Mobile Robot Applications Research Articles

Analytical Formalism for Data Representation and Object Detection with 2D LiDAR: Application in Mobile Robotics.

In mobile robotics, LASER scanners have a wide spectrum of indoor and outdoor applications, both in structured and unstructured environments, due to their accuracy and precision. Most works that use this sensor have their own data representation and their own case-specific modeling strategies, and no common formalism is adopted. To address this issue, this manuscript presents an analytical approach for the identification and localization of objects using 2D LiDARs. Our main contribution lies in formally defining LASER sensor measurements and their representation, the identification of objects, their main properties, and their location in a scene. We validate our proposal with experiments in generic semi-structured environments common in autonomous navigation, and we demonstrate its feasibility in multiple object detection and identification, strictly following its analytical representation. Finally, our proposal further encourages and facilitates the design, modeling, and implementation of other applications that use LASER scanners as a distance sensor.

Momordica charantia leaf disease detection and treatment using agricultural mobile robot

Detecting diseases is a vital and crucial step in maintaining healthy, high-yielding plants. The challenge of manually identifying infections is arduous as well. The proposed work is to diagnose plant leaf diseases and discuss their origins and remedies. Image processing is used to discover the infected leaf and provide remedial measures through a mobile robot application. The use of machine learning techniques allows for the detection of leaf diseases using the support vector machine model, the K nearest neighbor model, and the Naïve Bayes classification to categorize the sample leaves. In this paper, the Momordica charantia leaf and the common four diseases dataset are developed, and a classification model is developed to identify and categorize leaf curl, downy mildew, powdery mildew, and angular leaf spot. Based on the disease classification, appropriate chemical pesticides are sprayed by controlling the servo actuated valve in the proposed agriculture robot, which is controlled and validated. The result reveals that the proposed approach has an average accuracy of 82% in identifying the disease type that remains more prevalent in Momordica charantia leaves than other compared classification algorithms.

Trajectory Control of Designed Experimental Mobile Robot

Due to advancing technology; nowadays mobile robot applications in hospitals have been increased. For that reason, it is very important and necessary to analyze the trajectory of such helping robotic system. However; there are many types of mobile robots have been utilized in hospital applications such as helping nurses. In this simulation study; a designed and controlled mobile robot was controlled by using standard feedback controllers. On the other hand, the robot was also tested with disturbances of ground surface roughness. The simulation results were improved that standard PID controller has superior performance to overcome surface roughness of the robot trajectory.

Semantic segmentation of thermal defects in belt conveyor idlers using thermal image augmentation and U-Net-based convolutional neural networks

The belt conveyor (BC) is the main means of horizontal transportation of bulk materials at mining sites. The sudden fault in BC modules may cause unexpected stops in production lines. With the increasing number of applications of inspection mobile robots in condition monitoring (CM) of industrial infrastructure in hazardous environments, in this article we introduce an image processing pipeline for automatic segmentation of thermal defects in thermal images captured from BC idlers using a mobile robot. This study follows the fact that CM of idler temperature is an important task for preventing sudden breakdowns in BC system networks. We compared the performance of three different types of U-Net-based convolutional neural network architectures for the identification of thermal anomalies using a small number of hand-labeled thermal images. Experiments on the test data set showed that the attention residual U-Net with binary cross entropy as the loss function handled the semantic segmentation problem better than our previous research and other studied U-Net variations.

A novel methodology for vision-based path planning and obstacle avoidance in mobile robot applications

ABSTRACT The availability of overhead views of the environments around mobile robots always present useful information provided that these views are correctly perceived. In this paper, a novel approach for vision-based path planning and obstacle avoidance for mobile robots is presented. The approach uses visual perception integrated with a proposed field-based path-planning algorithm to overcome some common path-planning issues such as local minima, problematic destinations, and following long paths around obstacles. An exponential angle field is generated around each obstacle that deviates from the robot’s orientation as it directs towards the destination. Obstacle field is activated or deactivated based on a proposed collision prediction algorithm. To satisfy robot convergence to the goal point, the obstacle field parameters are determined using the Lyapunov stability criterion. The method also proposes a search algorithm for proper exit in the case of robot and/or goal point trapping. Both simulation and experiments are used in validating the algorithm. The results show the effectiveness of the proposed algorithm in converging towards target points, avoiding collision with stationary obstacles and overcoming famous path-planning problems.

Real-time navigation of mecanum wheel-based mobile robot in a dynamic environment

Path planning and control of a mobile robot, in a dynamic environment, has been an important research topic for many years. In this paper an algorithm for autonomous motion of a mobile robot is proposed, with mecanum wheels, to reach a goal while avoiding obstacles through the shortest path in a dynamic environment. The proposed method uses a hybrid A⁎ and a velocity obstacle algorithms for path planning and obstacle avoidance. The A⁎ algorithm is implemented to explore the shortest path from starting position to the goal while avoiding all the static obstacles. However, in real time applications the dynamic obstacles need to be avoided, therefore, for such a case velocity obstacle algorithm is unified with the A⁎ algorithm. Initially, the proposed algorithm is verified through simulations. Then it is implemented using experimental setup in real time environment using single and multiple static obstacles as well as on a dynamic obstacle. It can be observed that the robot reaches the goal, effectively by avoiding static and dynamic obstacles. Moreover, the performance of the proposed work is evaluated through qualitative comparison between proposed method and recently published work, showing that the proposed algorithm is gives better features than existing work. In the end, the possible application of mobile robot having mecanum wheels with proposed path planning method is also given in the paper.

Real-Time Production Scheduling and Industrial Sonar and Their Application in Autonomous Mobile Robots

In real-time production planning, there are exceptional events that can cause problems and deviations in the production schedule. These circumstances can be solved with real-time production planning, which is able to quickly reschedule the operations at each work centre. Mobile autonomous robots are a key element in this real-time planning and are a fundamental link between production centres. Work centres in Industry 4.0 environments can use current technology, i.e., a biomimetic strategy that emulates echolocation, with the aim of establishing bidirectional communication with other work centres through the application of agile algorithms. Taking advantage of these communication capabilities, the basic idea is to distribute the execution of the algorithm among different work centres that interact like a parasympathetic system that makes automatic movements to reorder the production schedule. The aim is to use algorithms with an optimal solution based on the simplicity of the task distribution, trying to avoid heuristic algorithms or heavy computations. This paper presents the following result: the development of an Industrial Sonar algorithm which allows real-time scheduling and obtains the optimal solution at all times. The objective of this is to reduce the makespan, reduce energy costs and carbon footprint, and reduce the waiting and transport times for autonomous mobile robots using the Internet of Things, cloud computing and machine learning technologies to emulate echolocation.

A Path Planning System for Orchard Mower Based on Improved A* Algorithm

The application of intelligent mobile robots in agriculture has emerged as a new research frontier, with the integration of autonomous navigation technology and intelligent agricultural robots being the key to the widespread adoption of smart agricultural machinery. This paper investigates comprehensive coverage path planning for tracked lawnmowers within orchard environments and addresses challenges related to task allocation and battery life. Firstly, in this study, the motion model of the tracked lawnmower was initially simplified based on assumptions about the orchard environment. Force analyses were conducted on each of its motion mechanisms. For the known orchard environment, a grid-based mapping technique was employed to model the orchard environment. Then, in order to improve the algorithm speed and reduce the number of turns during the lawnmower’s traversal, the A* search algorithm was enhanced by combining the method of robot cluster traversal in the orchard environment. Finally, the improved method was simulated and verified in the MATLAB platform to investigate the influence of the number of lawnmower clusters on the path planning in the connected and non-connected orchards. Furthermore, two sets of on-site field trials were meticulously designed to validate the reliability, practicality, and efficacy of the simulation experiments.

Distributed Fixed-Time Formation Tracking Control for the Multi-Agent System and an Application in Wheeled Mobile Robots

This work addresses the issue of multi-agent system (MAS) formation control under external disturbances and a directed communication topology. Firstly, a new disturbance observer is proposed to effectively reconstruct and compensate for external disturbances within a short period of time. Then, the integral terminal sliding mode technology is introduced to devise a novel distributed formation control protocol, ultimately realizing the stability of the MAS within a fixed time. Moreover, by means of rigorous Lyapunov theory analyses, a faster formation convergence rate and more accurate consensus accuracies are achieved in the proposed fixed-time strategy with variable exponent form. Finally, the formation tracking control scheme is applied to a multi-wheeled mobile robot (WMR) system. The experimental results strongly support the fine effectiveness of the control scheme designed in this work.

A Planar Multi-Inertial Navigation Strategy for Autonomous Systems for Signal-Variable Environments.

The challenge of precise dynamic positioning for mobile robots is addressed through the development of a multi-inertial navigation system (M-INSs). The inherent cumulative sensor errors prevalent in traditional single inertial navigation systems (INSs) under dynamic conditions are mitigated by a novel algorithm, integrating multiple INS units in a predefined planar configuration, utilizing fixed distances between the units as invariant constraints. An extended Kalman filter (EKF) is employed to significantly enhance the positioning accuracy. Dynamic experimental validation of the proposed 3INS EKF algorithm reveals a marked improvement over individual INS units, with the positioning errors reduced and stability increased, resulting in an average accuracy enhancement rate exceeding 60%. This advancement is particularly critical for mobile robot applications that demand high precision, such as autonomous driving and disaster search and rescue. The findings from this study not only demonstrate the potential of M-INSs to improve dynamic positioning accuracy but also to provide a new research direction for future advancements in robotic navigation systems.

Local feature matching and tracking optimization of machine VSLAM based on AG Sinkhorn

SummaryAt present, the visual Simultaneous localization and mapping method has a wide range of applications in mobile robots and unmanned driving. However, when the environment changes, the accuracy of the system drops sharply. Aiming at the low matching accuracy of local feature matching and tracking optimization in current visual simultaneous localization and mapping methods, the attention map neural network and Sinkhorn algorithm are used to extract and match the image features. Then, it improves the real‐time performance of the visual simultaneous localization and mapping system by local optimization of feature point matching, and proposes an optimization method for visual simultaneous localization and mapping in view of an end‐to‐end algorithm. The experiment illustrates that in the experimental comparison of HPatches View dataset, the Homography estimation was improved by 0.092, the matching accuracy was improved by 0.087, and the matching recall was improved by 0.038. In the experimental comparison of HPatches Illum dataset, the Homography estimation was improved by 0.015, the matching accuracy is improved by 0.076, and the matching recall was improved by 0.036. The local feature matching and tracking optimization method of the visual Simultaneous localization and mapping system through the attention map neural network and Sinkhorn algorithm can enhance the matching accuracy of local feature matching and tracking optimization in dynamic environment and under changing lighting conditions.

Autonomous Obstacle Avoidance and Trajectory Planning for Mobile Robot Based on Dual-Loop Trajectory Tracking Control and Improved Artificial Potential Field Method

In order to better meet the practical application needs of mobile robots, this study innovatively designs an autonomous obstacle avoidance and trajectory planning control strategy with low computational complexity, high cost-effectiveness, and the ability to quickly plan a collision-free smooth trajectory curve. This article constructs the kinematic model of the mobile robot, designs a dual-loop trajectory tracking control strategy for position control law and attitude control law algorithms, and improves the traditional artificial potential field method to achieve a good obstacle avoidance strategy for mobile robots. Based on the dual-loop trajectory tracking control and the improved artificial potential field method, the autonomous obstacle avoidance and trajectory planning scheme of the mobile robot is designed, and closed-loop stability verification and analysis are conducted on the overall control system. And through the detailed simulation and experiments, the advantages of the proposed method in trajectory tracking accuracy and motion stability compared to the existing methods are verified, showing good effectiveness and feasibility and laying a good foundation for the application of mobile robots in practical complex scenes.

Hybrid Whale Optimization with a Firefly Algorithm for Function Optimization and Mobile Robot Path Planning.

With the wide application of mobile robots, mobile robot path planning (MRPP) has attracted the attention of scholars, and many metaheuristic algorithms have been used to solve MRPP. Swarm-based algorithms are suitable for solving MRPP due to their population-based computational approach. Hence, this paper utilizes the Whale Optimization Algorithm (WOA) to address the problem, aiming to improve the solution accuracy. Whale optimization algorithm (WOA) is an algorithm that imitates whale foraging behavior, and the firefly algorithm (FA) is an algorithm that imitates firefly behavior. This paper proposes a hybrid firefly-whale optimization algorithm (FWOA) based on multi-population and opposite-based learning using the above algorithms. This algorithm can quickly find the optimal path in the complex mobile robot working environment and can balance exploitation and exploration. In order to verify the FWOA's performance, 23 benchmark functions have been used to test the FWOA, and they are used to optimize the MRPP. The FWOA is compared with ten other classical metaheuristic algorithms. The results clearly highlight the remarkable performance of the Whale Optimization Algorithm (WOA) in terms of convergence speed and exploration capability, surpassing other algorithms. Consequently, when compared to the most advanced metaheuristic algorithm, FWOA proves to be a strong competitor.

Enhancing indoor place classification for mobile robots using RGB-D data and deep learning architectures

Place classification is crucial for a robot's ability to make high- level decisions. When a robot can identify its operating environment, it can provide more appropriate services. This capability is similar to how humans use their understanding of their surroundings to make informed decisions about appropriate actions. Depth data offers valuable spatial information that can enhance place classification on a robot. However, it is more common for mobile robot applications to rely on RGB data rather than RGB-D data for classifying indoor places. This study demonstrates that incorporating depth information improves the classification of indoor places using a mobile robot. Data were collected from a mobile robot, and indoor scenes were classified based on RGB and RGB-D inputs. A comparison was made between the performance of VGG16, Inception v3, and ResNet50 architectures using RGB data alone. Subsequently, depth information was fused with these RGB models. Experiments showed that classification accuracy improved when tested on the mobile robot by including depth data. In the experiment, the robot created a map of the indoor environment and identified four different rooms on the map using the trained models. This demonstrates the enhanced classification capabilities achieved by incorporating depth information.

FPGA implementation in mobile robot applications: State of the art review

Field-programmable gate arrays (FPGAs) have emerged as a valuable technology in mobile robotics due to their unique ability to be reconfigured to perform specific tasks efficiently. This article presents a comprehensive state-of-the-art review of the implementations of FPGAs in mobile robots. The article explores the various applications where FPGAs have been successfully integrated and discuss their advantages and limitations. The review covers vital areas such as perception, control, navigation, and communication, highlighting each domain's innovative approaches and advancements. Additionally, the study examines FPGAs' impact on mobile robot performance, including improvements in real-time processing, energy efficiency, and adaptability to changing environments. Furthermore, challenges and current efforts in leveraging FPGAs for mobile robotics are discussed, paving the way for exciting developments and possibilities in this rapidly evolving field. This review is a valuable resource for researchers, engineers, and enthusiasts interested in understanding the state of the art and potential of FPGA implementations in mobile robots.

Analysis of kinematic characteristics of a mobile caterpillar robot with a SCARA-type manipulator

Automation and robotization of various production and technological processes in many industries is one of the leading trends in the development of modern society. Industrial robots have recently become quite widespread, and it is almost impossible to imagine any modern production in the fields of mechanical engineering (machine building), instrumentation, pharmaceuticals, food, chemical industries, etc., without robotic complexes. Over the past few decades, another area of robotics has emerged: autonomous mobile robots. It combines research in mechanics, electronics, and computer technologies, including artificial intelligence. Among the most common applications of autonomous mobile robots are the performance of various technological operations in places that are dangerous to human life (radiation, biological or chemical contamination) or uninhabitable (space, sea depths, volcanic craters, etc.). Mobile robots have also proven themselves in rescue operations during cataclysms and natural disasters, anti-terrorist operations, military operations, mine clearance, etc. Given the urgency of the issue of mobile robotics development, this article proposes a new design of an autonomous robotic complex built on the basis of a tracked chassis and equipped with a SCARA-type manipulator. The main task of the developed robot is to perform various technological operations in places where human presence is dangerous or impossible, in particular, when performing demining tasks. In the course of the research, the kinematics of the manipulator was analyzed in detail to determine its working area, and the kinematic parameters of the tracked chassis were experimentally tested while it was moving over rough terrain. The obtained results can be used to further improve the design and control system of the robot and manipulator and in the process of determining the specific technological tasks that will be assigned to this robotic platform.

Motion planning and control of an autonomous mobile robot

The application of autonomous mobile robots (AMRs) has gradually become crucial in smart factories due to the advantages of improving production efficiency and reducing labour costs. Motion planning has been a key part of AMR control development. This paper presents motion planning and position tracking control systems of an omnidirectional wheel AMR powered by a hybrid fuel cell and battery power source. First, the kinematical and dynamic models of the AMR are introduced. The navigation system comprises three loops, with the first loop being motor control, the second loop being position tracking control, and a motion planning layer. The position data of the AMR for feedback control is obtained through sensor fusion of data from the inertial measurement unit (IMU) sensor, encoder sensor, and ranging sensor with simultaneous localisation and mapping (SLAM) algorithm. The motion planning is then applied to obtain an optimal path with the shortest distance and collision-free movement. In addition, the tracking algorithm is designed to drive the AMR to follow the optimal path and achieve high accuracy. The experimental results show a 30% improvement in tracking accuracy compared to traditional approaches and 8 hours of continuous working, which is promising for industrial applications, and the results are satisfactory in terms of both accuracy and efficiency requirements.

Simulation of Autonomous Driving for a Line-Following Robotic Vehicle: Determining the Optimal Manoeuvring Mode

Mobile robotic systems offer valuable test platforms due to their shared features with autonomous vehicles, including features such as sensor technologies, navigation algorithms, and control systems. However, constraints in laboratory environments or technical resources, along with the need for extensive testing, often necessitate the use of virtual test laboratories. While line-following is a widely preferred application in mobile robotics, research on this topic within virtual laboratories is limited. This study pioneers the use of a car-like robotic vehicle in conducting line-following tests within a virtual laboratory environment. To facilitate these tests, a virtual simulator was developed to meet the requirements of realistic simulations. This simulator includes simulated elements, such as roads and environmental features, along with virtual sensors designed to collect and process dynamic motion data. An exceptional aspect of this study is its ability to collect consistent dynamic travel data by sampling realistic sensor information within a virtual environment. The developed line-following algorithm employs a controller to minimise lateral deviation while the robotic vehicle follows a road line during its movement. The study conducted virtual driving tests using two different manoeuvre modes on four distinct road segments, exploring how the manoeuvring style influences the driving quality. It was demonstrated that in the low manoeuvre mode, the ride is more comfortable, but exhibits a greater route deviation due to reduced oscillation, while the high manoeuvre mode exhibits the opposite behaviour.

A Mecanum wheel model based on orthotropic friction with experimental validation

Mecanum wheels are commonly used in mobile robotics applications as they enable a platform with three or more individually controlled wheels to move in arbitrary directions without turning. However, compared to the well-studied models for pneumatic and railroad wheels, modeling of Mecanum wheels remains mostly unexplored. In this research a novel, efficient Mecanum wheel model, based on orthotropic friction to model roller-ground contact, is presented together with experimental data. The equations are derived and the Mecanum wheel model is implemented in an open source multibody simulation code and shown to run faster than real-time with time steps in the order of milliseconds, enabling the model to be utilized for control tasks. Extensive experiments are performed with a real robot and statistical evaluation is presented. Both different robot velocities and changes in the center of mass, representing different payloads of the robot, are showcased. The simulation model captures the deviations from the idealized kinematics realistically and can reproduce the measurements.

Optimal Coverage Path Planning for Agricultural Vehicles with Curvature Constraints

Complete coverage path planning (CCPP) is vital in mobile robot applications. Optimizing CCPP is particularly significant in precision agriculture, where it enhances resource utilization, reduces soil compaction, and boosts crop yields. This work offers a comprehensive approach to CCPP for agricultural vehicles with curvature constraints. Our methodology comprises four key stages. First, it decomposes complex agricultural areas into simpler cells, each equipped with guidance tracks, forming a fixed track system. The subsequent route planning and smooth path planning stages compute a path that adheres to path constraints, optimally traverses the cells, and aligns with the track system. We use the generalized traveling salesman problem (GTSP) to determine the optimal traversing sequence. Additionally, we introduce an algorithm for calculating paths that are both smooth and curvature-constrained within individual cells, as well as paths that enable seamless transitions between cells, resulting in a smooth, curvature-constraint coverage path. Our modular approach allows method flexibility at each step. We evaluate our method on real agricultural fields, demonstrating its effectiveness in minimizing path length, ensuring efficient coverage, and adhering to curvature constraints. This work establishes a strong foundation for precise and efficient agricultural coverage path planning, with potential for further real-world applications and enhancements.