Autonomous Mobile Robot Platform Research Articles

Design of Interface Board Based on ESP32 Module for Autonomous Mobile Robot Platform

Design of Interface Board Based on ESP32 Module for Autonomous Mobile Robot Platform

Development of control algorithm for autonomous intelligent robotic complex (AIRTC)

The work is dedicated to the development of a control algorithm for the autonomous intelligent robotic complex (AIRTC). The unmanned ground vehicle consists of a corpus that contains batteries, motor wheels, sensors, video cameras, on-board computer for processing data from sensors and video cameras. The general dimensions of the autonomous mobile robotic platform are 780x650x550 mm. The small size of the mobile platform allows it to be used in small areas, where the use of other vehicles is economically unprofitable. The key role in the autonomous mode of operation of the robotic complex is intended to the use of machine learning technologies to realize the possibility of performing the task assigned to the autonomous robotic platform in indoor and outdoor areas. An algorithm has been developed to control the basic platform of an autonomous robotic technical complex and to detect key obstacles automatically using a neural network. The accuracy of detecting the eight designated classes of obstacles was 85%.

INCREASING Q-FACTOR OF THE DIGITAL SENSOR SIGNALS PROCESSING PATH IN AUTONOMOUS MOBILE ROBOTIC PLATFORMS WITH LIMITED COMPUTING RESOURCES

Abstract. The paper is considered the problem of constructing autonomous mobile robotic platform with the possibility of rearranging their characteristics depending on the operating conditions. A new approach is proposed for calculating the parameters of a new connection with a series connection of low-order components of the same type. The resulting ratio allows you to accurately calculate the frequencies of the n-th connection of the same type components. Such a connection allows increasing the quality factor characteristic of the amplitude frequency characteristic and reducing the bandwidth by 3 times with eight connected filters of the same type. Bandpass filters and notch Butterworth filters are considered. Behavior of the AFC for serial connection is described. Key words: amplitude frequency characteristic, phase frequency characteristic, Industry 4.0-6.0, digital filter, bandpass filter, notch filter, quality factor characteristics, frequency dependent component, notch filter based on bandpass filer, serial connection.

A Bayesian Neural Network-based Obstacle Avoidance Algorithm for an Educational Autonomous Mobile Robot Platform

Autonomous mobile robots belong to automatically controlled objects that are designed and produced for various demands. This study aimed to develop an inexpensive platform of autonomous mobile robots that can be used for educational and research purposes in technical universities. The robot was built based on popular ultrasonic sensors to detect obstacles and a Raspberry Pi 4, which is a Linux-embedded computer. An effective obstacle avoidance algorithm for the robot was developed using a Bayesian neural network for classification. Training a Bayesian neural network does not require a validation dataset separate from the available data. In addition, the Bayesian approach can effectively handle the uncertainty of the system and result in the best generation for the network when inferring the unseen data. Training data are generated using robot-to-obstacle distances and the corresponding navigation modes. The commands to control the left and right motors of the robot are generated by the pretrained Bayesian neural network for three modes of navigation, forward, left, and right. Finally, this system can be useful as it can be conveniently integrated with advanced robot control algorithms.

Development a prototype of the basic platform of the autonomous intelligent robotics system (AIRS)

This work is devoted to the development of a prototype of the basic platform of the autonomous intelligent robotics system (AIRS). The frame of the experimental basic platform was made, main units and aggregates location was determined, testing of mechanical units and aggregates was carried out. Ultrasonic and infrared rangefinders were used to determine the distance to the object. The calibration of the sensors was performed when interacting with objects made of different materials, at different angles of the surfaces of these objects, and under different envi-ronmental conditions. An electronic printed circuit board has been developed to connect various sensors to an au-tonomous mobile robotic platform. The overall dimensions of the platform are 780x650x550 mm. The small size of the mobile basic platform allows it to be used on small sites where the use of other machines is economically unprofitable.

Development of an Autonomous Mobile Robot Platform for Smart Farms

Development of an Autonomous Mobile Robot Platform for Smart Farms

FEASIBILITY AND ACCURACY OF AS-BUILT MODELLING FROM SLAM-BASED POINT CLOUDS: PRELIMINARY RESULTS

Abstract. Nowadays, portable Mobile Mapping Systems (MMSs) and robotic mapping platforms leveraging on Simultaneous Localization and Mapping (SLAM) methods are gaining increasing attention for architectural and construction surveying, representing an efficient solution for geometric data acquisition for scan-to-BIM purposes. However, the applicability of standard modelling workflows and the accuracy of Building Information Models (BIM) that can be obtained from SLAM-based point clouds is still an open question. In this paper, we propose a preliminary evaluation on the feasibility of extracting as-built BIM from (i) a point cloud acquired with a commercial portable MMS, and (ii) a point cloud obtained through an open-source SLAM algorithm, surveying the environment with an autonomous mobile robotic platform. In both cases, the main structural elements of the test site are accurately generated, thus showing promising results. On the other hand, the experiment highlights also the need for SLAM systems capable of providing less noisy point clouds, in order to capture and model architectural details.

Developing a Machine Learning Algorithm for Service Robots in Industrial Applications

Robots, which have mostly been effective in areas such as industrial, agricultural, and production facilities, have started to take a place in the service sector, as their technologies have become lower in cost and more easily accessible. This situation has attracted the attention of companies and researchers and has accelerated studies on this subject. In this study, an algorithm was developed for the autonomous mobile robot to serve in industrial areas. In line with this study, it was ensured that the autonomous mobile robot mapped the working environment, determined the working station in this environment, and then carried out transport operations between these working stations in accordance with a given work order. After the mobile robot fulfilled the work order, it went into a waiting state until a new work order was received. For the mobile robot to save energy, it was ensured that it waited close to the point where the work order came in the most, by means of machine learning in the waiting position. The developed algorithms were designed using the NI LabVIEW environment and then simulated in the RobotinoSIM environment and physically tested using the Robotino autonomous mobile robot platform. The experimental results showed that mapping and location reporting using an RGB camera, odometry, and a QR code eliminated permanent location errors, and the robot completed 50 work orders with 100% accuracy.

Development and Verification of an Autonomous and Controllable Mobile Robot Platform

In this paper, we design a mobile robot platform, which employs a fully autonomous mechanical structure and electrical control system. Two driving wheels realize flexible steering movement with four universal wheels. A variety of sensors are built on the mobile robot platform, including the Inertial Measurement Unit (IMU) used to establish the inertial navigation coordinate system and the Velodyne’s Puck lidar sensor (VLP-16) used to obtain the three-dimensional (3D) point cloud information of the environment. Then, we build a software control architecture based on the Robot Operating System (ROS), using multi-node communication to perform positioning, environment perception, dynamic obstacle avoidance, path planning and motion control. Furthermore, a method of actively exploring the environment and constructing a map is proposed, using multi-path evaluation for real-time path planning and obstacle avoidance. In the end, we conduct autonomous exploration experiments to verify the performance of the designed mobile robot platform in indoor multi-obstacle scenes.

Integrated Mobile Robotic Platform Model

Introduction. The “Smart Agroˮ committee of Research and Education Center “Engineering of the Future” has identified a number of tasks relevant for improving the efficiency of precision, soil-protecting and conservation agriculture. One of these tasks is the development of a digital multi-agent system, which provides a number of services for agricultural enterprises, developers and manufacturers of agricultural machinery. The purpose of the present study is to model an autonomous mobile robotic platform, including the development of software and hardware for trajectory control. Materials and Methods. To solve the problem, there are used modern CAx systems and their applications, the methods of 3D and full-body modeling, and the method of numerical solution of problems in solid mechanics. To expand and improve the standard functionality of CAx-systems (SolidWorks) in the software implementation of trajectory control algorithms, the methods and technologies of programming using API SolidWorks, VisualStudio C++ (MFC, ATL, COM) are used, and to build physical full-scale models ‒ Arduino and fischertechnik platforms. Results. The result of the study is a software and hardware module of trajectory control for an integrated (physical and virtual) model of a mobile robotic platform, which can be provided to the consumer as a service for technology autonomation. For the developed integrated model, control algorithms for various types of trajectories were tested. Discussion and Conclusion. The developed integrated software and hardware model of trajectory control can be used by developers and manufacturers of agricultural machinery, and directly by agro-enterprises for implementing typical technological processes. A feature of the implementation is an open hardware and software interface that provides the integration of mobile robotic platforms based on a digital multi-agent system.

Prototyping Using a Mobile Robot Platform Equipped with Low-End In-Wheel Motors

In the near future, autonomous mobile robots are expected to operate effectively in various locations, such as in living spaces as well as industrial establishments. Against this background, a new autonomous mobile robot platform was designed and prototyped in this research. For simplicity of design and easy assembly of the drive units, a robot with two low-end in-wheel motors is considered. It is also effective in saving space, and can be used for high-power operations and travelability in various road surface conditions. This paper presents a concept for developing a new type of autonomous mobile robot platform, its control system for autonomous operation, actual prototyping using this platform, and sample applications of this platform.

Exploration of Indoor Barrier-Free Plane Intelligent Lofting System Combining BIM and Multi-Sensors

Lofting is an essential part of construction projects and the high quality of lofting is the basis of efficient construction. However, the most common method of lofting currently which uses the total station in a multi-person cooperative way consumes much manpower and time. With the rapid development of remote sensing and robot technology, using robots instead of manpower can effectively solve this problem, but few scholars study this. How to effectively combine remote sensing and robots with lofting is a challenging problem. In this paper, we propose an intelligent lofting system for indoor barrier-free plane environment, and design a high-flexibility, low-cost autonomous mobile robot platform based on single chip microcomputer, Micro Electro Mechanical Systems-Inertial Measurement Unit (MEMS-IMU), wheel encoder, and magnetometer. The robot also combines Building Information Modeling (BIM) laser lofting instrument and WIFI communication technology to get its own position. To ensure the accuracy of localization, the kinematics model of Mecanum wheel robot is built, and Extended Kalman Filter (EKF) is also used to fuse multi-sensor data. It can be seen from the final experimental results that this system can significantly improve lofting efficiency and reduce manpower.

Prototype and Experiment of Middle-Sized Autonomous Mobile Robot Platform through Multi-Laboratory Collaboration

Prototype and Experiment of Middle-Sized Autonomous Mobile Robot Platform through Multi-Laboratory Collaboration

Development of an autonomous mobile robot platform for cleaning in drainage groove

Development of an autonomous mobile robot platform for cleaning in drainage groove

Evaluation of the performance of PoultryBot, an autonomous mobile robotic platform for poultry houses

Assessment of animal status, housing conditions and manually collecting floor eggs are the major daily tasks for poultry farmers. To assist the farmer in these tasks, PoultryBot, an autonomous mobile robot for use in poultry houses has been developed. In earlier research, several components of PoultryBot were discussed in detail. Here, performance of the robot is evaluated under practical conditions. For navigation, different paths were used to assess its navigation performance for various tasks, such as area sweeping and surveying close to walls. PoultryBot proved capable of navigating autonomously more than 3000 m, while avoiding obstacles and dealing with the hens present. The robustness of its navigation performance was tested by confronting PoultryBot with obstacles in different positions with respect to its path and using different settings of the navigation parameters. Both factors clearly influenced the driving behaviour of PoultryBot. For floor egg collection, detection and collection of eggs was assessed at 5 predefined egg positions lateral to the path of the robot. Over 300 eggs were tested; 46% were collected successfully, 37% was not collected successfully, and 16% were missed. The most observed failures occurred when the collection device was just next to the egg. It is thought that this problem can be solved by improving the control algorithm. The results demonstrate the validity of the PoultryBot concept and the possibility of autonomous floor egg collection in commercial poultry houses. Furthermore, they indicate that application of smart autonomous vehicles in dense animal environments is feasible.

High‐precision control of tracked field robots in the presence of unknown traction coefficients

AbstractAccurate steering through crop rows that avoids crop damage is one of the most important tasks for agricultural robots utilized in various field operations, such as monitoring, mechanical weeding, or spraying. In practice, varying soil conditions can result in off‐track navigation due to unknown traction coefficients so that it can cause crop damage. To address this problem, this paper presents the development, application, and experimental results of a real‐time receding horizon estimation and control (RHEC) framework applied to a fully autonomous mobile robotic platform to increase its steering accuracy. Recent advances in cheap and fast microprocessors, as well as advances in solution methods for nonlinear optimization problems, have made nonlinear receding horizon control (RHC) and receding horizon estimation (RHE) methods suitable for field robots that require high‐frequency (milliseconds) updates. A real‐time RHEC framework is developed and applied to a fully autonomous mobile robotic platform designed by the authors for in‐field phenotyping applications in sorghum fields. Nonlinear RHE is used to estimate constrained states and parameters, and nonlinear RHC is designed based on an adaptive system model that contains time‐varying parameters. The capabilities of the real‐time RHEC framework are verified experimentally, and the results show an accurate tracking performance on a bumpy and wet soil field. The mean values of the Euclidean error and required computation time of the RHEC framework are equal to 0.0423 m and 0.88 ms, respectively.

Development of Autonomous Anthropomorphic Wheeled Mobile Robotic Platform

This article presents the intelligent autonomous anthropomorphic wheeled mobile robotic platform motion control in unstructured environments. The fuzzy control of a wheeled autonomous anthropomorphic mobile robotic platform motion in unstructured environments with obstacles is proposed. Outputs of the fuzzy controller are the angular speed difference between the left and right wheels of the autonomous anthropomorphic robotic platform and the robot velocity. The simulation results show the effectiveness and the validity of the obstacle avoidance behaviour in the unstructured environment and velocity control of autonomous anthropomorphic mobile robotic platform motion of the proposed fuzzy control strategy. Wireless sensor-based remote control of autonomous anthropomorphic mobile robotic platform motion in unstructured environments is proposed.

Development for General-purpose Autonomous Mobile Robot Platform

Development for General-purpose Autonomous Mobile Robot Platform

Prototyping of Middle-Sized Autonomous Mobile Robot Platform through Multi-Laboratory Collaboration

Prototyping of Middle-Sized Autonomous Mobile Robot Platform through Multi-Laboratory Collaboration

Development of an autonomous mobile robot platform aimed at cleaning inside the drainage groove

Development of an autonomous mobile robot platform aimed at cleaning inside the drainage groove