Mobile Robot System Research Articles

Design and Experiment of Greenhouse Self-Balancing Mobile Robot Based on PR Joint Sensor

To avoid issues such as the greenhouse working robot’s inability to perform normal tasks or reduced working accuracy due to the influence of uneven ground, this study designed a set of greenhouse self-balancing mobile robots. The self-balancing mobile robot system designed in this study uses a quadruped mobile robot as a carrier, equipped with a three-degrees-of-freedom wheel-leg structure and is complemented with a posture control algorithm. The algorithm calculates the adjustment of each leg based on the vehicle’s tilt angle and wheel-ground pressure, achieving control over the robot’s posture angle, the center of gravity height, wheel-ground contact force, and other functions. To address the issue of over-constrained (weak legs) posture adjustment during mobile robot fieldwork, a flexible joint sensor based on the PR structure has been designed and developed. After field testing, it was verified that the greenhouse self-balancing mobile robot proposed in this study can adapt well to field environments, such as climbing hills, overcoming obstacles, crossing furrows, and so on. The response speed of the flexible joint sensor can meet the requirements of self-balancing while effectively solving the problem of weak legs.

Multi-level control for multiple mobile robot systems

This paper contributes with a multi-level, hierarchical control system for a fleet of mobile robots sharing a common 2D motion space. The system consists of three levels, with the top level being a supervisor based on a discrete representation of the Multiple Mobile Robot System (MMRS), in which robot motion processes are seen as sequences of stages. The supervisor controls centrally the changes of their stages by robots, ensuring their collision-, and deadlock-free concurrent movement. The intermediate control level supervises locally the execution of robot motion on individual stages in a manner consistent with the decisions of the top level. The lowest level, robot control, is responsible for motion execution as determined by the local supervisor. We capitalize on some earlier results concerning the supervisory control of MMRS and propose a common framework for three supervisory control models. Then we propose relevant solutions for the local supervisors, in particular, a DES-based robot-motion-mode control and application of the Artificial Potential Field model for ensuring collision-free motion of two robots sharing a space sector. Next we assume simple robot control and subject the system to simulation experiments aimed at comparing the impact of the different solutions on the performance of MMRS.

Design and implementation of resource‐aware robust event‐based integral sliding mode controller for a networked wheeled mobile robot

AbstractThis work discusses the design and implementation of a robust control strategy based on integral sliding mode (ISM) for tracking a reference trajectory by a nonholonomic wheeled mobile robot (WMR). A state feedback control law using dynamic feedback linearization is adopted to design the controller for the unperturbed dynamics of the WMR. To nullify the effects of perturbations in the WMR and to retain the performance of the nominal controller, the ISM control scheme is introduced, which uses discontinuous control to guarantee the robustness of the closed‐loop system. To relax the frequent use of the communication network due to the continuous exchange of the sensor measurements, the control law is redesigned using event‐based sampled data information. The control signal is updated in an aperiodic manner by designing a novel triggering rule where the triggering occurs only when either of the two sampling errors violates a predefined threshold condition. This design approach gives a balanced solution for a networked WMR regarding network usage and control performance. Finally, a simulation study with comparative analysis and hardware‐in‐loop testing of the proposed controller is performed on a mobile robot system to establish the feasibility of the theoretical results.

A lightweight encoder–decoder network for automatic pavement crack detection

AbstractCracks are the most common damage type on the pavement surface. Usually, pavement cracks, especially small cracks, are difficult to be accurately identified due to background interference. Accurate and fast automatic road crack detection play a vital role in assessing pavement conditions. Thus, this paper proposes an efficient lightweight encoder–decoder network for automatically detecting pavement cracks at the pixel level. Taking advantage of a novel encoder–decoder architecture integrating a new type of hybrid attention blocks and residual blocks (RBs), the proposed network can achieve an extremely lightweight model with more accurate detection of pavement crack pixels. An image dataset consisting of 789 images of pavement cracks acquired by a self‐designed mobile robot is built and utilized to train and evaluate the proposed network. Comprehensive experiments demonstrate that the proposed network performs better than the state‐of‐the‐art methods on the self‐built dataset as well as three other public datasets (CamCrack789, Crack500, CFD, and DeepCrack237), achieving F1 scores of 94.94%, 82.95%, 95.74%, and 92.51%, respectively. Additionally, ablation studies validate the effectiveness of integrating the RBs and the proposed hybrid attention mechanisms. By introducing depth‐wise separable convolutions, an even more lightweight version of the proposed network is created, which has a comparable performance and achieves the fastest inference speed with a model parameter size of only 0.57 M. The developed mobile robot system can effectively detect pavement cracks in real scenarios at a speed of 25 frames per second.

Modeling and Analysis of Three Properties of Mobile Interactive Systems Based on Variable Petri Nets

Due to the mobility and frequent disconnections, the correctness of mobile interaction systems, such as mobile robot systems and mobile payment systems, are often difficult to analyze. This paper introduces three critical properties of systems, called system connectivity, interaction soundness and data validity, and presents a related modeling and analysis method, based on a kind of Petri nets called VPN. For a given system, a model including component nets and interaction structure nets is constructed by using VPNs. The component net describes the internal process of each component, while the interaction structure net reflects the dynamic interaction between components. Based on this model, three properties are defined and analyzed. The case study of a practical mobile payment system shows the effectiveness of the proposed method.

A Study of the Global Topological Map Construction Algorithm Based on Grid Map Representation for Multirobot

In some large environments where humans and machines coexist, such as smart factories, restaurants, and hotels, multiple mobile robots system have certain advantages over single mobile robot in terms of task complexity, execution efficiency, and system robustness. However, path conflicts and blockages may occur among robots without effective global coordination method, especially in some special areas like narrow straight roads (N-S-R) and crossroads (C-R). To solve this problem, using the global topology map might be a good solution. Therefore, this paper proposes a method called Surplus Topology Generation Algorithm (<i>STGA</i>) for constructing a global topological map based on grid map representation. On the basis of grid maps, we designed <i>Node-Filter</i> to generate enough topological nodes, and simplified the node set by <i>Node-Selection</i>. In special areas such as N-S-R and C-R, which are prone to path conflicts and blocking scenarios, we extracted the Harris corner point and combined them to select the corner point combinations that characterize some special features like passage entrance, etc. Then, feature nodes that can characterize the special features were obtained by establishing mapping relationships between nodes and corner point combinations. Finally, the global topology map was obtained after establishing the reachability relationships between nodes. The simulation experiment results showed that the topological maps generated by <i>STGA</i> had obvious advantages in the richness of topological relationships. Besides, the topological maps also showed certain advantages compared to grid maps for the planning efficiency and security of global paths. Furthermore, the rich topological nodes on the global topological map were feasible in solving path conflicts of multiple robots. <i>Note to Practitioners</i>&#x2014;Multi-robot system has better efficiency and system robustness, but how to solve the path conflicts among robots is a problem. We propose a method to solve multi-robot conflicts with global topological maps. This topological map is constructed by STGA. The grid map environment is discretized into a set of topological nodes, which have been used to maintain the spatial transitions of each robot in the environment. When robots encounter unavoidable path conflicts, they are scheduled with the help of topological nodes to achieve orderly and efficient collaboration.

Review of Research Status of Autonomous Mobile Robot Environment Recognition and Path Planning Algorithms

The convergence of Industry 4.0 and the challenges posed by the COVID-19 pandemic amplify the growth prospects and market reach of autonomous mobile robots. This paper elucidates the current research landscape and forward-looking trajectory concerning perception systems for autonomous mobile robots. It advocates a future-oriented development path grounded in the utilization of vision sensors and multi-sensor configurations to enhance environmental recognition. Addressing the imperative of bolstering environmental discernment in intricate settings remains a priority. An emerging area of interest pertains to terrain prediction algorithms. The ensuing section deliberates upon the merits and demerits intrinsic to distinct robot path planning algorithms. These algorithms can be categorized into global path planning algorithms, entrusted with charting optimal overarching courses, and local path planning algorithms, designed to navigate impediments and effect localized route refinements. Envisioning the future, the maturation of robot path planning algorithms is poised to embrace multi-algorithm collaborative applications.

Real-Time Multi-Agent Mobile Robot based on A Client-Server Model

A client-server network is one of the most important topics in a computer network. In this work, a real-time computer control system is designed based on the Client-Server Model for a Multi-Agent Mobile Robot System (CSM-MAMRS) and is applied to a building that consists of (N) floors and uses one mobile robot on each floor that has specific actions in different types of environments. The new proposed CSM-MAMRS consists of four layers. The first stage manages the network communication for each agent. The second stage solves the major problems of path planning by using a proposed hybrid algorithm that combines the Rapidly Exploring Random Tree Star (RRT*) and the Particle Swarm Optimization (PSO) algorithms in order to provide the shortest and smoothest path with collision avoidance between the starting and the target points in a static and dynamic robot environments are used.. In the third stage, a velocity planner controller is based on an inverse kinematic mobile robot model. In the fourth stage, the Hypertext Transfer Protocol HTTP is used to send velocity values to real mobile robots via the Wireless Network Control Administration. The simulation results and experimental works successfully achieved to significant improvement in real-time when using three missions of three mobile robots on different static map floors in the building. The maximum tracking pose errors for three robots in the static enviormnets are 0.39 cm, 0.02 cm, 0.32 cm respectively, but the maximum tracking pose error in the dynamic environments are 2.94 cm and 2.8 cm only for two mobile robots along the maximum distance of 250 cm. Index Terms— Real-time computer control, Mobile robot, Multi-agent system, Path planning algorithms.

Vision- and Lidar-Based Autonomous Docking and Recharging of a Mobile Robot for Machine Tending in Autonomous Manufacturing Environments

Autonomous docking and recharging are among the critical tasks for autonomous mobile robots that work continuously in manufacturing environments. This requires robots to demonstrate the following abilities: (i) detecting the charging station, typically in an unstructured environment and (ii) autonomously docking to the charging station. However, the existing research, such as that on infrared range (IR) sensor-based, vision-based, and laser-based methods, identifies many difficulties and challenges, including lighting conditions, severe weather, and the need for time-consuming computation. With the development of deep learning techniques, real-time object detection methods have been widely applied in the manufacturing field for the recognition and localization of target objects. Nevertheless, those methods require a large amount of proper and high-quality data to achieve a good performance. In this study, a Hikvision camera was used to collect data from a charging station in a manufacturing environment; then, a dataset for the wireless charger was built. In addition, the authors of this paper propose an autonomous docking and recharging method based on the deep learning model and the Lidar sensor for a mobile robot operating in a manufacturing environment. In the proposed method, a YOLOv7-based object detection method was developed, trained, and evaluated to enable the robot to quickly and accurately recognize the charging station. Mobile robots can achieve autonomous docking to the charging station using the proposed Lidar-based approach. Compared to other methods, the proposed method has the potential to improve recognition accuracy and efficiency and reduce the computation costs for the mobile robot system in various manufacturing environments. The developed method was tested in real-world scenarios and achieved an average accuracy of 95% in recognizing the target charging station. This vision-based charger detection method, if fused with the proposed Lidar-based docking method, can improve the overall accuracy of the docking alignment process.

Approach toward the application of mobile robots in laser materials processing

Laser-based production systems have become more and more popular in recent years due to their potential to achieve high precision and accuracy in a wide range of different applications. However, the kinematic systems used for laser materials processing (LMP) are often inherited from other production technologies such as milling. The use of mobile robots (MRs) equipped with laser processing optics could disprove the current paradigm of adapted kinematic systems: scaling the size of the material processing system with the size of the components being processed and, thus, the resources used. The trend of autonomous MRs replacing classical kinematic systems in the field of material handling in industrial applications has been evident for years due to their higher flexibility, efficiency, and lower operating costs. In this paper, the prototype of a corresponding MR system is presented. In addition, the general design of the MR is presented. One challenge is the accuracy of an MR; for a common LMP such as laser cutting, the MR must be able to follow a predefined trajectory as accurately as possible. For this purpose, two different measurement systems are presented and compared. To demonstrate the potential of the mobile robot, an exemplary LMP process is also performed and evaluated. Finally, possibilities for improvement or further development, such as integration of scanner optics or the use of several autonomous MRs to increase productivity, are shown.

Lattice piecewise affine approximation of explicit nonlinear model predictive control with application to trajectory tracking of mobile robot

AbstractTo promote the widespread use of mobile robots in diverse fields, the performance of trajectory tracking must be ensured. To address the constraints and nonlinear features associated with mobile robot systems, nonlinear model predictive control (MPC) is applied to realize trajectory tracking of mobile robots. Specifically, to alleviate the online computational complexity of nonlinear MPC, this paper devises a lattice piecewise affine (PWA) approximation method that can approximate both the nonlinear system and control law of explicit nonlinear MPC. The kinematic model of the mobile robot is successively linearized along the trajectory to obtain a linear time‐varying description of the system, which is then expressed using a lattice PWA model. Subsequently, the nonlinear MPC problem can be transformed into a series of linear MPC problems. Furthermore, to reduce the complexity of online calculation of multiple linear MPC problems, the optimal solution of the linear MPCs is approximated by using the lattice PWA models. That is, for different sampling states, the optimal control inputs are obtained offline, and lattice PWA approximations are constructed for the state control pairs. Simulations are performed to evaluate the performance of the method in comparison with the state‐of‐the‐art methods, and the results show that the method has a higher online computing speed and can decrease the offline computing time without significantly increasing the tracking error.

Reaching the limit in autonomous racing: Optimal control versus reinforcement learning.

A central question in robotics is how to design a control system for an agile mobile robot. This paper studies this question systematically, focusing on a challenging setting: autonomous drone racing. We show that a neural network controller trained with reinforcement learning (RL) outperformed optimal control (OC) methods in this setting. We then investigated which fundamental factors have contributed to the success of RL or have limited OC. Our study indicates that the fundamental advantage of RL over OC is not that it optimizes its objective better but that it optimizes a better objective. OC decomposes the problem into planning and control with an explicit intermediate representation, such as a trajectory, that serves as an interface. This decomposition limits the range of behaviors that can be expressed by the controller, leading to inferior control performance when facing unmodeled effects. In contrast, RL can directly optimize a task-level objective and can leverage domain randomization to cope with model uncertainty, allowing the discovery of more robust control responses. Our findings allowed us to push an agile drone to its maximum performance, achieving a peak acceleration greater than 12 times the gravitational acceleration and a peak velocity of 108 kilometers per hour. Our policy achieved superhuman control within minutes of training on a standard workstation. This work presents a milestone in agile robotics and sheds light on the role of RL and OC in robot control.

Review of Hydro-Pneumatic Accumulator Models for the Study of the Energy Efficiency of Hydraulic Systems

This review article deals with hydro-pneumatic accumulators (HPAs) charged with nitrogen. The focus is on HPA models used in the study of the energy efficiency of hydraulic systems. Hydraulic circuits with HPA are presented along with their various applications for delivering the required volume of fluid, maintaining the required pressure, ensuring safe operation, safety stop, leak compensation, fluid volume change compensation, pulsation damping, and pressure shock absorption. A general regenerative hydraulic system and a general hybrid hydraulic system are also presented. The review focuses primarily on HPA computational, dynamic, and simulation models. Basic HPA calculation parameters and computational models of energy storage and thermodynamic cycle are presented. Various computational and dynamic models of HPA have been defined, such as the thermodynamic model, simulation model, dynamic model, pulsation damper model, and shock pulse damper model. Research projects that have used HPA in industrial hydraulic systems are reviewed, such as those maintaining operating pressure in an industrial 80 MN open-die hydraulic forging press and acting as a shock pulse absorber in the lifting and levelling module of a tracked mobile robotic bricklaying system. The development of energy storage technology in HPA from various sources is now a global challenge.

Wireless Power Transfer System for Mobile Robots with Impedance Matching by Adjusting Position of Driver Coil

Wireless power transfer based on magnetic resonant coupling can be used to supply power to a mobile robot within a few meters of a transmitter coil. However, when the robot moves or its power consumption fluctuates, its input impedance varies significantly and, in case of impedance mismatch, power reflection may occur. To address this issue, we employed a driver coil on the transmitter side to match input impedance. By regulating the coupling coefficient between the driver and the transmitter coils, the input impedance is matched and power reflection is eliminated. Input impedance regulation is achieved by moving the driver coil mechanically. As confirmed by our experiments, the transmitting efficiency matched with the analytically calculated value, and the input impedance was matched for a vast range of distances from 0 to 1 m and load resistances from 4 to 210 Ω by moving the driver coil properly. Therefore, the proposed system successfully addresses the power reflection problem in wireless power transfer for mobile robots.

Challenges and Solutions for Autonomous Ground Robot Scene Understanding and Navigation in Unstructured Outdoor Environments: A Review

The capabilities of autonomous mobile robotic systems have been steadily improving due to recent advancements in computer science, engineering, and related disciplines such as cognitive science. In controlled environments, robots have achieved relatively high levels of autonomy. In more unstructured environments, however, the development of fully autonomous mobile robots remains challenging due to the complexity of understanding these environments. Many autonomous mobile robots use classical, learning-based or hybrid approaches for navigation. More recent learning-based methods may replace the complete navigation pipeline or selected stages of the classical approach. For effective deployment, autonomous robots must understand their external environments at a sophisticated level according to their intended applications. Therefore, in addition to robot perception, scene analysis and higher-level scene understanding (e.g., traversable/non-traversable, rough or smooth terrain, etc.) are required for autonomous robot navigation in unstructured outdoor environments. This paper provides a comprehensive review and critical analysis of these methods in the context of their applications to the problems of robot perception and scene understanding in unstructured environments and the related problems of localisation, environment mapping and path planning. State-of-the-art sensor fusion methods and multimodal scene understanding approaches are also discussed and evaluated within this context. The paper concludes with an in-depth discussion regarding the current state of the autonomous ground robot navigation challenge in unstructured outdoor environments and the most promising future research directions to overcome these challenges.

Mobile robotics in smart farming: current trends and applications.

In recent years, precision agriculture and smart farming have been deployed by leaps and bounds as arable land has become increasingly scarce. According to the Food and Agriculture Organization (FAO), by the year 2050, farming in the world should grow by about one-third above current levels. Therefore, farmers have intensively used fertilizers to promote crop growth and yields, which has adversely affected the nutritional improvement of foodstuffs. To address challenges related to productivity, environmental impact, food safety, crop losses, and sustainability, mobile robots in agriculture have proliferated, integrating mainly path planning and crop information gathering processes. Current agricultural robotic systems are large in size and cost because they use a computer as a server and mobile robots as clients. This article reviews the use of mobile robotics in farming to reduce costs, reduce environmental impact, and optimize harvests. The current status of mobile robotics, the technologies employed, the algorithms applied, and the relevant results obtained in smart farming are established. Finally, challenges to be faced in new smart farming techniques are also presented: environmental conditions, implementation costs, technical requirements, process automation, connectivity, and processing potential. As part of the contributions of this article, it was possible to conclude that the leading technologies for the implementation of smart farming are as follows: the Internet of Things (IoT), mobile robotics, artificial intelligence, artificial vision, multi-objective control, and big data. One technological solution that could be implemented is developing a fully autonomous, low-cost agricultural mobile robotic system that does not depend on a server.

A mobile robot driven by uniaxial wave locomotion mechanism

This paper presents a novel mobile robot system that uses a uniaxial wave locomotion mechanism. The proposed mechanism can mechanically produce uniaxial surface waves by a single actuator to rotate a helix component that has discrete helical wings. This involves multiple pins with springs and a link mechanism. We designed and developed a mobile robot consisting of a main body and two helical wave locomotion units arranged in parallel. This paper also presents a locomotion model based on the proposed mechanics. The developed robot prototype demonstrated mobility on various surfaces and its skid-steering performance was evaluated. The experimental results validated the feasibility of the proposed wave-locomotion mechanism.

Fast Lidar Inertial Odometry and Mapping for Mobile Robot SE(2) Navigation

This paper presents a fast Lidar inertial odometry and mapping (F-LIOM) method for mobile robot navigation on flat terrain with high real-time pose estimation, map building, and place recognition. Existing works on Lidar inertial odometry have mostly parameterized the keyframe pose as SE(3) even when the robots moved on flat ground, which complicated the motion model and was not conducive to real-time non-linear optimization. In this paper, F-LIOM is shown to be cost-effective in terms of model complexity and computation efficiency for robot SE(2) navigation, as the motions in other degrees of freedom in 3D, including roll, pitch, and z, are considered to be noise terms that corrupt the pose estimation. For front-end place recognition, the smoothness information of the feature point cloud is introduced to construct a novel global descriptor that integrates geometry and environmental texture characteristics. Experiments under challenging scenarios, including self-collected datasets and public datasets, were conducted to validate the proposed method. The experimental results demonstrated that F-LIOM could achieve competitive real-time performance in terms of accuracy compared with state-of-the-art counterparts. Our solution has significant superiority and the potential to be deployed in limited-resource mobile robot systems.

An Ensemble Filter for Indoor Positioning Technology of Mobile Home Service with Agile iBeacon Deployment

In this article, we undertake a thorough investigation into the theory of indoor location, adopt an effective and quick positioning algorithm, and make use of a network of lowpower iBeacons. An iBeacon-based indoor positioning system (IPS) is presented to investigate how to utilize iBeacon for accurate location and whether it can successfully replace the current dominant positioning technology based on the analysis that was conducted. However, the first things that should be taken into account at this point in the design of house robots for indoor environments are how to quickly and precisely gather target node location information as well as how to regulate and plan a course. This article examined the more popular and often used indoor positioning techniques, provided a succinct summary of current indoor positioning technologies and regulators, and examined ultrasonic locating technology in depth. Based on this, a system for mobile home service robots was developed, and simulation tests were performed to assess the accuracy of node locating, node reception and arrival times, the best level of route planning, and navigation and path estimation errors in both absolute and comparative terms. Additionally, we go into detail about the difficulties in developing a practical IPS, the solutions that are already available, a thorough performance comparison, and some potential future IPS development trends.

Neural‐based online finite‐time optimal tracking control for wheeled mobile robotic system with inequality constraints

AbstractIn this study, a finite‐time online optimal controller was designed for a nonlinear wheeled mobile robotic system (WMRS) with inequality constraints, based on reinforcement learning (RL) neural networks. In addition, an extended cost function, obtained by introducing a penalty function to the original long‐time cost function, was proposed to deal with the optimal control problem of the system with inequality constraints. A novel Hamilton‐Jacobi‐Bellman (HJB) equation containing the constraint conditions was defined to determine the optimal control input. Furthermore, two neural networks (NNs), a critic and an actor NN, were established to approximate the extended cost function and the optimal control input, respectively. The adaptation laws of the critic and actor NN were obtained with the gradient descent method. The semi‐global practical finite‐time stability (SGPFS) was proved using Lyapunov's stability theory. The tracking error converges to a small region near zero within the constraints in a finite period. Finally, the effectiveness of the proposed optimal controller was verified by a simulation based on a practical wheeled mobile robot model.