Development Of Autonomous Mobile Robot Research Articles

Path Planning for Autonomous Mobile Robot Using Intelligent Algorithms

Machine learning technologies are being integrated into robotic systems faster to enhance their efficacy and adaptability in dynamic environments. The primary goal of this research was to propose a method to develop an Autonomous Mobile Robot (AMR) that integrates Simultaneous Localization and Mapping (SLAM), odometry, and artificial vision based on deep learning (DL). All are executed on a high-performance Jetson Nano embedded system, specifically emphasizing SLAM-based obstacle avoidance and path planning using the Adaptive Monte Carlo Localization (AMCL) algorithm. Two Convolutional Neural Networks (CNNs) were selected due to their proven effectiveness in image and pattern recognition tasks. The ResNet18 and YOLOv3 algorithms facilitate scene perception, enabling the robot to interpret its environment effectively. Both algorithms were implemented for real-time object detection, identifying and classifying objects within the robot’s environment. These algorithms were selected to evaluate their performance metrics, which are critical for real-time applications. A comparative analysis of the proposed DL models focused on enhancing vision systems for autonomous mobile robots. Several simulations and real-world trials were conducted to evaluate the performance and adaptability of these models in navigating complex environments. The proposed vision system with CNN ResNet18 achieved an average accuracy of 98.5%, a precision of 96.91%, a recall of 97%, and an F1-score of 98.5%. However, the YOLOv3 model achieved an average accuracy of 96%, a precision of 96.2%, a recall of 96%, and an F1-score of 95.99%. These results underscore the effectiveness of the proposed intelligent algorithms, robust embedded hardware, and sensors in robotic applications. This study proves that advanced DL algorithms work well in robots and could be used in many fields, such as transportation and assembly. As a consequence of the findings, intelligent systems could be implemented more widely in the operation and development of AMRs.

ADVANCING INDUSTRY 4.0 WITH ROS: A CASE STUDY ON AUTONOMOUS MOBILE ROBOT TECHNOLOGICAL ADVANCEMENTS

In the world of Industry 4.0, Autonomous Mobile Robots (AMRs) are now vital parts of modern industrial automation. This study examines how the Robot Operating System (ROS) plays a crucial role in advancing technology for AMRs. By looking at real-life examples, it shows how ROS helps in creating and using AMRs, changing how industrial processes work. The study demonstrates how ROS is being integrated into AMR design and operation, leading to improved autonomy, flexibility, and productivity in industrial settings. This study discusses how ROS-powered AMRs have transformed various tasks like material handling, warehouse logistics, and autonomous navigation, leading to increased productivity and cost-efficiency. It also explores the challenges and opportunities brought about by ROS in the Industry 4.0 era, including sensor fusion, machine learning, and human-robot teamwork. Furthermore, ROS not only influences the design and operation of AMR, but also enables smooth integration with advanced technologies such as sensor fusion and machine learning. This opens up opportunities for improved flexibility and teamwork between humans and robots in the ever-evolving environment of Industry 4.0. The importance of ROS in connecting traditional manufacturing practices with the changing demands of the fourth industrial revolution is emphasized.

Development of Autonomous Mobile Robot with 3DLidar Self-Localization Function Using Layout Map

In recent years, there has been growing interest in autonomous mobile robots equipped with Simultaneous Localization and Mapping (SLAM) technology as a solution to labour shortages in production and distribution settings. SLAM allows these robots to create maps of their environment using devices such as Lidar, radar, and sonar sensors, enabling them to navigate and track routes without prior knowledge of the environment. However, the manual operation of these robots for map construction can be labour-intensive. To address this issue, this research aims to develop a 3D SLAM autonomous mobile robot system that eliminates the need for manual map construction by utilizing existing layout maps. The system includes a PC for self-position estimation, 3DLidar, a camera for verification, a touch panel display, and the mobile robot itself. The proposed SLAM method extracts stable wall point cloud information from 3DLidar, matches it with the wall surface information in the layout map, and uses a particle filter to estimate the robot’s position. The system also includes features such as route creation, tracking, and obstacle detection for autonomous movement. Experiments were conducted to compare the proposed system with conventional 3D SLAM methods. The results showed that the proposed system significantly reduced errors in self-positioning and enabled accurate autonomous movement on specified routes, even in the presence of slight differences in layout maps and obstacles. Ultimately, this research demonstrates the effectiveness of a system that can transport goods without the need for manual environment mapping, addressing labour shortages in such environments.

Dynamic Obstacle Avoidance and Path Planning through Reinforcement Learning

The use of reinforcement learning (RL) for dynamic obstacle avoidance (DOA) algorithms and path planning (PP) has become increasingly popular in recent years. Despite the importance of RL in this growing technological era, few studies have systematically reviewed this research concept. Therefore, this study provides a comprehensive review of the literature on dynamic reinforcement learning-based path planning and obstacle avoidance. Furthermore, this research reviews publications from the last 5 years (2018–2022) to include 34 studies to evaluate the latest trends in autonomous mobile robot development with RL. In the end, this review shed light on dynamic obstacle avoidance in reinforcement learning. Likewise, the propagation model and performance evaluation metrics and approaches that have been employed in previous research were synthesized by this study. Ultimately, this article’s major objective is to aid scholars in their understanding of the present and future applications of deep reinforcement learning for dynamic obstacle avoidance.

Optimising local map updates in a dynamic environment using an object detection method

Keeping maps up to date in a time-varying environment is a big challenge in the development of autonomous mobile robots. In this paper, the authors propose a new solution to update the local map of the robot in a dynamic environment in real time. This proposed method works based on a combination of lidar scan data and object recognition and positioning algorithms to allow the robot to update the map continuously. Laser scan data for the same part of the environment at different times are compared to perform change detection. When there are enough changes in a location, the map will be edited to remove old features or add new features. To optimise updates for moving objects, authors use object recognition and positioning to decide which objects should be added to the map. The results are displayed in a simulated environment, showing the ability to maintain an up-to-date map despite long-term environmental changes.

Obstacle-avoidance trajectory planning and sliding mode-based tracking control of an omnidirectional mobile robot

Trajectory planning and tracking control play a vital role in the development of autonomous mobile robots. To fulfill the tasks of trajectory planning and tracking control of a Mecanum-wheeled omnidirectional mobile robot, this paper proposes an artificial potential field-based trajectory-planning scheme and a discrete integral terminal sliding mode-based trajectory-tracking control strategy. This paper proposes a trajectory-planning scheme and a trajectory-tracking control strategy for a Mecanum-wheeled omnidirectional mobile robot by using artificial potential field and discrete integral terminal sliding mode, respectively. First, a discrete kinematic-and-dynamic model is established for the Mecanum-wheeled omnidirectional mobile robot. Then, according to the positions of the robot, target, and obstacles, a reference an obstacle-avoidance trajectory is planned and updated iteratively by utilizing artificial potential field functions. Afterward, a discrete integral terminal sliding mode control algorithm is designed for the omnidirectional mobile robot such that the robot can track the planned trajectory accurately. Meanwhile, the stability of the control system is analyzed and guaranteed proved in the sense of Lyapunov. Last, simulations are executed in the scenarios of static obstacles and dynamic obstacles. The simulation results demonstrate the effectiveness and merits of the presented methods.

Human Action Recognition of Autonomous Mobile Robot Using Edge-AI

The development of autonomous mobile robots (AMRs) has brought with its requirements for intelligence and safety. Human action recognition (HAR) within AMR has become increasingly important because it provides interactive cognition between human and AMR. This study presents a full architecture for edge-artificial intelligence HAR (Edge-AI HAR) to allow AMR to detect human actions in real time. The architecture consists of three parts: a human detection and tracking network, a key frame extraction function, and a HAR network. The HAR network is a cascade of a DenseNet121 and a double-layer bidirectional long-short-term-memory (DLBiLSTM), in which the DenseNet121 is a pretrained model to extract spatial features from action key frames and the DLBiLSTM provides a deep two-directional LSTM inference to classify complicated time-series human actions. Edge-AI HAR undergoes two optimizations—ROS distributed computation and TensorRT structure optimization—to give a small model structure and high computational efficiency. Edge-AI HAR is demonstrated in two experiments using an AMR and is demonstrated to give an average precision of 97.58% for single action recognition and around 86% for continuous action recognition.

Autonomous mobile robot development based on duckietown platform for recognizing and following the traffic sign

This paper presents development of an autonomous mobile robot which can recognize the traffic sign and move according to that recognized sign. The mobile robot was developed based on Duckietown platform, which is a research which focuses on self-driving vehicles. The mobile robot was equipped with a fish eye camera as its vision device and raspberry pi.3 controller module as the main controller for doing image processing in order to recognize the signs and controlling the movement of the robot. The program runs on ROS platform. The system has been tested by moving mobile robot through all area and the results showed average recognition rate of 80%. The system also has been tested in variation of light intensity and the result showed average recognition rate of 80%. Another test also was done by adding disturbance object which has similar or same color as sign color and the result showed average recognition rate of 43.3%.

Special Issue on Real World Robot Challenge in Tsukuba and Osaka

The Tsukuba Challenge is an open experiment of autonomous mobile robots in the real world. In its third stage since 2018, it is now to be held on a new course that starts at the Tsukuba City Hall. New tasks that require functions expected for autonomous travel in the real world have now been added, including passing checkpoints announced a day before the event, starting two vehicles simultaneously, traveling in an unmeasured environment, and strictly observing stop lines in the course. Also, in the spirit of the Tsukuba Challenge, the Nakanoshima Challenge, an open demonstration experiment project, has been held in the city of Osaka since 2018. As the only event in which autonomous mobile robots travel in the urban area of Osaka, the Nakanoshima Challenge is expected to identify new issues peculiar to autonomous navigation in real urban environments and to find solutions to them. This special issue includes a review paper on the Tsukuba Challenge, four research papers on the results of experiments done in the Tsukuba Challenge, four research papers related to the Nakanoshima Challenge, and three development reports. This special issue provides its readers with the frontline issues and the current status of development of autonomous mobile robots in real-world environments. We hope that the innovative efforts presented in this special issue will contribute to the development of science and industry.

Development of a Web system that facilitates development of autonomous mobile robots by deep reinforcement learning

Development of a Web system that facilitates development of autonomous mobile robots by deep reinforcement learning

Design of Sensor Data Fusion Algorithm for Mobile Robot Navigation Using ANFIS and Its Analysis Across the Membership Functions

Design and development of autonomous mobile robots attracts more attention in the era of autonomous navigation. There are various algorithms used in practice for solving research problems related to the robot model and its operating environment. This paper presents the design of data fusion algorithm using Adaptive Neuro Fuzzy Interface (ANFIS) for the navigation of mobile robots. Detailed analysis of various membership functions (MFs) provided in this paper helps to select the most appropriate MF for the design of similar navigation systems. The combined use of fuzzy and neural networks in ANFIS makes the measured distance value of the residual covariance consistent with its actual value. The data fusion algorithm within the controller of the mobile robot fuses the input from ultrasonic and infrared sensors for better environment perception. The results indicate that the data fusion algorithm provides minimal root mean square error (RMSE) and mean absolute percentage error (MAPE) when compared with that of the individual sensors.

Bioenergy Based Power Sources for Mobile Autonomous Robots

This paper presents the problem of application of modern developments in the field of bio-energy for the development of autonomous mobile robots’ power sources. We carried out analysis of biofuel cells, gasification and pyrolysis of biomass. Nowadays, very few technologies in the bioenergy field are conducted with regards to the demands brought by robotics. At the same time, a number of technologies, such as biofuel cells, have now already come into use as a power supply for experimental autonomous mobile robots. The general directions for research that may help to increase the efficiency of power energy sources described in the article, in case of their use in robotics, are also presented.

Development of autonomous mobile robot equipped with sensors for dead reckoning in indoor

Development of autonomous mobile robot equipped with sensors for dead reckoning in indoor

Student Education Utilizing the Development of Autonomous Mobile Robot for Robot Competition

The purpose of our education approach is the giving the knowledge and the experience for the research activity to the freshman student who assigned the Lab. We think that “the interaction between the robot and the real world information which is observed by sensor” is important for the student education for student’s research in the Lab. Here, the interaction means how perform the robot based on the real world information which is observed by sensors. The development of the mobile robot which work in Robot competition (Tsukuba Challenge) requires “the interaction between the mobile robot and real world.” For this reason, we think that this activity (participating in the Tsukuba Challenge anf development of the mobile robot) is effective for the student education, and we educate the student by utilizing the development of the mobile robot for Tsukuba Challenge. Although our approach performed in short span, we achieve the good result. For example, the students achieves the research outcome by utilizing the learned skill, and submitted the paper to the academic conference. We verified that our education approach is effective for improvement the student education and motivation.

Development of Autonomous Mobile Robot that Can Navigate in Rainy Situations

[abstFig src='/00280004/02.jpg' width='300' text='Navigation under strong rainy condition' ] The Real World Robot Challenge (RWRC), a technical challenge for mobile outdoor robots, has robots automatically navigate a predetermined path over 1 km with the objective of detecting specific persons. RWRC 2015 was conducted in the rain and every robot could not complete the mission. This was because sensors on the robots detected raindrops and the robots then generated unexpected behavior, indicating the need to study the influence of rain on mobile navigation systems – a study clearly not yet sufficient. We begin by describing our robot’s waterproofing function, followed by investigating the influence of rain on the external sensors commonly used in mobile robot navigation and discuss how the robot navigates autonomous in the rain. We conducted navigation experiments in artificial and actual rainy environments and those results showed that the robot navigates stably in the rain.

Development of Autonomous Mobile Robot “MML-05” Based on i-Cart Mini for Tsukuba Challenge 2015

[abstFig src='/00280004/04.jpg' width='300' text='Autonomous mobile robots entered in the Tsukuba Challenge 2015' ] This paper describes a self-localization method for autonomous mobile robots entered in the Tsukuba Challenge 2015. One of the important issues in autonomous mobile robots is accurately estimating self-localization. An occupancy grid map, created manually before self-localization has typically been utilized to estimate the self-localization of autonomous mobile robots. However, it is difficult to create an accurate map of complex courses. We created an occupancy grid map combining local grid maps built using a leaser range finder (LRF) and wheel odometry. In addition, the self-localization of a mobile robot was calculated by integrating self-localization estimated by a map and matching it to wheel odometry information. The experimental results in the final run of the Tsukuba Challenge 2015 showed that the mobile robot traveled autonomously until the 600 m point of the course, where the occupancy grid map ended.

Prototyping of Kinematics Simulator for Supporting Autonomous Mobile Robot Development

[abstFig src='/00280004/05.jpg' width='300' text='Appearance of the kinematics simulator' ] A kinematics simulator for an autonomous mobile robot has been proposed to simulate complicated motions such as those caused by the interaction between a robot and its environment in terms of geometric relationship. The simulator is expected to assist in the development of a robot control system for autonomous running in the real world. This paper presents the simulator concept, its basic configuration, and the results of preliminary simulation experiments, which have been performed to evaluate a simple motion model, and an environment model based on occupancy grid maps and a laser range finder pseudo sensor model consisting of a typical probabilistic density. The results of the simulation experiments using the aforementioned multiple models are also presented to demonstrate that the simulator can perform in various numerical environments.

Development of Autonomous Mobile Robot Using Articulated Steering Vehicle and Lateral Guiding Method

<div class=""abs_img""> <img src=""[disp_template_path]/JRM/abst-image/00270004/03.jpg"" width=""300"" /> Autonomous robot “AR Chair”</div> This paper is discusses an autonomous mobile robot entered in the Real World Robotics Challenges 2014 (RWRC) in Tsukuba. Our project was to develop a wheelchair able to navigate stairs autonomously. Step 1 develops a center articulated vehicle, called the AR Chair, which has 4 wheels and a controller including LIDARs. The center articulated vehicle has a stiff structure and travels with the front and rear wheels on the same path, so there is no inner wheels difference. The robotic vehicle carries users weighing up to 100 kg. The autonomous controller is the same as the Smart Dump 7 combined with the RWRC 2013 to achieve the challenge, excluding the geometrical relationship of the steering angle and communication command for motor drivers to the AR Chair. The advantage of the robot is shown by experimental data from the RWRC 2014’s final run. </span>

Development of Autonomous Mobile Robot Based on Accurate Map in the Tsukuba Challenge 2014

<div class=""abs_img""> <img src=""[disp_template_path]/JRM/abst-image/00270004/04.jpg"" width=""300"" /> Navigation method for mobile robots</div> We describe a navigation method for autonomous mobile robots and detail knowledge obtained through Tsukuba Challenge 2014 trial runs. The challenge requires robots to navigate autonomously 1.4 km in an urban area and to search for five persons in three areas. Accurate maps are important tools in localization on complex courses in autonomous outdoor navigation. We constructed an occupancy grid map using laser scanners, gyro-assisted odometry and a differential global positioning system (DGPS). In this study, we use maps as a graphical interface. Namely, by using maps, we give environmental information, untravelable low objects such as curb stones, and areas in nonsearches for “target” persons. For the purpose of increasing the map reusability, we developed a waypoint editor, which can modify waypoints on maps to fit a course to a situation. We also developed a velocity control method that the robot uses to follow pedestrians and other robot by keeping safety distance on the course. As a result, our robot took part five of seven official trial runs to get to the goal. This indicates that the autonomous navigation method was stable in the Tsukuba Challenge 2014 urban environment. </span>

Wireless Position Tracking of a DTMF based Mobile Robot using GSM and GPS

The advent of new technologies has revolutionized the era of embedded system design in a way that every user is surrounded by smart devices (robots) which makes their life easier and comfortable. It has also been predicted by the researchers that by 2020, there will be billions of embedded devices talking to each other as compared to human beings termed as Internet of things (IOT). This paper is generally appropriated with the development of autonomous mobile robot used for wireless position tracking using GPS and sending that precise information on to a device such as mobile or tablet using GSM. This robot is equipped with GPS (for detection of location), GSM (for wireless data transfer), DTMF (for controlling robot with mobile or tab), sonar sensor (for obstacle detection) and flash light (for night vision). This mobile robot has wide variety of applications in industries like defense, aerospace, agriculture, and security etc. The basic functionality of the proposed design is simulated on proteus 7.1 and the complete design is implemented around ARM7 controller with required necessary interfaces.