Position Of Mobile Robot Research Articles

A novel algorithm based on nonlinear optimization for parameters calibration of wheeled robot mobile chasses

Mobile robot positioning, mapping, and navigation systems generally employ an inertial measurement unit to obtain the acceleration and angular velocity of the robot. However, errors in the internal and external parameters of an inertial measurement unit arising from defective calibration directly affect the accuracy of robot positioning and pose estimation. While this issue has been addressed by the mature internal parameters calibration method available for inertial measurement unit, external reference calibration method between the inertial measurement unit and the chassis of a mobile robot are lacking. This study addresses this issue by proposing a novel algorithm for internal parameter calibration of mecanum wheel omnidirectional mobile platform and external parameter calibration of mecanum chassis- inertial measurement unit based on principal component analysis and nonlinear optimization, which is designed for robots equipped with cameras, inertial measurement unit, mecanum wheels, and wheel speed odometers, and functions under the premise of accurate calibrations for the internal parameters of the inertial measurement unit and the internal and external parameters of the camera. All of the internal and external parameters calibrations are conducted using the robot's existing equipment without the need for additional calibration aids. The feasibility of the method is verified by its application to a mecanum wheel omnidirectional mobile platform. The proposed calibration method is thereby demonstrated to guarantee the accuracy of robot pose estimation.

Study on influence of road surface on estimation accuracy of a relative position of multiple omnidirectional mobile robots

Study on influence of road surface on estimation accuracy of a relative position of multiple omnidirectional mobile robots

Soccer‐Assisted Training Robot Based on Image Recognition Omnidirectional Movement

With the continuous emergence and innovation of computer technology, mobile robots are a relatively hot topic in the field of artificial intelligence. It is an important research area of more and more scholars. The core of mobile robots is to be able to realize real‐time perception of the surrounding environment and self‐positioning and to conduct self‐navigation through this information. It is the key to the robot’s autonomous movement and has strategic research significance. Among them, the goal recognition ability of the soccer robot vision system is the basis of robot path planning, motion control, and collaborative task completion. The main recognition task in the vision system is the omnidirectional vision system. Therefore, how to improve the accuracy of target recognition and the light adaptive ability of the robot omnidirectional vision system is the key issue of this paper. Completed the system construction and program debugging of the omnidirectional mobile robot platform, and tested its omnidirectional mobile function, positioning and map construction capabilities in the corridor and indoor environment, global navigation function in the indoor environment, and local obstacle avoidance function. How to use the local visual information of the robot more perfectly to obtain more available information, so that the “eyes” of the robot can be greatly improved by relying on image recognition technology, so that the robot can obtain more accurate environmental information by itself has always been domestic and foreign one of the goals of the joint efforts of scholars. Research shows that the standard error of the experimental group’s shooting and dribbling test scores before and the experimental group’s shooting and dribbling test results after the standard error level is 0.004, which is less than 0.05, which proves the use of soccer‐assisted robot‐assisted training. On the one hand, we tested the positioning and navigation functions of the omnidirectional mobile robot, and on the other hand, we verified the feasibility of positioning and navigation algorithms and multisensor fusion algorithms.

Порівняння точності позиціювання методом трилатерації та методом на базі генетичного алгоритму

The article compares the accuracy of mobile robot positioning by the technique based on genetic algorithms, which are related to artificial intelligence, and by the trilateration technique. The authors consider the application of appropriate terminology borrowed from genetics and data processing algorithms for this technical problem. When using the genetic algorithm, the coordinates of the robot are found using angular methods or rigid logic methods, which are not particularly effective because of the large amount of data that is not needed for positioning, so there is a need to select the most likely indicators to find the best route to the target. The genetic algorithm used in this study first selects the data by a certain criterion to enter the first population, and then the data falls into the beginning of the genetic algorithm. Each individual has chromosomes that represent a sequence of data, i.e., genes. After a chromosome is coded, the following genetic operations are performed: crossing over and mutation. These operations occur cyclically until a population with high fitness is found. The solution is a sequence of selected coordinates, from which a system is constructed to determine the optimal route to the destination. The robot navigation techniques are compared in terms of coordinate positioning accuracy. Calculation results on dispersion and absolute positioning error show that the positioning using genetic algorithm gives less error than the one using trilateration method. The genetic algorithm allows finding the optimal solution of the positioning problem while reducing a significant influence of the measurement error of sensors and other measuring devices on the result.

Rapid Development of a Mobile Robot for the Nakanoshima Challenge Using a Robot for Intelligent Environments

Automated mobile platforms are commonly used to provide services for people in an intelligent environment. Data on the physical position of personal electronic devices or mobile robots are important for information services and robotic applications. Therefore, automated mobile robots are required to reconstruct location data in surveillance tasks. This paper describes the development of an autonomous mobile robot to achieve tasks in intelligent environments. In particular, the robot constructed route maps in outdoor environments using laser imaging detection and ranging (LiDAR), and RGB-D sensors via simultaneous localization and mapping. The mobile robot system was developed based on a robot operating system (ROS), reusing existing software. The robot participated in the Nakanoshima Challenge, which is an experimental demonstration test of mobile robots in Osaka, Japan. The results of the experiments and outdoor field tests demonstrate the feasibility of the proposed robot system.

Mobile Robot Position Controlling System Based On IoT Through Raspberry Pi

In IoT communication technologies, interconnected computing devices, mechanical and digital machnies, objects, animals or unique identifiers (UIDs) can transmit data over network without the need for people or networks. Increasingly, organizations in various industries use IoT technology to work more efficiently, provide better customer service, improve decision-making and increase business value. IoT devices share the collected sensor data by connecting them to an IoT gateway or to a terminal other than the cloud from which the data is sent to the cloud for local analysis or analyzed locally. Sometimes, these devices communicate with other related devices and act on the information they receive from each other. Devices do most of the work without human intervention, but people can interact with devices, such as setting them up, giving instructions, or accesing data. In this study, a control panel was created using HTML and by using this control panel, IoT based mobile robot position control was performed on Raspberry Pi. The control of this mobile robot is entirely IoT-based, and it is controlled via the control panel on the web page from anywhere with Internet access.

Research on Mobile Robot Positioning and Navigation System Based on Multi-sensor Fusion

The mobile robot integrates walking and operation functions, which can greatly expand the application range of traditional industrial robots. Aiming at the shortcomings of single sensor positioning under unknown indoor environment, such as large accumulated error and environmental limitations, a multi-sensor fusion positioning navigation system is designed to improve the positioning accuracy of autonomous navigation of mobile robots. The mobile robot uses the laser sensor as the core sensor to obtain two-dimensional information of the environment and realize real-time display of algorithms and interfaces such as map construction, positioning and navigation. The data results of the indoor navigation experiment show that the system has good robustness. At the same time, the feasibility and reliability of the system are verified.

Time-Relative RTK-GNSS: GNSS Loop Closure in Pose Graph Optimization

A pose-graph-based optimization technique is widely used to estimate robot poses using various sensor measurements from devices such as laser scanners and cameras. The global navigation satellite system (GNSS) has recently been used to estimate the absolute 3D position of outdoor mobile robots. However, since the accuracy of GNSS single-point positioning is only a few meters, the GNSS is not used for the loop closure of a pose graph. The main purpose of this study is to generate a loop closure of a pose graph using a time-relative real-time kinematic GNSS (TR-RTK-GNSS) technique. The proposed TR-RTK-GNSS technique uses time differential carrier phase positioning, which is based on carrier-phase-based differential GNSS with a single GNSS receiver. Unlike a conventional RTK-GNSS, we can directly compute the robot's relative position using only a stand-alone GNSS receiver. The initial pose graph is generated from the accumulated velocity computed from GNSS Doppler measurements. To reduce the accumulated error of velocity, we use the TR-RTK-GNSS technique for the loop closure in the graph-based optimization framework. The kinematic positioning tests were performed using an unmanned aerial vehicle to confirm the effectiveness of the proposed technique. From the tests, we can estimate the vehicle's trajectory with approximately 3 cm accuracy using only a stand-alone GNSS receiver.

SLAM System Based on Tightly Coupled Visual-inertial

Aiming at the problem of mobile robot positioning, IMU information is used to improve the performance of visual SLAM. Firstly, instead of the calculation of feature descriptor, LK optical flow is used in the processing of visual information, to increase the speed of feature extraction. And detectors are used to remove outliers. Secondly, the visual information and IMU information are initialized separately to obtain the camera pose and IMU pose. And then Visual-inertia joint initialization is conducted by tightly coupling of the IMU information and the visual information, to improve the calculation. Finally, the pose state estimation is achieved through back-end optimization. The validity of the proposed algorithm is verified by the experiments.

Mobile robot position control in environment with static obstacles

This study addresses the problem of efficiently navigating a differential drive mobile robot to a target pose in a region with obstacles, without explicitly generating a trajectory. The robot is assumed to be equipped with an omnidirectional range sensor, while the region may or may not be a priori known. Given the known obstacles in each iteration of the controller, the shortest path connecting the robot and the target point provides a raw desired movement direction. Considering the unobstructed area in that direction, the size of the robot and the obstacle contours in its visibility range, the reference direction is determined. Finally, respecting the velocity and acceleration constraints of the robot, the angular velocity is properly selected to rotate the robot towards the reference direction, while the linear velocity is chosen to efficiently minimise the distance to the final target, as well as to avoid collisions. After the robot has reached the target, the controller switches to orientation mode in order to fix the orientation. Experimental studies demonstrate the effectiveness of the algorithm.

Position and Posture Measurements Using Laser Projection Markers for Infrastructure Inspection

Infrastructure such as roads, tunnels and bridges needs periodical inspection. The conventional structure inspection method, in which a human inspector uses a crack gauge, may lead to problems such as measurement errors and lengthy inspection times. Mobile robots and image processing have been used in the infrastructure inspection field. An image sensor or any sensor is used for measuring the state of the infrastructures. It is necessary that the mobile robot knows its own position and posture during automatic inspection. Generally, the Global Positioning System (GPS) has been used to sense the position of the mobile robot. However, GPS is not usable in places with the ceilings such as under bridges and in tunnels. Therefore, we developed a system to sense the position and posture of mobile robots. This system uses laser projection markers and cameras, and it has a very simple configuration. The camera photographs a target structure and projection laser markers, and the position and posture of the camera and mobile robot were calculated by an image application. The system makes infrastructure inspection more effective and decreases the time needed for inspection. In this paper, we examined the number of necessary laser markers, and we verified our method by experiment.

Obstacle-avoiding intelligent algorithm for quad wheel robot path navigation

PurposeThe purpose of this work is to propose quad wheel robot with path navigation using an intelligent novel algorithm named as obstacle-avoiding intelligent algorithm (OAIA).Design/methodology/approachThe paper proposes OAIA algorithm, which is used to minimize the path distance and elapsed time between source and goal.FindingsThe hardware implementation of the Quad Wheel Robot design includes a global positioning system (GPS) module for path navigation. An ultrasonic module (HC SR04) is mainly used as the sensing unit for the system. In the proposed scheme, the GPS locator (L80) is used to obtain the current location of the robot, and the ultrasonic sensor is utilized to avoid the obstacles. An ARM processor serves as the heart of the Quad Wheel Robot.Practical implicationsThis paper includes real-time implementation of quad wheel robot for various coordinate values, and the movement of the robot is captured and analysed.Originality/valueThe proposed OAIA is capable of estimating the mobile robot position exactly under ideal circumstances. Simulation and hardware implementation are carried out to evaluate the performance of the proposed system.

옥외용 이동로봇의 분산점 칼만 필터 기반 3차원 위치 인식

This paper proposes a practical method, for evaluating 3-D positioning of outdoor mobile robots using the Unscented Kalman Filter (UKF). The UKF method does not require the linearization process unlike conventional EKF localization, so it can minimize effects of errors caused by linearization of non-linear models for position estimation. Also, this method does not require Jacobian calculations difficult to calculate in the actual implementation. The 3-D position of the robot is predicted using an encoder and tilt sensor, and the optimal position is estimated by fusing these predicted positions with the GPS and digital compass information. Experimental results revealed the proposed method is stable for localization of the 3D position regardless of initial error size, and observation period.

Localization and Mapping Algorithm for the Indoor Mobile Robot Based on LIDAR

In order to realize the autonomous Localization and navigation of indoor robots, this paper designs an indoor mobile robot positioning and mapping system based on LIDAR, and proposes a kind of localization and mapping task based on the improved RBPF-SLAM algorithm. This method mainly solves the deficiency in the original RBPF-SLAM algorithm from the aspect of improving the proposed distribution and limiting the number of resampling, making it more suitable for indoor mobile robot localization and mapping. Finally, the accuracy and rationality of the robot localization and mapping based on the RBPFSLAM algorithm based on LIDAR are verified by experiments.

Porównanie metod pozycjonowania robota mobilnego w terenie

Porównanie metod pozycjonowania robota mobilnego w terenie

Control Position of Mobile Robot Based on Odometry Method and PID Controller

A mobile robot had been made based on kinematics drive differential concept. This robot used odometry method in order to reach a position and determine its current position. Proportional Integral Derivative (PID) control has been applied for improving response of robot while it reach a detremined position. Two rotary encoder sensors were attached at right and left side of robot motors for measuring distance. While motor rotating, rotary encoder sensors counted a number of pulses conversed into left and right wheel mileage. Both of them were used to get robot position at certain time (x, y, and heading angle). Robot passed some tracks and reached two positions in each track as destination positions. These positions are the position which had final heading 30° and 90°on first track, 45° and 90° on second track, 60° and 90° on third track. PID control was examined by put mobile robot in first position 0,0,0°. Some PID parameters have been applied to examine the performance and response of robot. The increment of derivative parameter reduced the steady state error and increased the stability of system.

Position control of a mobile robot using reinforcement learning

Abstract Robotics has been introduced in education at all levels during the last years. In particular, the application of mobile robots for teaching automatic control is becoming more popular in engineering because of the attractive experiments that can be performed. This paper presents the design, development, and implementation of an algorithm to control the position of a wheeled mobile robot using Reinforcement Learning in an advanced 3D simulation environment. In this approach, the learning process occurs when the agent makes some actions in the environment to get some rewards. Trying to make a balance between the new information of the environment and the current knowledge about it. In this way, the algorithm is divided into two phases: 1) the learning stage, and 2) the operational stage. In the first stage, the robot learns how to reach a known destination point from its current position. To do it, it uses the information of the environment and the rewards, to build a learning matrix that is used later during the operational stage. The main advantage of this algorithm concerning traditional control algorithms is that the learning process is carried out automatically with a recursive procedure and the result is a controller that can make the specific task, without the need for a dynamic model. Its main drawback is that the learning stage can take a long time to finish and it depends on the hardware resources of the computer used during the learning process.

Reinforcement Learning for Position Control Problem of a Mobile Robot

Due to the increase in complexity in autonomous vehicles, most of the existing control systems are proving to be inadequate. Reinforcement Learning is gaining traction as it is posed to overcome these difficulties in a natural way. This approach allows an agent that interacts with the environment to get rewards for appropriate actions, learning to improve its performance continuously. The article describes the design and development of an algorithm to control the position of a wheeled mobile robot using Reinforcement Learning. One main advantage of this approach concerning traditional control algorithms is that the learning process is carried out automatically with a recursive procedure forward in time. Moreover, given the fidelity of the model for the particular implementation described in this work, the whole learning process can be carried out in simulation. This fact avoids damages to the actual robot during the learning stage. It shows that the position control of the robot (or similar specific tasks) can be done without the need to know the dynamic model of the system explicitly. Its main drawback is that the learning stage can take a long time to finish and that it depends on the complexity of the task and the availability of adequate hardware resources. This work provides a comparison between the proposed approach and traditional existing control laws in simulation and real environments. The article also discusses the main effects of using different controlled variables in the performance of the developed control law.

On selection of optimal stop position and spatial orientation of mobile robot

The paper analyses the problem of finding optimal coordinates of a mobile robot equipped with a manipulator. An optimization criterion is proposed and validated, and a method for calculating most rational coordinates of the robot is described.

Vision-based Indoor Localization Algorithm using Improved ResNet

The output of the residual network fluctuates greatly with the change of the weight parameters, which greatly affects the performance of the residual network. For dealing with this problem, an improved residual network is proposed. Based on the classical residual network, batch normalization, adaptive -dropout random deactivation function and a new loss function are added into the proposed model. Batch normalization is applied to avoid vanishing/exploding gradients. -dropout is applied to increase the stability of the model, which we select different dropout method adaptively by adjusting parameter. The new loss function is composed by cross entropy loss function and center loss function to enhance the inter class dispersion and intra class aggregation. The proposed model is applied to the indoor positioning of mobile robot in the factory environment. The experimental results show that the algorithm can achieve high indoor positioning accuracy under the premise of small training dataset. In the real-time positioning experiment, the accuracy can reach 95.37.