Robot Control System Research Articles

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.

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

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.

Implementation of ESP32-Based Web Host For Control and Monitoring of Robotic Arm

In the modern technological era, the Internet of Things (IoT) plays a significant role in developing robotic control systems, including robotic arms. This study explores the implementation of an ESP32-based webhost to control and monitor robotic arm operations in real time via a web interface connected to a Wi-Fi network. The system is designed to provide ease of access, reliable performance, and low latency, making it suitable for education, technical training, and other applications. Testing revealed that the system delivers fast response times, high stability under moderate loads, and an intuitive interface for users with varying skill levels. These findings present opportunities for further development, including integration with cloud-based technologies and data security enhancements

Adaptive Fuzzy Event-Triggered Tracking Control of Flexible-Joint Robot Systems: Design and Experiment

Adaptive Fuzzy Event-Triggered Tracking Control of Flexible-Joint Robot Systems: Design and Experiment

Application of Compensation Algorithms to Control the Speed and Course of a Four-Wheeled Mobile Robot.

This article presents a tuned control algorithm for the speed and course of a four-wheeled automobile-type robot as a single nonlinear object, developed by the analytical approach of compensation for the object's dynamics and additive effects. The method is based on assessment of external effects and as a result new, advanced feedback features may appear in the control system. This approach ensures automatic movement of the object with accuracy up to a given reference filter, which is important for stable and accurate control under various conditions. In the process of the synthesis control algorithm, an inverse mathematical model of the robot was built, and reference filters were developed for a closed-loop control system through external effect channels, providing the possibility of physical implementation of the control algorithm and compensation of external effects through feedback. This combined approach allows us to take into account various effects on the robot and ensure its stable control. The developed algorithm provides control of the robot both when moving forward and backward, which expands the capabilities of maneuvering and planning motion trajectories and is especially important for robots working in confined spaces or requiring precise movement into various directions. The efficiency of the algorithm is demonstrated using a computer simulation of a closed-loop control system under various external effects. It is planned to further develop a digital algorithm for implementation on an onboard microcontroller, in order to use the new algorithm in the overall motion control system of a four-wheeled mobile robot.

Application and optimisation of intelligent control system for home robots

Abstract. As a significant part of the smart home, intelligent home robots can provide a variety of convenient home services such as automatic cleaning, elderly care, entertainment interaction, etc., which are highly valued and favoured by the majority of home users, and the market scale is expanding. However, the design and optimization of the control system represents a crucial technical challenge to truly realize the intelligence and autonomy of home robots. This paper presents a comprehensive analysis of the design and optimization of automatic control systems for intelligent home robots, which aims to improve the performance and user experience of intelligent home robots and better meet the needs of families. Through collection and analysis of the research on domestic robots and their control technology ,the core technical elements of the control system are summarized and refined, and the hardware structure and software framework of the control system are analyzed. The research results provide valuable insights for improving the performance and capabilities of home robot control systems to better meet the diverse needs and expectations of home users. These innovations provide a solid foundation for advancing the development of intelligent robots in the home. Future trends in control systems may include enhanced perceptual capabilities and decision-making intelligence to enable robots to better understand and meet the needs of home users.

Agreeing to Disagree: Translating Representations to Uncover a Unified Representation for Social Robot Actions

Researchers and designers of social robots often approach robot control system design from a single perspective; such as designing autonomous robots, teleoperated robots, or robots programmed by an end-user. While each design approach presents a tradeoff between some advantages and limitations, there is an opportunity to integrate these approaches where people benefit from the best-fit approach for their use case. In this work, we propose integrating these seemingly distinct robot control approaches to uncover a common data representation of social actions defining social expression by a robot. We demonstrate the value of integrating an authoring system, teleoperation interface, and robot planning system by integrating instances of these systems for robot storytelling. By relying on an integrated system, domain experts can define behaviors through end-user interfaces that teleoperators and autonomous robot programmers can use directly thus providing a cohesive expert-driven robot system.

FDM‐Printed CMOS Logic Gates from Flexing Beam Mechanisms for the Control of Soft Robotic Systems

Fluidic control systems target unique applications where conventional electronics fail. However, current fluidic control systems face challenges in accessible fabrication, reproducibility, and modifiable characteristics such as operating pressure and instability count. Herein, fused deposition‐modeled compliant mechanisms with flexing beams and soft linear actuators for fluid switching and the control of soft robotic systems are introduced. A linear actuator switches a compliant mechanism to cut off airflow through off‐the‐shelf tubing. The modular compliant logic devices can be configured as normally open or normally closed switches, as NOT, AND, and OR gates, and as nonvolatile memory elements. Their use is demonstrated in controlling a fluidic stepper motor, a worm‐like robot, and a fluidic display. These fluidic switches are printable using inexpensive desktop 3D printers, can be reliably reproduced in large quantities, and offer a wide range of modifiable parameters, including scalability, adaptability in operating pressure, and the tunability of instability counts for computational and memory functions.

Impulsive tracking synchronization control of networked robotic manipulator systems in the task space

This study investigates the impulsive tracking synchronization control of networked robotic manipulator systems based on Lagrangian systems. In the task space, the end-effector positions of networked robotic manipulators can synchronize and track a desired trajectory by designing an impulsive controller. Based on impulsive control, some algebraic synchronization criteria are derived. As a direct application of the theoretical results, tracking synchronization of six double-link robotic manipulators in the task space is discussed in detail. Numerical simulations are given to demonstrate the effectiveness and feasibility of the proposed control strategy.

Pengaruh Penyesuaian Parameter Membership Function pada Sistem Kendali Robot Balancing Berbasis Fuzzy Logic

This research develops a balancing robot control system using a fuzzy logic approach, focusing on the adjustment of membership function parameters. The main components of the system include the ESP32 microcontroller, MPU6050 sensor for detecting tilt angle and angular velocity, and L298 motor driver for DC motor actuation. Triangular-shaped membership functions are implemented, and parameters a, b, and c are adjusted through simulation to enhance system performance. Evaluation results indicate an average settling time of 1.2 seconds, a maximum overshoot of 5%, and a steady-state error of less than 2 degrees. This adjustment successfully balances response speed and stability, providing important guidance for developers in designing a more optimal fuzzy logic control system. The research was conducted at the Electrical Engineering Laboratory of 17 August 1945 University (Untag) Surabaya from June to December 2023.

АВТОМАТИЗОВАНА СИСТЕМА УПРАВЛІННЯ МАНІПУЛЯЦІЙНИМИ РОБОТАМИ

The article deals with the development of an automated control system for manipulative robots based on a mathematical model and the use of an Android mobile device for remote control. The main goal of the study is to create a prototype of a robotic arm that can be controlled using wireless technology, which provides convenience and efficiency in real-time control. The 4-degree-of-freedom robotic arm was created and is controlled through a mobile application developed using MIT App Inventor, which is based on Bluetooth technology. Data from the application is transmitted serially via a Bluetooth transmitter, processed by the receiver and used to control the robot's movements. The development and implementation of such a control system is an important step in the development of robotic technologies, as it not only provides a high level of automation but also allows robots to be used in various fields of activity, such as industry, medicine, logistics and other areas where the accuracy and speed of task performance are critical. A systematic approach to real-time control of manipulation mechanisms allows for high precision in control, which is especially important in a dynamically changing working environment. One of the key features of this system is the use of mathematical models that allow for a detailed analysis of the manipulator's kinematics and dynamics. This makes it possible to optimally control the robot's movements, which increases its flexibility in interacting with the environment. Mathematical models provide the ability to predict and correct robot actions in real time, which minimises the risk of errors and increases the overall efficiency of the system. One of the main advantages of using an Android mobile application is its accessibility and ease of use. Modern mobile devices have considerable computing power, which makes it possible to integrate complex control algorithms into the application and control robotic systems without the need for expensive specialised equipment. The application, created using MIT App Inventor, allows the user to easily configure robot control parameters, receive real-time feedback and control all aspects of the robot through a user-friendly interface. Bluetooth data transmission is another important component of the system, as it provides reliable and fast communication between the mobile device and the robot. The data is transmitted sequentially, which allows for precise coordination of the robot's movements in accordance with the commands coming from the app. This approach also minimises delays in the transmission of commands, which is key for real-time tasks. During the experiments, successful tests of the linear movement of the robotic arm were conducted, which confirmed the system's performance and its potential for further development. The tests demonstrated that the robot is able to accurately perform tasks related to linear movement, which is the basis for its use in more complex scenarios. This opens up wide opportunities for the introduction of such robots in various fields where it is necessary to perform tasks with high accuracy and speed. In general, the proposed system combines advanced technologies such as mathematical modelling, wireless communication and mobile applications, which ensures high flexibility and efficiency in the management of robotic systems. The use of mathematical models allows not only for precise control of the robot's movements, but also for adaptation to changes in the environment, which makes the system more stable and reliable in use.

Humanoid Robot Path Planning Using Rapidly Explored Random Tree and Motion Primitives

Path planning is an essential part of the control system of any mobile robot. In this article the path planner for a humanoid robot is presented. The short description of an universal control framework and the Motion Generation System is also presented. Described path planner utilizes a limited number of motions called the Motion Primitives. They are generated by Motion Generation System. Four different algorithms, namely the: Informed RRT, Informed RRT with random bias, and RRT with A* likeheuristics were tested. For the last one the version with biased random function was also considered. All mentioned algorithms were evaluated considering three dif ferent scenarios. Obtained results are described and discussed.

Decentralized fault-tolerant control of multi-mobile robot system addressing LiDAR sensor faults

The control of multi-robot formations is a crucial aspect in various applications, such as transport, surveillance and monitoring environments. Maintaining robots in a specific formation pose or performing a cooperative task is a significant challenge when a fault occurs among any of the robots. This work presents a Decentralized Fault-Tolerant Control (DFTC) scheme that addresses lidar sensor faults within a system of multiple differential wheeled mobile robots. The robots change the formation shape according to the number of available robots within the formation. A Graph theory is implemented to represent the multi-robot formation and communication. Each mobile robot is equipped with three sensors: a wheel encoder, an Inertial Measurement Unit (IMU), and a lidar sensor. Sensor fault detection and isolation (FDI) is implemented at two levels. The pose estimation obtained from the wheel encoder and IMU is fused using an extended Kalman filter (EKF), and this pose estimation is utilized at the local level of lidar sensor FDI. At the system level, the FDI of the lidar sensor involves computing a residual by comparing the pose estimation with other lidar sensors mounted on other mobile robots within the formation. The presented FTC scheme is simulated in Simulink multi-robot environments.

An Approach to Realize Generalized Optimal Motion Primitives Using Physics Informed Neural Networks

Abstract Autonomous manipulation is a challenging problem in field robotics due to uncertainty in object properties, constraints, and coupling phenomenon with robot control systems. Humans learn motion primitives over time to effectively interact with the environment. We postulate that autonomous manipulation can be enabled by basic sets of motion primitives as well, but do not necessitate mimicking human motion primitives. This work presents an approach to generalized optimal motion primitives using physics-informed neural networks. Our simulated and experimental results demonstrate that optimality is notionally maintained where the mean maximum observed final position percent error was 0.564% and the average mean error for all the trajectories was 1.53%. These results indicate that notional generalization is attained using a physics-informed neural network approach that enables near optimal real-time adaptation of primitive motion profiles.

Improved particle swarm optimization for fractional order PID control design in robotic manipulator system: A performance analysis

This research seeks to promote the field via the design and implementation optimized robotic manipulator control systems, recognizing control techniques' vital role in current engineering applications. This study introduces an improved particle swarm optimization (IPSO) technique that maximizes the efficiency of a Fractional Order Proportional-Integral-Derivative (FOPID) controller by optimally adjusting FOPID gains in robotic manipulator systems. The controller has undergone refinement and enhancement using state-of-the-art particle swarm optimization (PSO) techniques incorporating a cost function and a representative bio-inspired algorithm. The IPSO algorithm enhances global search efficiency by preventing premature convergence and local minima trapping through chaos-based initialization and adaptive mutation strategies. The performance of IPSO-tuned FOPID controllers is benchmarked against conventional PSO-tuned FOPID controllers using various objective functions. The stabilizing fractional order PID controllers demonstrated a higher stability margin than traditional PID controllers. Numerical simulations support the developed strategy by analyzing the step and sinusoidal responses of the closed-loop system within the stability region. The results indicate that IPSO outperforms PSO with improvements of approximately 50% for 10 iterations, about 12% for 50 iterations, and around 20% for 100 iterations across ITSE, ITAE, and ITAE metrics, respectively. Furthermore the statistical analysis based on Wilcoxon sign rank test proof that the IPSO algorithm significantly improves convergence speed, controller accuracy, and overall performance, thereby enhancing the effectiveness of the IPSO technique such as in case of 10 iteration the confidence intervals do not include zeros, which indicates that IPSO outperformed the traditional POS in all scenarios.