankers, Guns, Humans, Drones, Laptop Camera, CNN, Laser, Led, Telegram, Bluetooth.

Analysis and Control of Partially Observed Discrete-Event Systems via Positively Constructed Formulas

This paper establishes a connection between control theory for partially observed discrete-event systems (DESs) and automated theorem proving (ATP) in the calculus of positively constructed formulas (PCFs). The language of PCFs is a complete first-order language providing a powerful tool for qualitative analysis of dynamical systems. Based on ATP in the PCF calculus, a new technique is suggested for checking observability as a property of formal languages, which is necessary for the existence of supervisory control of DESs. In the case of violation of observability, words causing a conflict can also be extracted with the help of a specially designed PCF. With an example of the problem of path planning by a robot in an unknown environment, we show the application of our approach at one of the levels of a robot control system. The prover Bootfrost developed to facilitate PCF refutation is also presented. The tests show positive results and perspectives for the presented approach.