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This paper introduces a novel resource-efficient control structure for remote path-following control of autonomous vehicles based on a comprehensive combination of Kalman filtering, non-uniform dual-rate sampling, periodic event-triggered communication, and prediction-based and packet-based control techniques. An essential component of the control solution is a non-uniform dual-rate extended Kalman filter (NUDREKF), which includes an h-step ahead prediction stage. The prediction error of the NUDREKF is ensured to be exponentially mean-square bounded. The algorithmic implementation of the filter is straightforward and triggered by periodic event conditions. The main goal of the approach is to achieve efficient usage of resources in a wireless networked control system (WNCS), while maintaining satisfactory path-following behavior for the vehicle (a holonomic Mecanum-wheeled robot). The proposal is additionally capable of coping with typical drawbacks of WNCS such as time-varying delays, and packet dropouts and disorder. A Simscape Multibody simulation application reveals reductions of up to 93% in resource usage compared to a nominal time-triggered control solution. The simulation results are experimentally validated in the holonomic Mecanum-wheeled robotic platform. • A novel, comprehensive periodic event-triggered networked control solution is proposed. • High reduction of resource usage (of up to 93%) can be reached while maintaining acceptable control performance. • A non-uniform dual-rate extended Kalman filter (NUDREKF) is essential in the control structure. • Some conditions are provided such that the prediction error of the NUDREKF is exponentially mean square bounded. • Real path-following control tests for a holonomic Mecanum-wheeled robot validate the control solution. [ABSTRACT FROM AUTHOR]

When legged robots perform complex tasks in unstructured environments, falls are inevitable due to unknown external disturbances. However, current research mainly focuses on the locomotion control of legged robots without falling. This paper proposes a comprehensive decision-making and control framework to address the falling over of quadruped robots. First, a capturability-based fall prediction algorithm is derived for planar single-contact and 3D multi-contact locomotion with a predefined gait sequence. For safe fall control, a novel contact-implicit trajectory optimization method is proposed to generate both state and input trajectories and contact mode sequences. Specifically, incorporating uncertainty into the system and terrain models enables mitigating the non-smoothness of contact dynamics while improving the robustness of the resulting trajectories. Furthermore, a model-free deep reinforcement learning-based approach is presented to achieve fall recovery after the robot completes a fall. Experimental results demonstrate that the proposed fall prediction algorithm accurately predicts robot falls with up to 95% accuracy approximately 395ms in advance. Compared to classical locomotion controllers, which often struggle to maintain balance under significant pushes or terrain perturbations, the presented framework can autonomously switch to the fall controller approximately 0.06s after the perturbation, effectively preventing falls or achieving recovery with a threefold reduction in touchdown impact velocity. These findings highlight the effectiveness of the proposed framework in enhancing the stability and safety of legged robots in unstructured environments. • The proposed fall prediction approach exhibits significantly enhanced reliability and flexibility. • The proposed controller can autonomously generate contact sequences, reducing damage from falls. • The designed reinforcement learning-method improves efficiency and robustness in fall recovery. [ABSTRACT FROM AUTHOR]

The objective of this paper is to address the localization problem using omnidirectional images captured by a catadioptric vision system mounted on the robot. For this purpose, we explore the potential of Siamese Neural Networks for modeling indoor environments using panoramic images as the unique source of information. Siamese Neural Networks are characterized by their ability to generate a similarity function between two input data, in this case, between two panoramic images. In this study, Siamese Neural Networks composed of two Convolutional Neural Networks (CNNs) are used. The output of each CNN is a descriptor which is used to characterize each image. The dissimilarity of the images is computed by measuring the distance between these descriptors. This fact makes Siamese Neural Networks particularly suitable to perform image retrieval tasks. First, we evaluate an initial task strongly related to localization that consists in detecting whether two images have been captured in the same or in different rooms. Next, we assess Siamese Neural Networks in the context of a global localization problem. The results outperform previous techniques for solving the localization task using the COLD-Freiburg dataset, in a variety of lighting conditions, specially when using images captured in cloudy and night conditions. [ABSTRACT FROM AUTHOR]

Aiming at the problem of poor autonomous adaptability of mobile robots to dynamic environments, this paper propose a YOLACT++ based semantic visual SLAM for autonomous adaptation to dynamic environments of mobile robots. First, a light-weight YOLACT++ is utilized to detect and segment potential dynamic objects, and Mahalanobis distance is combined to remove feature points on active dynamic objects, also, epipolar constraint and clustering are employed to eliminate feature points on passive dynamic objects. Then, in terms of the semantic labels of dynamic and static components, the global semantic map is divided into three parts for construction. The semantic overlap and uniform motion model are chose to track moving objects and the dynamic components are added to the background map. Finally, a 3D semantic octree map is constructed that is consistent with the real environment and updated in real time. A series of simulations and experiments demonstrated the feasibility and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Autonomous navigation in dynamic environments is a significant challenge in robotics. The primary goals are to ensure smooth and safe movement. This study introduces a control strategy based on an Adaptive Neuro-Fuzzy Inference System (ANFIS). It enhances autonomous robot navigation in dynamic environments with a focus on collision-free path planning. The strategy uses a path-planning technique to develop a trajectory that allows the robot to navigate smoothly while avoiding both static and dynamic obstacles. The developed control system incorporates four ANFIS controllers: two are tasked with guiding the robot toward its end point, and the other two are activated for obstacle avoidance. The experimental setup conducted in CoppeliaSim involves a mobile robot equipped with ultrasonic sensors navigating in an environment with static and dynamic obstacles. Simulation experiments are conducted to demonstrate the model's capability in ensuring collision-free navigation, employing a path-planning algorithm to ascertain the shortest route to the target destination. The simulation results highlight the superiority of the ANFIS-based approach over conventional methods, particularly in terms of computational efficiency and navigational smoothness. [ABSTRACT FROM AUTHOR]

Visual simultaneous localization and mapping (VSLAM) is pivotal for intelligent mobile robots. VSLAM systems can be used to identify scenes by obtaining massive amounts of redundant texture information from the environment. However, VSLAM faces a major challenge in dynamic low-light environments, in which the extraction of feature points is often difficult, leading to tracking failure with mobile robots. Therefore, we developed a method to improve the feature point extraction method used for VSLAM. We first used the contrast limited adaptive histogram equalization (CLAHE) method to increase the contrast in low-light images, allowing for the extraction of more feature points. Second, in order to increase the effectiveness of the extracted feature points, the redundant feature points were removed. We developed three conditions to filter the feature points. Finally, the proposed method was tested on popular datasets (e.g., TUM and OpenLORIS-Scene), and the results were compared with those of several traditional methods. The results of the experiments showed that the proposed method is feasible and highly robust in dynamic low-light environments. [ABSTRACT FROM AUTHOR]

In this study, under the limited volume of 18 cm × 18 cm × 21 cm, a small-sized mobile robot is designed and implemented. It consists of a CPU, a GPU, a 2D LiDAR (Light Detection And Ranging), and two fisheye cameras to let the robot have good computing processing and graphics processing capabilities. In addition, three functions of road detection, sign recognition, and obstacle avoidance are implemented on this small-sized robot. For road detection, we divide the captured image into four areas and use Intel NUC to perform road detection calculations. The proposed method can significantly reduce the system load and also has a high processing speed of 25 frames per second (fps). For sign recognition, we use the YOLOv4-tiny model and a data augmentation strategy to significantly improve the computing performance of this model. From the experimental results, it can be seen that the mean Average Precision (mAP) of the used model has increased by 52.14%. For obstacle avoidance, a 2D LiDAR-based method with a distance-based filtering mechanism is proposed. The distance-based filtering mechanism is proposed to filter important data points and assign appropriate weights, which can effectively reduce the computational complexity and improve the robot's response speed to avoid obstacles. Some results and actual experiments illustrate that the proposed methods for these three functions can be effectively completed in the implemented small-sized robot. [ABSTRACT FROM AUTHOR]

Traditional mobile robots have limited mobility in complex terrain environments. Generally, the closed-chain leg structure of a foot-type robot relies on the speed difference to turn, but it is difficult to complete the turning action in narrow spaces. Therefore, this study proposes a closed-chain foot-type robot that can move in multiple directions, inspired by the WATT-I leg structure. Firstly, the closed-chain single-leg structure is designed, and the leg structure is analyzed in terms of the degrees of freedom, kinematics, and singularity. A simulation is also carried out. Secondly, based on the present trajectory, a heuristic algorithm is used to solve the inverse trajectory problem, and the size of the mechanism is optimized. Finally, the steering mechanism of the leg with a zero turning radius is designed and analyzed, which achieves the steering function of the whole robot and satisfies the goal of enabling the foot robot to walk in all directions. This study provides theoretical guidance for the structural dimension optimization of the proposed foot mobile robot and its application in engineering fields such as rescue, exploration, and the military. [ABSTRACT FROM AUTHOR]

The main objective of this work is to address problems related to the speed control of mobile robots with non-holonomic constraints and differential traction—specifically, robots for football games in the VSS (Very Small Size) category. To achieve this objective, an implementation and comparison is carried out between two control strategies: an adaptive control strategy by gain scheduling and a fuzzy multimodel intelligent control strategy. The mathematical models of the wheel motors for each operating range are approximated by a first-order system since data acquisition is performed using the step response. Tuning of the proportional and integral gains of the local controllers is carried out using the root locus technique in discrete time. For each mathematical model obtained for an operating range, a local controller is tuned. Finally, with the local controllers in hand, the implementation of and comparison between the gain scheduling adaptive control strategy and the fuzzy multimodel intelligent control strategy are carried out, in which the control strategies are programmed into the low-level code of a non-holonomic robot with a differential drive to verify the performance of the speed tracking dynamics imposed on the wheel motors to improve robot navigation during a robot football match. [ABSTRACT FROM AUTHOR]

This document provides a summary of a book that explores the challenges and advancements in the guidance, navigation, and control (GNC) of autonomous and transport vehicles. It emphasizes the need for vehicles to operate autonomously and efficiently in congested city environments and discusses the development of collaborative GNC for capabilities like platooning. The document also mentions the impact of policies on GNC, such as the authorization of eVTOL and drone operations by the FAA in the United States. It highlights the growing research areas in vehicle navigation and control, including sensor fusion, trajectory optimization, and control challenges. The document concludes by encouraging readers to explore the publications in the Special Issue to gain a deeper understanding of the emerging research priorities in GNC for a diverse range of vehicles. [Extracted from the article]

Object tracking is one of the major tasks for mobile robots in many real-world applications. Also, artificial intelligence and automatic control techniques play an important role in enhancing the performance of mobile robot navigation. In contrast to previous simulation studies, this paper presents a new intelligent mobile robot for accomplishing multi-tasks by tracking red-green-blue (RGB) colored objects in a real experimental field. Moreover, a practical smart controller is developed based on adaptive fuzzy logic and custom proportional-integral-derivative (PID) schemes to achieve accurate tracking results, considering robot command delay and tolerance errors. The design of developed controllers implies some motion rules to mimic the knowledge of experienced operators. Twelve scenarios of three colored object combinations have been successfully tested and evaluated by using the developed controlled image-based robot tracker. Classical PID control failed to handle some tracking scenarios in this study. The proposed adaptive fuzzy PID control achieved the best accurate results with the minimum average final error of 13.8 cm to reach the colored targets, while our designed custom PID control is efficient in saving both average time and traveling distance of 6.6 s and 14.3 cm, respectively. These promising results demonstrate the feasibility of applying our developed image-based robotic system in a colored object-tracking environment to reduce human workloads. [ABSTRACT FROM AUTHOR]

The paper is concerned with the region tracking control problem of multiple nonholonomic mobile robot systems with collision avoidance. The peculiar characteristic of the proposed scheme is that a class of potential energy functions is chosen to form the desired target region constraining the robots for aggregation, and some local potential energy functions are selected to maintain collision avoidance among robots. Such that each mobile robot only needs to interact with their neighbors to realize the region tracking control task. Moreover, this paper provides a new analytical framework of region tracking control for a network of wheeled mobile robots, in which the integration of the system decomposition and sliding mode control method enables to convert the nonholonomic constraint problem of the controlled mobile robot system into a holonomic constraint problem. This provides a new approach to solving the mobile robot control problem. Theoretical analysis and numerical simulation results are given to validate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

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To provide the necessary theoretical models of sphere–soil interaction for the structural design, motion control, and simulation of spherical robots, this paper derives analytical expressions for traction force and driving torque when spherical robots slide and sink into sandy terrain, based on terramechanics and multibody dynamics. Furthermore, orthogonal experimental analysis identifies the load, joint angular acceleration, and maximum joint angular velocity of spherical robots as influencing factors, highlighting that the load significantly affects their longitudinal motion performance. Experimental results indicate that rolling friction and additional resistance on sandy terrain cannot be ignored. The corrected theoretical model effectively replicates the temporal variation of driving torque exerted by spherical robots on sandy terrain. Numerical computations and experimental analyses demonstrate that increasing the radius of the sphere shell, the load, and the slip ratio all lead to increased traction force and driving torque. However, traction force and driving torque begin to decrease once the slip ratio reaches approximately 0.5. Therefore, in the design of spherical robot structures and control laws, appropriate parameters such as load and slip ratio should be chosen based on the established sphere–soil interaction theoretical model to achieve high-quality longitudinal motion performance on sandy terrain. [ABSTRACT FROM AUTHOR]

This paper deals with the synthesis and implementation of a controller for asymptotic tracking of the desired trajectory of a mobile robot. The mobile robot used for the experimental validation has eight motors with an inner control loop. Four steering actuators are controlled using position controllers and four driving actuators are controlled using velocity controllers. A complex robot kinematic model is converted into a control-oriented linear time-varying system, which is then used to design a time-varying control law that minimizes the quadratic optimality criterion. In contrast to conventional methodologies for solving the corresponding Riccati differential equations, a computational approach that explicitly determines the time-varying controller matrix by employing recurrent matrix computations is proposed. Mobile robot control inputs (linear velocity, steering angles and steering velocities) are forwarded to the steering and driving actuators with properly tuned position and velocity controllers using an inverse kinematic model of the mobile robot. The obtained control law is evaluated on an experimental set-up of a real mobile robot system. The controller is implemented using the Robot Operating System. [ABSTRACT FROM AUTHOR]

Effective path planning is crucial for autonomous mobile robots navigating complex environments. The "global–local" coupled path planning algorithm exhibits superior global planning capabilities and local adaptability. However, these algorithms often fail to fully realize their potential due to low efficiency and excessive constraints. To address these issues, this study introduces a simpler and more effective integration strategy. Specifically, this paper proposes using a bi-layer map and a feasible domain strategy to organically combine the D*Lite algorithm with the Dynamic Window Approach (DWA). The bi-layer map effectively reduces the number of nodes in global planning, enhancing the efficiency of the D*Lite algorithm. The feasible domain strategy decreases constraints, allowing the local algorithm DWA to utilize its local planning capabilities fully. Moreover, the cost functions of both the D*Lite algorithm and DWA have been refined, enabling the fused algorithm to cope with more complex environments. This paper conducts simulation experiments across various settings and compares our method with A_DWA, another "global–local" coupled approach, which combines A* and DWA. D_DWA significantly outperforms A_DWA in complex environments, despite a 7.43% increase in path length. It reduces the traversal of risk areas by 71.95%, accumulative risk by 80.34%, global planning time by 26.98%, and time cost by 35.61%. Additionally, D_DWA outperforms the A_Q algorithm, a coupled approach validated in real-world environments, which combines A* and Q-learning, achieving reductions of 1.34% in path length, 67.14% in traversal risk area, 78.70% in cumulative risk, 34.85% in global planning time, and 37.63% in total time cost. The results demonstrate the superiority of our proposed algorithm in complex scenarios. [ABSTRACT FROM AUTHOR]

Recent dramatic increase in e-commerce has also increased the adoption of automation technologies in warehouses. Autonomous mobile robots (AMRs) are from those technologies widely utilized in warehouse operations. It is important to design the operation of those robotic systems in such a way that, they meet the current and future system requirements correctly. In this paper, we study flexible travel of AMRs in warehouses by developing smart deadlock and collision prevention algorithms on agent-based modelling. By that, AMR agents can interact with each other and environment, so that they can make smart decisions maximizing their goals. We compare the performance of the developed flexible travel system with non-flexible designs where there is a single AMR dedicated to a specific zone so that no deadlock or collision possibility takes place. The results show that AMRs may provide up to 39% improvement in the flexible system compared to its non-flexible design. [ABSTRACT FROM AUTHOR]

This paper introduces an advanced formation control algorithm based on a Lyapunov approach for coordinating multiple omnidirectional mobile robots in collaborative object transport tasks. The semi-decentralized strategy ensures that the robots maintain a predefined geometric formation, crucial for stability during material transportation, and dynamically adapt to avoid collisions using onboard sensors. Experimental with a physical robot simulator demonstrates successful maintenance of line and triangle formations achieving an average side length maintenance of 1.00 meters with minimal deviation. Quantitative analysis across 30 experimental runs reveals consistent performance, with a maximum side length fluctuation of only 2 centimeters, validating the effectiveness of maintaining formation within a multi-robot system (MRS) framework. The Lyapunov-based approach proves to be an efficient method for cooperative object transport, achieving consistent performance with minimal deviation. [ABSTRACT FROM AUTHOR]

Obstacle avoidance is essential for the effective operation of autonomous mobile robots, enabling them to detect and navigate around obstacles in their environment. While deep learning provides significant benefits for autonomous navigation, it typically requires large, accurately labeled datasets, making the data's preparation and processing time-consuming and labor-intensive. To address this challenge, this study introduces a transfer learning (TL)-based automatic labeling segmentation (ALS) framework. This framework utilizes a pretrained attention-based network, DifferNet, to efficiently perform semantic segmentation tasks on new, unlabeled datasets. DifferNet leverages prior knowledge from the Cityscapes dataset to identify high-entropy areas as road obstacles by analyzing differences between the input and resynthesized images. The resulting road anomaly map was refined using depth information to produce a robust drivable area and map of road anomalies. Several off-the-shelf RGB-D semantic segmentation neural networks were trained using pseudo-labels generated by the ALS framework, with validation conducted on the GMRPD dataset. Experimental results demonstrated that the proposed ALS framework achieved mean precision, mean recall, and mean intersection over union (IoU) rates of 80.31%, 84.42%, and 71.99%, respectively. The ALS framework, through the use of transfer learning and the DifferNet network, offers an efficient solution for semantic segmentation of new, unlabeled datasets, underscoring its potential for improving obstacle avoidance in autonomous mobile robots. [ABSTRACT FROM AUTHOR]

To address the issue of error-prone and unstable trajectory tracking and dynamic obstacle avoidance of mobile robots in locally observable nonlinear dynamic settings, a deep reinforcement learning (RL)-based visual perception, and decision-making system is proposed. The technique creates a closed loop between the system's environmental perception and decision-making capabilities by combining the perceptual capabilities of convolutional neural networks with the decision-making capabilities of RL in a generic form. It achieves direct output control from the visual perception input of the environment to the action through end-to-end learning. The simulation results show that this approach is capable of meeting the demands of multi-task intelligent perception and decision making. It also more effectively addresses issues with traditional algorithms, including their tendency to fall into local optimums, oscillate in groups of similar obstacles without recognizing the path, oscillate in tight spaces and inaccessible targets close to obstacles and significantly enhance real-time and adaptability of robot trajectory tracking and dynamic obstacle avoidance. [ABSTRACT FROM AUTHOR]

In the non-deterministic, large-scale navigation environment under the Mars exploration mission, there is a large space for action and many environmental states. Traditional reinforcement learning algorithms that can only obtain rewards at target points and obstacles will encounter the problems of reward sparsity and dimension explosion, making the training speed too slow or even impossible. This work proposes a deep layered learning algorithm based on the goal-driven layered deep Q-network (GDH-DQN), which is more suitable for mobile robots to explore, navigate, and avoid obstacles without a map. The algorithm model is designed in two layers. The lower layer provides behavioral strategies to achieve short-term goals, and the upper layer provides selection strategies for multiple short-term goals. Use known position nodes as short-term goals to guide the mobile robot forward and achieve long-term obstacle avoidance goals. Hierarchical execution not only simplifies tasks but also effectively solves the problems of reward sparsity and dimensionality explosion. In addition, each layer of the algorithm integrates a Hindsight Experience Replay mechanism to improve performance, make full use of the goal-driven function of the node, and effectively avoid the possibility of misleading the agent by complex processes and reward function design blind spots. The agent adjusts the number of model layers according to the number of short-term goals, further improving the efficiency and adaptability of the algorithm. Experimental results show that, compared with the hierarchical DQN method, the navigation success rate of the GDH-DQN algorithm is significantly improved, and it is more suitable for unknown scenarios such as Mars exploration. [ABSTRACT FROM AUTHOR]

This article presents a combined route and path planning strategy to guide Skid–Steer Mobile Robots (SSMRs) in scheduled harvest tasks within expansive crop rows with complex terrain conditions. The proposed strategy integrates: (i) a global planning algorithm based on the Traveling Salesman Problem under the Capacitated Vehicle Routing approach and Optimization Routing (OR-tools from Google) to prioritize harvesting positions by minimum path length, unexplored harvest points, and vehicle payload capacity; and (ii) a local planning strategy using Informed Rapidly-exploring Random Tree ( IRRT * ) to coordinate scheduled harvesting points while avoiding low-traction terrain obstacles. The global approach generates an ordered queue of harvesting locations, maximizing the crop yield in a workspace map. In the second stage, the IRRT * planner avoids potential obstacles, including farm layout and slippery terrain. The path planning scheme incorporates a traversability model and a motion model of SSMRs to meet kinematic constraints. Experimental results in a generic fruit orchard demonstrate the effectiveness of the proposed strategy. In particular, the IRRT * algorithm outperformed RRT and RRT * with 96.1% and 97.6% smoother paths, respectively. The IRRT * also showed improved navigation efficiency, avoiding obstacles and slippage zones, making it suitable for precision agriculture. [ABSTRACT FROM AUTHOR]

This research addresses the challenges of using the Rapidly Exploring Random Tree (RRT) algorithm as a mobile robot path planner. While RRT is known for its flexibility and wide applicability, it has limitations, including careful tuning, susceptibility to local minima, and generating jagged paths. The main objective is to improve the smoothness of RRT-generated trajectories and reduce significant path curvature. A novel approach is proposed to achieve these, integrating the RRT path planner with a modified version of the Whale Optimization Algorithm (RRT-WOA). The modified WOA algorithm incorporates parameter variation (C) specifically designed to optimize trajectory smoothness. Additionally, Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) instead of conventional splines for point interpolation further smoothes the generated paths. The modified WOA algorithm is thoroughly evaluated through a comprehensive comparative analysis, outperforming other popular population-based optimization algorithms such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Firefly Algorithm (FA) in terms of optimization time, trajectory smoothness, and improvement from the initial guess. This research contributes a refined trajectory planning approach and highlights the competitive advantage of the modified WOA algorithm in achieving smoother and more efficient trajectories compared to existing methods. [ABSTRACT FROM AUTHOR]

The nuclear energy sector can benefit from mobile robots for remote inspection and handling, reducing human exposure to radiation. Advances in cyber–physical systems have improved robotic platforms in this sector through digital twin (DT) technology. DTs enhance situational awareness for robot operators, crucial for safety in the nuclear energy sector, and their value is anticipated to increase with the growing complexity of cyber–physical systems. The primary motivation of this work is to rapidly develop and evaluate a robot fleet interface that accounts for these benefits in the context of nuclear environments. Here, we introduce a multimodal immersive DT platform for cyber–physical robot fleets based on the ROS‐Unity 3D framework. The system design enables fleet monitoring and management by integrating building information models, mission parameters, robot sensor data, and multimodal user interaction through traditional and virtual reality interfaces. A modified heuristic evaluation approach, which accounts for the positive and negative aspects of the interface, was introduced to accelerate the iterative design process of our DT platform. Robot operators from leading nuclear research institutions (Sellafield Ltd. and the Japan Atomic Energy Agency) performed a simulated robot inspection mission while providing valuable insights into the design elements of the cyber–physical system. The three usability themes that emerged and inspired our design recommendations for future developers include increasing the interface's flexibility, considering each robot's individuality, and adapting the platform to expand sensor visualization capabilities. [ABSTRACT FROM AUTHOR]

Accurate location information is crucial for ensuring the reliability of service robots, such as field vehicles and agricultural robots. These practical robots often encounter fluctuating or inclined terrains in dynamic conditions during operation. Additionally, the performance of mobile robots depends on the physical size and weight of the localization system–physically heavy systems may degrade performance. In this paper, we present a compact, lightweight RTK‐GNSS device weighing under 40 g, which achieves centimeter‐level accuracy across different attitudes and dynamic situations. To enhance signal reception and positioning performance, we lessen noise levels in the power circuit and radio‐frequency signal conditioning using a low‐dropout regulator and low‐noise amplifier. Utilizing the configured device, we verified centimeter‐level accuracy through experiments of tracking an unmanned ground vehicle. The potential of the device is substantiated via three application scenarios—a localization of a moving human for a wearable device, a heading estimation of a mobile robot in stationary conditions, and practical field tests in various signal‐reception environments. Attributable to its compact dimensions and lightweight features, the proposed device can yield a low system strain while maintaining high accuracy and reliable performance, rendering it highly adaptable for various robotic applications. [ABSTRACT FROM AUTHOR]

The stability of the robot's performance is very important, especially for the wheeled mobile robots that use swerve drives, which need kinematic control to reach the destination point. The study of robot movement known as kinematics is based on an examination of the geometric structure of the robot, with no consideration given to the mass, force, or acceleration that the robot experiences during movement. This study aims to model and simulate the kinematic control design of a wheeled robot that uses a swerve drive. This robot uses BLDC motor actuator so that the robot can reach its destination very quickly and steadily. The test is carried out by simulating and comparing the performance response using BLDC motors and DC motors. According to the testing and trials, the robot can reach its destination by modeling its kinematic control, and BLDC motors are found to be more reliable and efficient for driving and steering than DC motors. [ABSTRACT FROM AUTHOR]

Home Security Systems (HSSs) have received much attention and have been widely adopted for practical deployment. However, detection and warning accuracy still need to be improved, along with the range of the realm, making it challenging to satisfy the user demands. This study proposes a security monitoring system based on mobile robots and the Internet of Things (IoT), allowing users to monitor and control devices remotely. The mobile robot integrated sensor systems and surveillance cameras are utilized for unauthorized early intrusion detection and to give users instant warnings. The data collected by the robot were stored on the Firebase Cloud server, and a mobile application and a Telegram interface were integrated to manage and control the system. In addition, adaptive motion control was adopted to correct errors in the robot’s trajectory. The implementation results proved that this system operated effectively with a minimal response delay of 0.87–1.67 s and a high detection accuracy (96.25%) in two experimental cases, which makes it suitable for real-time applications. [ABSTRACT FROM AUTHOR]

This article deals with ensuring and increasing the safety of mobile robotic systems in human-machine collaboration. The goal of the research was to design and implement an artificial intelligence application that recognizes obstacles, including humans, and increases safety. The resulting mobile Android application uses a MiDaS model to generate a depth map of the environment from the drone’s camera to approximate the distance from all obstacles to avoid the drone’s collision. Besides, this work introduced us to DJI Mobile SDK and neural network optimizations for their use on smartphones.

Abstract 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.