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This article focuses on the region-reaching control for a non-holonomic mobile robot system with two actuated wheels. The region-reaching control task consists of three basic control objectives: reaching an objective region, maintaining a rest state in the objective region, and stabilising at a desired posture. The constraints imposed on the motion of the mobile robot system are not integrable and have no inverse resolution, and thus increase the complexity of the controller design for non-holonomic systems. A system decomposition approach is proposed to convert the non-holonomic constraint control system to the cascade holonomic constraint control subsystems, and an adaptive control gain technique is presented to guarantee the stability of the non-holonomic system. Then, a novel torque controller is presented to implement the region-reaching control task. Specially, the objective region can be considered as a parking spot for a mobile robot. [ABSTRACT FROM AUTHOR]

The need for fully autonomous mobile robots has surged over the past decade, with the imperative of ensuring safe navigation in a dynamic setting emerging as a primary challenge impeding advancements in this domain. In this article, a Safety Critical Model Predictive Control based on Dynamic Feedback Linearization tailored to the application of differential drive robots with two wheels is proposed to generate control signals that result in obstacle‐free paths. A barrier function introduces a safety constraint to the optimization problem of the Model Predictive Control (MPC) to prevent collisions. Due to the intrinsic nonlinearities of the differential drive robots, computational complexity while implementing a Nonlinear Model Predictive Control (NMPC) arises. To facilitate the real‐time implementation of the optimization problem and to accommodate the underactuated nature of the robot, a combination of Linear Model Predictive Control (LMPC) and Dynamic Feedback Linearization (DFL) is proposed. The MPC problem is formulated on a linear equivalent model of the differential drive robot rendered by the DFL controller. The analysis of the closed‐loop stability and recursive feasibility of the proposed control design is discussed. Numerical experiments illustrate the robustness and effectiveness of the proposed control synthesis in avoiding obstacles with respect to the benchmark of using Euclidean distance constraints. [ABSTRACT FROM AUTHOR]

Target tracking is a key component of control systems with applications in various domains. Several examples of commercial applications may be given, which benefit from this technology: the development of car collision avoidance systems which must detect and track potential obstacles and hazards, or the development of UAVs that can track and record the evolution of athletes. For the purpose of our research, this technology was developed and tested as part of a less complex application. The current work describes a simple yet practical implementation of vision-based control for a mobile robot system. In the experiment, we used a mobile robot as target and a similar robot as follower. To achieve the tracking task, the used strategy involves the detection of a specific visual object mounted on the target, by extracting its features which are then used in issuing control commands within a remotely-closed loop. A Deep Learning approach is used for object detection, incorporating a detector model into the strategy while preserving an explicit controller in the overall scheme. The carried experiment has proven that this new approach provides the expected results, which make it a suitable tool for development of larger scale applications of control systems. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

With the development of automation and intelligent technologies, the demand for autonomous mobile robots in the industry has surged to alleviate labor-intensive tasks and mitigate labor shortages. However, conventional industrial mobile robots' route-tracking algorithms typically rely on passive markers, leading to issues such as inflexibility in changing routes and high deployment costs. To address these challenges, this study proposes a novel approach utilizing active landmarks—battery-powered luminous landmarks that enable robots to recognize and adapt to flexible navigation requirements. However, the reliance on batteries necessitates frequent recharging, prompting the development of an automatic power supply system. This system integrates omnidirectional contact electrodes on mobile robots, allowing to recharge active landmarks without precise positional alignment. Despite these advancements, challenges such as the large size of electrodes and non-adaptive battery charging across landmarks persist, affecting system efficiency. To mitigate these issues, this research focuses on miniaturizing active landmarks and optimizing power distribution among landmarks. The experimental results of this study demonstrated the effectiveness of our automatic power supply method and the high accuracy of landmark detection. Our power distribution calculation method can adaptively manage energy distribution, improving the system's persistence by nearly three times. This study aims to enhance the practicality and efficiency of mobile robot remote control systems utilizing active landmarks by simplifying installation processes and extending operational durations with adaptive and automatic power supply distribution. [ABSTRACT FROM AUTHOR]

Brain-computer interface (BCI) provides direct communication and control between the human brain and physical devices. It is achieved by converting EEG signals into control commands. Such interfaces have significantly improved the lives of disabled individuals suffering from neurological disorders—such as stroke, amyotrophic lateral sclerosis (ALS), and spinal cord injury—by extending their movement range and thereby promoting self-independence. Brain-controlled mobile robots, however, often face challenges in safety and control performance due to the inherent limitations of BCIs. This paper proposes a shared control scheme for brain-controlled mobile robots by utilizing fuzzy logic to enhance safety, control performance, and robustness. The proposed scheme is developed by combining a self-learning neuro-fuzzy (SLNF) controller with an obstacle avoidance controller (OAC). The SLNF controller robustly tracks the user's intentions, and the OAC ensures the safety of the mobile robot following the BCI commands. Furthermore, SLNF is a model-free controller that can learn as well as update its parameters online, diminishing the effect of disturbances. The experimental results prove the efficacy and robustness of the proposed SLNF controller including a higher task completion rate of 94.29% (compared to 79.29%, and 92.86% for Direct BCI and Fuzzy-PID, respectively), a shorter average task completion time of 85.31 s (compared to 92.01 s and 86.16 s for Direct BCI and Fuzzy-PID, respectively), and reduced settling time and overshoot. [ABSTRACT FROM AUTHOR]

This article addresses the issue of collaborative wildfire monitoring using a group mobile mecanum‐wheeled omnidirectional vehicles (MWOVs) affected by nonlinear uncertainties and external disturbances. By integrating finite‐time extended state observers (FTESO) and backstepping nonsingular fast terminal sliding mode (BNFTSM) control method, an observer‐based finite‐time time‐varying elliptical formation control scheme is proposed for a group of MWOVs tasked with monitoring the propagation of wildfires in an elliptical pattern. First, the FTESO is employed to estimate the unavailable velocity system states and the lumped disturbances. Then, a novel nonsingular fast terminal sliding surface, enhanced with an exponential term, is introduced to improve the convergence rate. Through the Lyapunov theorem, the convergence of position and velocity cooperative tracking errors to zero in fast finite‐time is demonstrated. To showcase the effectiveness of the proposed control scheme, comparative simulation results are presented. [ABSTRACT FROM AUTHOR]

This paper presents a decentralized, cooperative, real-time avoidance control strategy for robotic manipulators. The proposed avoidance control law builds on the concepts of artificial potential field functions and provides tighter bounds on the minimum safe distance when compared to traditional potential-based controllers. Moreover, the proposed avoidance control law is given in analytical, continuous closed form, avoiding the use of optimization techniques and discrete algorithms, and is rigorously proven to guarantee collision avoidance at all times. Examples of planar and 3D manipulators with cylindrical links under the proposed avoidance control are given and compared with the traditional approach of modeling links and obstacles with multiple spheres. The results show that the proposed avoidance control law can achieve, in general, faster convergence, smaller tracking errors, and lower control torques than the traditional approach. Furthermore, we provide extensions of the avoidance control to robotic manipulators with bounded control torques. [ABSTRACT FROM AUTHOR]

Mobile robots often follow humans in warehouse environments or indoor office spaces. A mobile robot requires a control system that stably follows humans without colliding with them. A human-following robot is composed of target detection, target tracking, and control. Conventional 2D laser-based human following systems exploit information from 2D planar laser data to detect human legs through machine learning methods, such as support vector data description and random forest. However, in crowded or cluttered environments, 2D LiDAR data is limited by the lack of features that distinguish human legs from obstacles. Due to the lack of features, false-positive detection is a problem in crowded or cluttered environments. Recent studies using 3D LiDAR have used sensors mounted at an elevated height to measure the overall shape of a person to extract features. Typical mobile robots are mounted in the bottom due to the vibration of the sensors, so there is a cost problem that additional sensors are required. We propose a framework for human following using 3D LiDAR. Our method is able to detect human legs using a LiDAR sensor attached at a low position without additional sensors. This study proposes a 3D laser-based human leg detection and tracking framework to improve the robustness of human-following for autonomous mobile robots. With a deep learning-based human leg detector, using the Point-Voxel-RCNN model, the proposed 3D human leg tracking system can help robots robustly follow humans in cluttered and crowded environments. Additionally, we demonstrate the robustness of our method in a practical cluttered environment by comparing the performance of a conventional human leg detection and following system. [ABSTRACT FROM AUTHOR]

This paper outlines a novel approach to developing navigation control systems for autonomous mobile robots utilizing 3D Lidar technology. By harnessing the capabilities of 3D Lidar sensors, robots can perceive their environment with remarkable precision, enabling them to navigate effectively in complex and dynamic surroundings. The proposed control framework encompasses a fusion of sensor data processing, localization algorithms, path planning strategies, and motion control mechanisms. Through the integration of these components, autonomous mobile robots can navigate diverse environments, both indoors and outdoors, with efficiency and safety. The use of 3D Lidar technology enhances the robots' perception capabilities, allowing them to generate detailed and accurate maps of their surroundings in real-time. Additionally, adaptive algorithms are incorporated into the control system to address uncertainties and dynamically changing obstacles encountered during navigation. Experimental results demonstrate the effectiveness and reliability of the proposed navigation control systems, highlighting their potential for various real-world applications such as warehouse logistics, surveillance, and search and rescue operations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The tracking control problem of non‐holonomic mobile robot systems has been extensively investigated in the past decades, however, most of the existing control strategies were developed specifically for the fixed‐point tracking. This technical note focuses on the region tracking control for a non‐holonomic mobile robot system with parameter uncertainties in the robot dynamics. With the system decomposition and adaptive control method, some restrictions imposed on the angular and linear velocities of the non‐holonomic mobile robot in recent literature are removed, enabling to track dynamic trajectories with any values of the angular and line velocities. The proposed adaptive control scheme can simultaneously solve both the regulation and region tracking problems of a non‐holonomic mobile robot with one passive wheel and two actuated wheels. By utilizing the designed control laws, the mobile robot system is able to globally reach inside a moving region specified by potential functions whose path can be a circular curve, a straight line, or sinusoidal curve, by using a single adaptive controller. Since the dynamic region can be specified arbitrarily small, the fixed‐point tracking can be regarded as a special case of region tracking studied in this paper. Compared with the traditional fixed‐point tracking, region tracking has more flexibility and better robustness. Numerical results are presented to show the effectiveness of the designed strategy. [ABSTRACT FROM AUTHOR]

Troops in the context of reconnaissance or search tasks to find out the enemy in difficult, protected and disguised terrain is an element of vulnerability that needs to be taken into account. If you insist on finding out the enemy's condition, there is a possibility that contact will occur early and cause casualties which could thwart the main task. Therefore, a tool or robot is needed to help obtain data about protected enemies by diverting the enemy's attention and providing resistance. The aim of this research is to create a prototype chainwheeled combat robot platform system that is capable of maneuvering, firing SS2 and rockets, detecting mine-prone areas, and detecting body thermals. The robot control system consists of a mobile controller and a base station. The mobile controller functions to control robot movements such as forward, backward, right turn, left turn, weapon azimuth movement, weapon elevation movement and shooting using 2.4 GHz radio control and target monitoring using FPV ((First person view) directly in the field on a frequency of 5.6 GHz, base station functions to control the robot's movement and monitor targets using a laptop via the internet. The robot system is equipped with GPS and a metal detector, so that the robot's position can be monitored and report the presence of mines to the base station. Thus, the robot created is expected to be able to help to combat duty in the context of war military operations [ABSTRACT FROM AUTHOR]

Over decades of robotics research, cooperative object transportation has been studied as a meaningful model problem for robotic networks because it possesses a variety of crucial challenges. Although these challenges are demanding, the cooperation of multiple robots has the potential to solve automation problems that are beyond the scope of an individual robot. So far, the model problem has mostly been addressed by explicitly controlling the robots' positions. However, the position-based approach suffers from some intrinsic detriments, for example, the lack of explicit feedback between robots and object. Moreover, it remains an open question how many robots shall be employed to ensure a successful transportation. This paper's purpose is to overcome these challenges using a novel force-based approach taking into account the robots' actual manipulation capabilities, that is, the exerted forces. Using cost-efficient hardware, the interaction forces are measured and, what is more, explicitly controlled by a highly responsive onboard controller. Employing a tailored software architecture, the novel force-based scheme, useful for robotic manipulation beyond the benchmark problem, is probably the most flexible of its kind regarding the number of robots and the object's shape. The controller's functionality and performance as well as the scheme's versatility are demonstrated by several hardware experiments. [ABSTRACT FROM AUTHOR]

Background: Hydraulic quadruped robots have broad application prospects. Control system design is the core content of robot design. However, the micro-controllers used in the past have shortcomings such as long sampling period and simple algorithm. Methods: An electric control system of the layered, distributed structure for hydraulically actuated quadruped robots is designed considering a dog as a bionic model. In order to improve the response time and the steady precision of the system at the same time, a Fuzzy–PID compound control algorithm is put forward in this paper. The hardware and software of the control system are designed. Results: The lower computer's control system for hydraulically actuated quadruped robots is developed using TMS320F28335 in series of DSP2000 as the core processor. Outside control circuit expands some external chips, such as AD7606, AD5754R, and PCA82C250, and a peripheral interface circuit is designed. Taking full advantage of the efficient processing power of the chip and the rich on-chip resources, the hardware circuit is simpler and reliable, and the software is also easy to implement. It is verified that the control system is rational and effective using experiments. Conclusions: The experimental results show that the control system designed in this paper is reasonable and can effectively control the joints of the quadruped robot. It has strong scalability and can meet the basic requirements of the autonomous mobile robot control system. [ABSTRACT FROM AUTHOR]

In this article, the fault-attack control problem is investigated for wheeled mobile robot (WMR) systems subjected to actuator faults, disturbances, communication attacks, and limited communication resources. An event-observer based compensation controller is presented. With the help of the observer estimation values and the attack sleep/active instant trigger information, the tracking control performance is realized for the robot system with the assistance of the neural network approximation technology. Concretely, first, the robot system dynamic model with actuator faults, disturbances, and attacks is established. Then, an event-based proportional–integral observer (PIO) is established. In the observer framework, a state estimator, an actuator fault efficiency estimator, and a disturbance estimator are embedded. Based on the observer outputs, a second-order adaptive sliding mode fault-compensation reliable controller is presented. In this controller framework, the fault compensation, disturbance attenuation, and the attack sleep/active time instant information are contained to guarantee the reliability and performance recoverability of the robot system. Furthermore, a dynamic even condition and an adaptive trigger scheme are constructed in the sensor and actuator channel to achieve the communication-efficient purpose. Finally, two cases of the robot system are performed to verify the system recoverability of the presented approach. • A class of wheeled mobile robot systems with actuator faults, disturbances, and communication attacks is established. • An event-based proportional–integral observer framework is designed for the robot system to estimate the states, actuator faults and lumped disturbances. • A novel observer-event-based adaptive fault-attack-tolerant control architecture is presented to simultaneously preserve and recover control performance for the robot system while saving the communication resources. [ABSTRACT FROM AUTHOR]

The rapid development of network technology makes the world enter a new era, but it also brings a variety of severe information security problems, which makes the network security more and more important. Based on the existing literature and information, this paper expounds the current situation of database information system security. In view of the security risks of the media database information, this paper proposes to use the remote control system of the network robot, through sorting out the organizational structure characteristics and technology of multimedia data, establish the security defense system, and try to manage the multimedia data security. This system takes the mobile robot as the control object and adds the vision and ultrasonic transducer for the robot, so that it can carry on the semi-autonomous movement under the control of human. In addition, the host PC is used as the main control terminal to send instructions to the remote robot and receive the feedback data information. The simulation results show that the robot remote control system has good security, can ensure the establishment of database security defense technology system, and has the characteristics of convenient operation, easy to expand, and strong mobility. From the research point of view of this paper, the combination of robotics and multimedia information security is booming and will become a new application prospect. [ABSTRACT FROM AUTHOR]

The integration of intelligent robots in industrial production processes has the potential to significantly enhance efficiency and reduce human adversity. However, for such robots to effectively operate within human environments, it is critical that they possess an adequate understanding of their surroundings and are able to navigate through narrow aisles while avoiding both stationary and moving obstacles. In this research study, an omnidirectional automotive mobile robot has been designed for the purpose of performing industrial logistics tasks within heavy traffic and dynamic environments. A control system has been developed, which incorporates both high-level and low-level algorithms, and a graphical interface has been introduced for each control system. A highly efficient micro-controller, namely myRIO, has been utilized as the low-level computer to control the motors with an appropriate level of accuracy and robustness. Additionally, a Raspberry Pi 4, in conjunction with a remote PC, has been utilized for high-level decision making, such as mapping the experimental environment, path planning, and localization, through the utilization of multiple Lidar sensors, IMU, and odometry data generated by wheel encoders. In terms of software programming, LabVIEW has been employed for the low-level computer, and the Robot Operating System (ROS) has been utilized for the design of the higher-level software architecture. The proposed techniques discussed in this paper provide a solution for the development of medium- and large-category omnidirectional mobile robots with autonomous navigation and mapping capabilities. [ABSTRACT FROM AUTHOR]

This paper designed a voice interactive robot system that can conveniently execute assigned service tasks in real-life scenarios. It is equipped without a microphone where users can control the robot with spoken commands; the voice commands are then recognized by a well-trained deep neural network model of automatic speech recognition (ASR), which enables the robot to execute and complete the command based on the navigation of a real-time simultaneous localization and mapping (SLAM) algorithm. The voice interaction recognition model is divided into two parts: (1) speaker separation and (2) ASR. The speaker separation is applied by a deep-learning system consisting of eight convolution layers, one LSTM layer, and two fully connected (FC) layers to separate the speaker's voice. This model recognizes the speaker's voice as a referrer that separates and holds the required voiceprint and removes noises from other people's voiceprints. Its automatic speech recognition uses the novel sandwich-type conformer model with a stack of three layers, and combines convolution and self-attention to capture short-term and long-term interactions. Specifically, it contains a multi-head self-attention module to directly convert the voice data into text for command realization. The RGB-D vision-based camera uses a real-time appearance-based mapping algorithm to create the environment map and replace the localization with a visional odometer to allow the robot to navigate itself. Finally, the proposed ASR model was tested to check if the desired results will be obtained. Performance analysis was applied to determine the robot's environment isolation and voice recognition abilities. The results showed that the practical robot system successfully completed the interactive service tasks in a real environment. This experiment demonstrates the outstanding performance with other ASR methods and voice control mobile robot systems. It also verified that the designed voice interaction recognition system enables the mobile robot to execute tasks in real-time, showing that it is a convenient way to complete the assigned service applications. [ABSTRACT FROM AUTHOR]

The Mindstorms EV3 robot, developed by LEGO, is one of the popular robots that has been widely used in various fields. Unlike previous versions of mobile LEGO robots, EV3 allows the development of real-time applications for teaching a variety of subjects, as well as for conducting research experiments. The object of research in this case is the Mindstorms EV3 robot connected to MATLAB/Simulink. The design consists of a controller, one color sensor, two servo motors and one support wheel. Each servo motor is built on a DC collector motor with a matching gearbox and has the ability to measure the number of revolutions corresponding. A digital sensor with a sampling frequency of 1 kHz is used as a color sensor, which can determine the color or brightness of light. Despite its popularity, the EV3 robot control system in interaction with the MATLAB/Simulink programming environment is a rather complex solution and therefore requires further research. The scientific part of the research focuses on discovering the regularities of the Mindstorms EV3 control system, developing a control system model, and exploring the potential of MATLAB/Simulink to expand the robot’s capabilities. An analysis of the main elements of the control system, such as sensors and servos, was carried out. The graphs of the dependences of the characteristics of the servo drives were built and the efficiency of the robot movement was checked depending on the parameters set in the program. The result of the development of the mobile robot control system was the adjustment of the mobile robot movement regulators along a given trajectory in the form of a drawn line, which allowed estimating the maximum permissible speed of the robot movement. The presented research and development of a control system based on MATLAB/Simulink elements allows using the proposed method to control a mobile robot with high precision, analyze and verify the robot’s electromechanical parameters in real time. This control system has a high potential and can practically be integrated into industrial objects of mobile robotics, provided types the sensors and executive mechanisms of the mobile robot match. [ABSTRACT FROM AUTHOR]

Extended Kalman filter (EKF) is one of the most widely used Bayesian estimation methods in the optimal control area. Recent works on mobile robot control and transportation systems have applied various EKF methods, especially for localization. However, it is difficult to obtain adequate and reliable process-noise and measurement-noise models due to the complex and dynamic surrounding environments and sensor uncertainty. Generally, the default noise values of the sensors are provided by the manufacturer, but the values may frequently change depending on the environment. Thus, this paper mainly focuses on designing a highly accurate trainable EKF-based localization framework using inertial measurement units (IMUs) for the autonomous ground vehicle (AGV) with dead reckoning, with the goal of fusing it with a laser imaging, detection, and ranging (LiDAR) sensor-based simultaneous localization and mapping (SLAM) estimation for enhancing the performance. Convolution neural networks (CNNs), backward propagation algorithms, and gradient descent methods are implemented in the system to optimize the parameters in our framework. Furthermore, we develop a unique cost function for training the models to improve EKF accuracy. The proposed work is general and applicable to diverse IMU-aided robot localization models. [ABSTRACT FROM AUTHOR]

Recently, interval-type 3 fuzzy systems have begun to appear in different research areas. This article outlines a methodology for the parameterization of interval type-3 membership functions using vertical cuts applied to the dynamic parameter adaptation of the differential evolution algorithm and implemented in an interval-type 3 Sugeno controller. This methodology was applied to the dynamic adaptation of the F (mutation) parameter in differential evolution to improve the performance of this method as the generations occur. To test the type-3 fuzzy differential evolution algorithm, the optimal design of a type-3 Sugeno controller was considered. In this case, the parameterization of the type-3 membership functions of this Sugeno fuzzy controller was performed. The experimentation is based on the application of three different noise levels for validation of the efficacy of the method and performing a comparison study with respect to other articles in the literature. The main idea is to implement the parameterization of interval type-3 membership functions to enhance the ability of differential evolution in designing an optimal interval type-3 system to control a unicycle mobile robot. [ABSTRACT FROM AUTHOR]

Robots in the medical industry are becoming more common in daily life because of various advantages such as quick response, less human interference, high dependability, improved hygiene, and reduced aging effects. That is why, in recent years, robotic aid has emerged as a blossoming solution to many challenges in the medical industry. In this manuscript, meta-heuristics (MH) algorithms, specifically the Firefly Algorithm (FF) and Genetic Algorithm (GA), are applied to tune PID controller constraints such as Proportional gain Kp Integral gain Ki and Derivative gain Kd. The controller is used to control Mobile Robot System (MRS) at the required set point. The FF arrangements are made based on various pre-analysis. A detailed simulation study indicates that the proposed PID controller tuned with Firefly Algorithm (FF-PID) for MRSis beneficial and suitable to achieve desired closed-loop system response. The FF is touted as providing an easy, reliable, and efficient tuning technique for PID controllers. The most suitable ideal performance is accomplished with FF-PID, according to the display in the time response. Further, the observed response is compared to those received by applying GA and conventional off-line tuning techniques. The comparison of all tuning methods exhibits supremacy of FF-PID tuning of the given nonlinear Mobile Robot System than GA-PID tuning and conventional controller. [ABSTRACT FROM AUTHOR]

The present study addresses the problem of fixed-time stabilization (FTS) of mobile robots (MRs). The study's distinguishing aspects are that the system under examination is subjected to external disturbances, and the system states are pushed to zero in a finite time. This paper suggests new control techniques for chained-form nonholonomic systems (CFNS) subjected to disturbances. First, a switching fractional-order (FO) control approach is proposed for a first-order subsystem (FOS) of an MR under complex disturbances. Secondly, an FO generic global sliding mode control approach is designed for the second-order system (SOS) of the MR in the presence of disturbances. The suggested sliding manifold for the SOS of the MR guarantees global system stability and reduces the chattering problem during control operations. A conventional quadratic Lyapunov function (QLF) is used to converge to the origin in a finite time (FnT). Through this study, a stabilizer for an MR in the presence of disturbances based on an FO switching time-varying controller that can stabilize immeasurable states in a fixed time is proposed. Finally, three case simulations are provided to demonstrate the efficacy of the control strategy proposed in this work against external disturbances. [ABSTRACT FROM AUTHOR]

This article addresses the problem of cloud distributed control systems development for mobile robots. The authors emphasize the lack of a design methodology to guide the process of the development in accordance with specific technical and economic requirements for the robot. On the analysis of various robots architectures, the set of the nine most significant parameters are identified to direct the development stage by stage. Based on those parameters, the design methodology is proposed to build a scalable three-level cloud distributed control system for a robot. The application of the methodology is demonstrated on the example of AnyWalker open source robotics platform. The developed methodology is also applied to two other walking robots illustrated in the article. [ABSTRACT FROM AUTHOR]

In this paper, we consider the distributed control design problem for Nash equilibrium (NE) seeking of a multi-agent mobile robot system using only sampled neighbor measurements. To this end, we define new continuously differentiable variables, the so-called S-GRANE variables, that smoothly incorporate the sampled neighbor information within a sampling period. It is proven that the NE seeking problem can be restated as a regulation problem for the S-GRANE variables. Communication time-delays are also allowed with the new variables since the samples are processed after some fixed time has passed from their sampling time. Distributed control laws are then designed for the mobile robot multi-agent system dynamics based on the prescribed performance control (PPC) methodology that approximately solve the NE seeking problem by regulating the S-GRANE variables to a neighborhood of zero. Simulation results are shown that verify our theoretical analysis. [ABSTRACT FROM AUTHOR]

Aiming at the problem of trajectory tracking control for mobile robot nonlinear systems with non-repetitive uncertain parameters, we propose a novel neural network compensator-based robust iterative learning control (NNRILC) scheme to achieve excellent tracking performance and uncertainty compensation. The NNRILC scheme consists of a parallel structure that includes a robust iterative learning control (RILC) term and a neural network (NN) compensator. The RILC term is used to ensure robust control performance and promise closed-loop stability. The compensator based on the neural network is employed to handle the uncertainties resulted from the nonlinear dynamics as well as the suppressed disturbances in the mobile robot nonlinear systems. Additionally, the fourth-order Runge-Kutta algorithm is employed to solve the state differential equation of the mobile robot. Consequently, The H ∞ robust technique is used to construct an iterative learning control update rule to reduce the impact of disturbances. Meanwhile, the RILC scheme is designed to optimize the neural network initial parameters and weights of the NN compensator at each trial. Then the convergence of the proposed NNRILC scheme is analyzed, and the results are incorporated into the control update for the next process trial. Finally, the effectiveness of the proposed method is verified by mobile robot simulation. • A novel ILC scheme combining neural network compensator is designed to solve trajectory tracking problem. • The Runge-Kutta algorithm is introduced to solve the state differential equation and the controller equation. • A neural network compensator is constructed to compensate the suppressed noises, disturbances, and uncertainties. • The H∞ robust technique is used to suppress the noises, disturbances in the ILC process. [ABSTRACT FROM AUTHOR]

The haptic terrain classification is an essential component of a mobile walking robot control system, ensuring proper gait adaptation to the changing environmental conditions. In practice, such components are a part of an autonomous system and thus have to be lightweight, provide fast inference time, and guarantee robustness to minor changes in recorded sensory data. We propose transformer-based HAPTR and HAPTR2 terrain classification methods that use force and torque measurements from feet to meet these requirements. For reliable comparison of the proposed solutions, we adapt two classical machine learning algorithms (DTW-KNN and ROCKET), one temporal convolution network (TCN), and use the state-of-the-art CNN-RNN. The experiments are performed on publicly available PUTany and QCAT datasets. We show that the proposed HAPTR and HAPTR2 methods achieve accuracy on par or better than state-of-the-art approaches with a lower number of parameters, faster inference time, and improved robustness to input signal distortions. These features make HAPTR and HAPTR2 excel in terrain recognition tasks when considering real-world requirements. • Fast and lightweight transformer-based approach for the terrain classification. • Modality Attention Layer improves the system's robustness in real-world scenarios. • Autonomy requirements inspired our evaluation of terrain classification algorithms. • The benchmark on two open source datasets creates a baseline for further research. [ABSTRACT FROM AUTHOR]