Robot Control System Research Articles

The Application of an Intelligent Agaricus bisporus-Harvesting Device Based on FES-YOLOv5s

To address several challenges, including low efficiency, significant damage, and high costs, associated with the manual harvesting of Agaricus bisporus, in this study, a machine vision-based intelligent harvesting device was designed according to its agronomic characteristics and morphological features. This device mainly comprised a frame, camera, truss-type robotic arm, flexible manipulator, and control system. The FES-YOLOv5s deep learning target detection model was used to accurately identify and locate Agaricus bisporus. The harvesting control system, using a Jetson Orin Nano as the main controller, adopted an S-curve acceleration and deceleration motor control algorithm. This algorithm controlled the robotic arm and the flexible manipulator to harvest Agaricus bisporus based on the identification and positioning results. To confirm the impact of vibration on the harvesting process, a stepper motor drive test was conducted using both trapezoidal and S-curve acceleration and deceleration motor control algorithms. The test results showed that the S-curve acceleration and deceleration motor control algorithm exhibited excellent performance in vibration reduction and repeat positioning accuracy. The recognition efficiency and harvesting effectiveness of the intelligent harvesting device were tested using recognition accuracy, harvesting success rate, and damage rate as evaluation metrics. The results showed that the Agaricus bisporus recognition algorithm achieved an average recognition accuracy of 96.72%, with an average missed detection rate of 2.13% and a false detection rate of 1.72%. The harvesting success rate of the intelligent harvesting device was 94.95%, with an average damage rate of 2.67% and an average harvesting yield rate of 87.38%. These results meet the requirements for the intelligent harvesting of Agaricus bisporus and provide insight into the development of intelligent harvesting robots in the industrial production of Agaricus bisporus.

A study on motion control system for rehabilitation robot based on multi-sensor fusion

A study on motion control system for rehabilitation robot based on multi-sensor fusion

Industrial robot control system research based on the direction of programmable logic controllers

Industrial robot control system research based on the direction of programmable logic controllers

Performance investigation of MVMD-MSI algorithm in frequency recognition for SSVEP-based brain-computer interface and its application in robotic arm control.

This study focuses on improving the performance of steady-state visual evoked potential (SSVEP) in brain-computer interfaces (BCIs) for robotic control systems. The challenge lies in effectively reducing the impact of artifacts on raw data to enhance the performance both in quality and reliability. The proposed MVMD-MSI algorithm combines the advantages of multivariate variational mode decomposition (MVMD) and multivariate synchronization index (MSI). Compared to widely used algorithms, the novelty of this method is its capability of decomposing nonlinear and non-stationary EEG signals into intrinsic mode functions (IMF) across different frequency bands with the best center frequency and bandwidth. Therefore, SSVEP decoding performance can be improved by this method, and the effectiveness of MVMD-MSI is evaluated by the robot with 6 degrees-of-freedom. Offline experiments were conducted to optimize the algorithm's parameters, resulting in significant improvements. Additionally, the algorithm showed good performance even with fewer channels and shorter data lengths. In online experiments, the algorithm achieved an average accuracy of 98.31% at 1.8s, confirming its feasibility and effectiveness for real-time SSVEP BCI-based robotic arm applications. The MVMD-MSI algorithm, as proposed, represents a significant advancement in SSVEP analysis for robotic control systems. It enhances decoding performance and shows promise for practical application in this field.

FAULT-TOLERANT ADAPTIVE CONTROL OF A WALKING HEXAPOD WITH FAILURE OF ONE LEG

The article is devoted to the improvement of the control system of a walking mobile robot with six legs (hexapod) in order to ensure its stable movement in case of damage or failure of one limb. A tripod gait is considered as the basic movement algorithm. The failure of one of the joints is considered as a failure of the limb. To ensure movement in the event of failure of one limb, a tripod walking algorithm has been developed, which ensures that each of the defect-free limbs remains in the stage of stable support during two phases of movement in a row. The evaluation of the possibility of fault-tolerant gait of the hexapod according to the developed algorithm was performed on the basis of support triangles and a quadrangle between the limbs that are in the support phase. The structural and functional synthesis of the system of fault-tolerant adaptive control of the hexapod, which consists of the following modules and subsystems, was performed: module of the dynamic model of the robot; basic gait algorithm module; subsystem of executive elements; diagnosis and decision-making subsystem; a subsystem of alternative gait algorithms. The structural and functional scheme of the control channel was developed and substantiated for the implementation of the algorithm for diagnosing the joint of one limb, making a decision about its condition and switching to the continuation of the basic gait algorithm, if the condition of the joint is assessed as defect-free. The scheme of the control channel for switching to the execution of an alternative gait algorithm, if the condition of the joint is assessed as defective, has been developed and substantiated. The proposed structural and functional solutions are generalized for the case of failure of other joints of the considered limb and other limbs of the hexapod. It is shown that based on the detection and localization of joint failures of the hexapod limbs, the reconfiguration of the gait algorithm and the control system takes place, and in this way a fault-tolerant adaptive control of the movement of the hexapod is realized. The implementation of the proposed alternative tripod gait algorithms and control channels in the fault-tolerant adaptive control system will ensure stable movement of the hexapod in case of failure of one of the limbs.

Camera-based control system of a planar cable-driven parallel robot intended for functional rehabilitation

Abstract This paper investigates a closed-loop visual servo control scheme for controlling the position of a fully constrained cable-driven parallel robot (CDPR) designed for functional rehabilitation tasks. The control system incorporates real-time position correction using an Intel RealSense camera. Our CDPR features four cables exiting from pulleys, driven by AC servomotors, to move the moving platform (MP). The focus of this work is the development of a control scheme for a closed-loop visual servoing system utilizing depth/RGB images. The developed algorithm uses this data to determine the actual Cartesian position of the MP, which is then compared to the desired position to calculate the required Cartesian displacement. This displacement is fed into the inverse kinematic model to generate the servomotor commands. Three types of trajectories (circular, square, and triangular) are used to test the controller’s compliance with its position. Compared to the open-loop control of the robot, the new control system increases positional accuracy and effectively handles cable behavior, various perturbations, and modeling errors. The obtained results showed significant improvements in control performance, notably reduced root mean square error and maximal error in terms of position.

Predictive Maintenance and Fault Detection for Motor Drive Control Systems in Industrial Robots Using CNN-RNN-Based Observers

This research work presents an integrated method leveraging Convolutional Neural Networks and Recurrent Neural Networks (CNN-RNN) to enhance the accuracy of predictive maintenance and fault detection in DC motor drives of industrial robots. We propose a new hybrid deep learning framework that combines CNNs with RNNs to improve the accuracy of fault prediction that may occur on a DC motor drive during task processing. The CNN-RNN model determines the optimal maintenance strategy based on data collected from sensors, such as air temperature, process temperature, rotational speed, and so forth. The proposed AI model has the capacity to make highly accurate predictions and detect faults in DC motor drives, thus helping to ensure timely maintenance and reduce operational breakdowns. As a result, comparative analysis reveals that the proposed framework can achieve higher accuracy than the current existing method of combining CNN with Long Short-Term Memory networks (CNN-LSTM) as well as other CNNs, LSTMs, and traditional methods. The proposed CNN-RNN model can provide early fault detection for motor drives of industrial robots with a simpler architecture and lower complexity of the model compared to CNN-LSTM methods, which can enable the model to process faster than CNN-LSTM. It effectively extracts dynamic features and processes sequential data, achieving superior accuracy and precision in fault diagnosis, which can make it a practical and efficient solution for real-time fault detection in motor drive control systems of industrial robots.

Material Visual Perception and Discharging Robot Control for Baijiu Fermented Grains in Underground Tank.

Addressing the issue of excessive manual intervention in discharging fermented grains from underground tanks in traditional brewing technology, this paper proposes an intelligent grains-out strategy based on a multi-degree-of-freedom hybrid robot. The robot's structure and control system are introduced, along with analyses of kinematics solutions for its parallel components and end-effector speeds. According to its structural characteristics and working conditions, a visual-perception-based motion control method of discharging fermented grains is determined. The enhanced perception of underground tanks' positions is achieved through improved Canny edge detection algorithms, and a YOLO-v7 neural network is employed to train an image segmentation model for fermented grains' surface, integrating depth information to synthesize point clouds. We then carry out the downsampling and three-dimensional reconstruction of these point clouds, then match the underground tank model with the fermented grain surface model to replicate the tank's interior space. Finally, a digging motion control method is proposed and experimentally validated for feasibility and operational efficiency.

Adaptive Fuzzy Sliding Mode Controller Design for Uncertain Robotic Manipulator With Finite‐Time Convergence

ABSTRACTThis article investigates the issue of finite‐time tracking control for uncertain robotic manipulator systems with unknown actuator faults and shifting loads based upon a fuzzy sliding mode control strategy. A novel fuzzy adaptive sliding mode fault‐tolerant control law is synthesized, where a fuzzy approximation algorithm is utilized to fit the unknown plant parameters, potential disturbances, and relational actuator faults, and the corresponding adaptive robust term is designed to estimate the unidentified boundary of the estimated errors, and the boundedness of all the relevant design parameters of the manipulator systems is ensured combining with the average dwell time mechanism of switched system control theory. Furthermore, the reachability of the pre‐devised sliding surface and the finite‐time convergence of tracking error are achieved under the presented controller design. Ultimately, simulation results exhibit the feasibility and superiority of the proposed algorithm.

EEG-Based Mobile Robot Control Using Deep Learning and ROS Integration

Efficient BCIs (Brain-Computer Interfaces) harnessing EEG (Electroencephalography) have shown potential in controlling mobile robots, also presenting new possibilities for assistive technologies. This study explores the integration of advanced deep learning models—ASTGCN, EEGNetv4, and a combined CNN-LSTM architecture—with ROS (Robot Operating System) to control a two-wheeled mobile robot. The models were trained using a published EEG dataset, which includes signals from subjects performing thought-based tasks. Each model was evaluated based on its accuracy, F1-score, and latency. The CNN-LSTM architecture model exhibited the best performance on the cross-subject strategy with an accuracy of 88.5%, demonstrating significant potential for real-time applications. Integration with ROS was facilitated through a custom middleware, enabling seamless translation of neural commands into robot movements. The findings indicate that the CNN-LSTM model not only outperforms existing EEG-based systems in terms of accuracy but also underscores the practical feasibility of implementing such systems in real-world scenarios. Considering its efficacy, CNN-LSTM shows a great potential for assistive technology in the future. This research contributes to the development of a more intuitive and accessible robotic control system, potentially enhancing the quality of life for individuals with mobility impairments.

Touch-down condition control for the bipedal spring-mass model in walking

Behaviors of animal bipedal locomotion can be described, in a simplified form, by the bipedal spring-mass model. The model provides predictive power, and helps us understand this complex dynamical behavior. In this paper, we analyzed a range of gaits generated by the bipedal spring-mass model during walking, and proposed a stabilizing touch-down condition for the swing leg. This policy is stabilizing against disturbances inside and outside the same energy level and requires only internal state information. In order to generalize the results to be independent of size and dimension of the system, we nondimensionalized the equations of motion for the bipedal spring-mass model. We presented the equilibrium gaits (a.k.a fixed point gaits) as a continuum on the walking state space showing how the different types of these gaits evolve and where they are located in the state space. Then, we showed the stability analysis of the proposed touch-down control policy for different energy levels and leg stiffness values. The results showed that the proposed touch-down control policy can stabilize towards all types of the symmetric equilibrium gaits. Moreover, we presented how the peak leg force changes within an energy level and as it varies due to the type of the gait since peak force is important as a measurement of injury or damage risk on a robot or animal. Finally, we presented simulations of the bipedal spring-mass model walking on level ground and rough terrain transitioning between different equilibrium gaits as the energy level of the system changes with respect to the ground height. The analysis in this paper is theoretical, and thus applicable to further our understanding of animal bipedal locomotion and the design and control of robotic systems like ATRIAS, Cassie, and Digit.

Control system of a humanoid robot (NAO) by a laser scanner

Control system of a humanoid robot (NAO) by a laser scanner

A Latency Composition Analysis for Telerobotic Performance Insights Across Various Network Scenarios

Telerobotics involves the operation of robots from a distance, often using advanced communication technologies combining wireless and wired technologies and a variety of protocols. This application domain is crucial because it allows humans to interact with and control robotic systems safely and from a distance, often performing activities in hazardous or inaccessible environments. Thus, by enabling remote operations, telerobotics not only enhances safety but also expands the possibilities for medical and industrial applications. In some use cases, telerobotics bridges the gap between human skill and robotic precision, making the completion of complex tasks requiring high accuracy possible without being physically present. With the growing availability of high-speed networks around the world, especially with the advent of 5G cellular technologies, applications of telerobotics can now span a gamut of scenarios ranging from remote control in the same room to robotic control across the globe. However, there are a variety of factors that can impact the control precision of the robotic platform and user experience of the teleoperator. One such critical factor is latency, especially across large geographical areas or complex network topologies. Consequently, military telerobotics and remote operations, for example, rely on dedicated communications infrastructure for such tasks. However, this creates a barrier to entry for many other applications and domains, as the cost of dedicated infrastructure would be prohibitive. In this paper, we examine the network latency of robotic control over shared network resources in a variety of network settings, such as a local network, access-controlled networks through Wi-Fi and cellular, and a remote transatlantic connection between Finland and the United States. The aim of this study is to quantify and evaluate the constituent latency components that comprise the control feedback loop of this telerobotics experience—of a camera feed for an operator to observe the telerobotic platform’s environment in one direction and the control communications from the operator to the robot in the reverse direction. The results show stable average round-trip latency of 6.6 ms for local network connection, 58.4 ms when connecting over Wi-Fi, 115.4 ms when connecting through cellular, and 240.7 ms when connecting from Finland to the United States over a VPN access-controlled network. 255,235,61. These findings provide a better understanding of the capabilities and performance limitations of conducting telerobotics activities over commodity networks, and lay the foundation of our future work to use these insights for optimizing the overall user experience and the responsiveness of this control loop.

Concurrent Learning-Based Adaptive Control of Underactuated Robotic Systems With Guaranteed Transient Performance for Both Actuated and Unactuated Motions.

With the wide applications of underactuated robotic systems, more complex tasks and higher safety demands are put forward. However, it is still an open issue to utilize "fewer" control inputs to satisfy control accuracy and transient performance with theoretical and practical guarantee, especially for unactuated variables. To this end, for underactuated robotic systems, this article designs an adaptive tracking controller to realize exponential convergence results, rather than only asymptotic stability or boundedness; meanwhile, unactuated states exponentially converge to a small enough bound, which is adjustable by control gains. The maximum motion ranges and convergence speed of all variables both exhibit satisfactory performance with higher safety and efficiency. Here, a data-driven concurrent learning (CL) method is proposed to compensate for unknown dynamics/disturbances and improve the estimate accuracy of parameters/weights, without the need for persistency of excitation or linear parametrization (LP) conditions. Then, a disturbance judgment mechanism is utilized to eliminate the detrimental impacts of external disturbances. As far as we know, for general underactuated systems with uncertainties/disturbances, it is the first time to theoretically and practically ensure transient performance and exponential convergence speed for unactuated states, and simultaneously obtain the exponential tracking result of actuated motions. Both theoretical analysis and hardware experiment results illustrate the effectiveness of the designed controller.

High-Order Control Barrier Function-Based Safety Control of Constrained Robotic Systems: An Augmented Dynamics Approach

High-Order Control Barrier Function-Based Safety Control of Constrained Robotic Systems: An Augmented Dynamics Approach

Parametric function based fuzzy tuned path-following controller for nonholonomic mobile robotic systems: Experimental performance and analysis

Parametric function based fuzzy tuned path-following controller for nonholonomic mobile robotic systems: Experimental performance and analysis

Design and implementation of a simple quadruped robot

Abstract This paper designed a quadruped bionic robot control system based on STM32. Through the control module, execution module, and sensor module, precise control of the robot’s movements and posture was achieved. A posture feedback control algorithm ensured the adjustment speed of the torso. The hardware part was centered around the STM32 microcontroller, with the control circuit designed to integrate various communication interfaces such as UART and Ethernet. The QT5 software was used for simulation, analyzing the quadruped robot’s adaptability and stability during movement through the motor’s torque and angle output parameters, providing both theoretical and practical foundations for optimization and integration in real-world applications.

Speech-Controlled Robot Enabling Cognitive Training and Stimulation in Dementia Prevention for Severely Disabled People

Abstract The current German medical S3-guideline on dementia recommends the use of cognitive training and stimulation, e.g. through shared games, for mild cognitive impairment and mild dementia. The use of cobots, which allow direct human-robot collaboration, enables people with paralyzed upper limbs to actively participate in social activities. This research presents a novel approach through the development of a voice-controlled board game specifically tailored to the inclusion needs of people with severe disabilities. The human voice commands recorded via USB microphones are digitally filtered. Speech recognition of the control commands is performed using a Convolutional Neural Network (CNN) based on the VGG- 16 architecture. The robot´s activity is controlled utilizing the ROS 2 robot operating system. A portable table-based complete system with a 3D-printed Tic-Tac-Toe playing field and robot assistant for severely disabled paralyzed people has been developed. The robot control system employs a pick-and-place mechanism, seamlessly integrating with speech recognition to enhance gameplay interaction. The CNN model achieves an impressive accuracy rate of 97%, ensuring reliable speech recognition performance throughout gameplay. The targeted integration of robotic technologies and artificial intelligence opens new avenues in the prevention of mental illness, care support and inclusion of older and disabled people.

Ball Launching Robot and Object Retriever with V- Belt Method and Robotic Arm Method

This research develops a ball launcher and object picker robot controlled remotely /wirelessly using a microcontroller. The robot is designed to enhance mechanical work efficiency and control in the Indonesian robot contest, with the ability to automatically throw balls and collect objects in a designated area. The robot's drive system utilizes DC motors for mobility, with ball launching and pneumatic systems used for the object collection mechanism. This study emphasizes designing a more efficient system that offers good responsiveness, aiming to provide optimal robot control. The test results showed that the robot could perform ball launching with adequate accuracy and collect objects with suitable speed and precision. With the application of this technology, the robot is expected to be useful in various fields, including industry, sports games, education, agriculture, and other entertainment activities. This robot research opens opportunities for further development in more advanced and practical remote control robotic systems.

PID Algorithm Based on PSO Realizes the Dynamic Stable Output of the Robot

This paper uses a PID controller based on a particle swarm optimization (PSO) algorithm to improve the control performance of the robot dynamics system. Although the traditional PID controller is widely used in industrial automation, its control effect is often insufficient in the face of nonlinear and coupled systems, especially when dealing with external disturbances and parameter changes. To solve these problems, the PSO algorithm is used to optimize the parameters of the PID controller, so as to improve the dynamic response, control precision, and steady-state performance of the system. According to MATLAB simulation experiments, compared with the unoptimized PID controller, the PSO-optimized PID controller significantly improves the response speed of the system, reduces the steady-state error, and enhances the robustness of the system. The results show that the PSO optimization algorithm has a wide range of application potential, not only for robot control systems, but also for complex control scenarios such as unmanned aerial vehicles and autonomous driving.