Encoderless position estimation and error correction techniques for miniature mobile robots

This paper presents an encoderless position estimation technique for miniature-sized mobile robots. Odometry techniques, which are based on the hardware components, are commonly used for calculating the geometric location of mobile robots. Therefore, the robot must be equipped with an appropriate sensor to measure the motion. However, due to the hardware limitations of some robots, employing extra hardware is impossible. On the other hand, in swarm robotic research, which uses a large number of mobile robots, equipping the robots with motion sensors might be costly. In this study, the trajectory of the robot is divided into several small displacements over short spans of time. Therefore, the position of the robot is calculated within a short period, using the speed equations of the robot's wheel. In addition, an error correction function is proposed that estimates the errors of the motion using a current monitoring technique. The experiments illustrate the feasibility of the proposed position estimation and error correction techniques to be used in miniature-sized mobile robots without requiring an additional sensor

人間を模擬した知覚系を持つ2足ヒューマノイドロボットの自己位置推定機能に対する歩行動作の影響に関する研究

早大学位記番号:新8279早稲田大

Location of a Mobile Robot using Odometry in the DMF

La Universidad de Ciencias Aplicadas de Viena, cuenta con una fábrica digital en miniatura en la que se puede realizar la investigación, desarrollo e implementación de las diferentes tecnologías de la industria 4.0. Esta fábrica tiene varias estaciones de trabajo y un robot móvil que se mueve entre ellas para hacer llegar al cliente las piezas pedidas correspondientes del mosquetón. El tema principal de este trabajo fin de grado es el desarrollo de un procedimiento por el cual se pueda obtener la localización del robot calculando sus coordenadas y su ángulo; todo ello con el objetivo de integrarlo en la fábrica miniaturizada de la universidad. El método que se usará para conocer la posición y orientación del robot estará basado en la odometría de un robot diferencial. El control del robot se realizará mediante el puerto serie de Arduino o mediante Thing Worx, enviando los comandos necesarios para su movimiento. La pose (posición en coordenadas y orientación) del robot será enviada al servidor central haciendo uso de la comunicación IoT, donde se podrán visualizar y hacer uso para otros trabajos.The University of Applied Sciences Technikum Wien has a digital miniature factory in which it can be done the research, development and implementation of different technologies related with the 4.0 industry. This miniature factory has several working stations and a mobile robot that moves between them in order to deliver the corresponding carabiner parts ordered by the supposed customer. The main subject of this final bachelor project is the development of a procedure by which the localization of the mobile robot can be obtained by calculating its coordinates and angle; all with the aim of integrating it into the miniaturised factory of the university. The method to be used to know the position and orientation of the robot will be based on the wheel odometry of a differential robot. The control of the robot is done through the serial port of the Arduino or through ThingWorx, sending the necessary commands to make it moves. The pose (position in coordinates and orientation) of the robot will be sent to the central server using IoT communication, where it can be visualised and used for other projects.Departamento de Tecnología ElectrónicaGrado en Ingeniería en Electrónica Industrial y Automátic

Design of an adaptive neural predictive nonlinear controller for nonholonomic mobile robot system based on posture identifier in the presence of disturbance

This paper proposes an adaptive neural predictive nonlinear controller to guide a nonholonomic wheeled mobile robot during continuous and non-continuous gradients trajectory tracking. The structure of the controller consists of two models that describe the kinematics and dynamics of the mobile robot system and a feedforward neural controller. The models are modified Elman neural network and feedforward multi-layer perceptron respectively. The modified Elman neural network model is trained off-line and on-line stages to guarantee the outputs of the model accurately represent the actual outputs of the mobile robot system. The trained neural model acts as the position and orientation identifier. The feedforward neural controller is trained off-line and adaptive weights are adapted on-line to find the reference torques, which controls the steady-state outputs of the mobile robot system. The feedback neural controller is based on the posture neural identifier and quadratic performance index optimization algorithm to find the optimal torque action in the transient state for N-step-ahead prediction. General back propagation algorithm is used to learn the feedforward neural controller and the posture neural identifier. Simulation results show the effectiveness of the proposed adaptive neural predictive control algorithm; this is demonstrated by the minimised tracking error and the smoothness of the torque control signal obtained with bounded external disturbances

Design and Implementation of Indoor Disinfection Robot System

After the outbreak of COVID-19 virus, disinfection has become one of the important means of epidemic prevention. Traditional manual disinfection can easily cause cross infection problems. Using robots to complete disinfection work can reduce people's social contact and block the spread of viruses. This thesis implements an engineering prototype of a indoor disinfection robot from the perspective of product development, with the amin of using robots to replace manual disinfection operations. The thesis uses disinfection module, control module and navigation module to compose the hardware of the robot. The disinfection module uses ultrasonic atomizers, UV-C ultraviolet disinfection lamps, and air purifiers to disinfect and disinfect the ground and air respectively. The control module is responsible for the movement and obstacle avoidance of the robot. The navigation module uses Raspberry Pi and LiDAR to achieve real-time robot positioning and two-dimensional plane mapping. In terms of robot software,we have done the following work: (1) Based on the ROS framework, we have implemented functions such as SLAM mapping, location positioning, and odometer data calibration.(2) Customize communication protocols to manage peripheral devices such as UV-C lights, ultrasonic atomizers, air purifiers, and motors on the control board. (3) Develop an Android mobile app that utilizes ROSBridge's lightweight communication architecture to achieve cross platform data exchange between mobile devices and navigation boards, as well as network connectivity and interaction between mobile phones and robots Finally, this thesis implements an engineering prototype of a household disinfection robot from the perspective of product development

An Improved Approach For Multi-Robot Localization

Cooperative multi-robot localization techniques use sensor measurements to estimate poses (locations, orientations) of robots relative to a given map of the environment. Existing approaches update a robot\u27s pose instantly whenever it detects another robot. However, such instant update may not be always necessary and effective, since both robots\u27 pose estimates could be highly uncertain at the time of the detection. In this thesis, we develop a new information exchange mechanism to collaborative multi-robot localization. We also propose a new scheme to calculate how much information is contained in a robot\u27s belief by using entropy. Instead of updating beliefs whenever detection occurs, our approach first compares the beliefs of the robots which are involved in the detection, and then decide whether the information exchange is necessary. Therefore, it avoids unnecessary information exchange whenever one robot perceives another robot. On the other hand, this approach does allow information exchange between detecting robots and such information exchange always contributes positively to the localization process, hence, improving the effectiveness and efficiency of multi-robot localization. The technique has been implemented and tested using two mobile robots as well as simulations. The results indicate significant improvements in localization speed and accuracy when compared to the single mobile robot localization

PID-based with Odometry for Trajectory Tracking Control on Four-wheel Omnidirectional Covid-19 Aromatherapy Robot

Inhalation therapy is one of the most popular treatments for many pulmonary conditions. The proposed Covid-19 aromatherapy robot is a type of Unmanned Ground Vehicle (UGV) mobile robot that delivers therapeutic vaporized essential oils or drugs needed to prevent or treat Covid-19 infections. It uses four omnidirectional wheels with a controlled speed to possibly move in all directions according to its trajectory. All motors for straight, left, or right directions need to be controlled, or the robot will be off-target. The paper presents omnidirectional four-wheeled robot trajectory tracking control based on PID and odometry. The odometry was used to obtain the robot's position and orientation, creating the global map. PID-based controls are used for three purposes: motor speed control, heading control, and position control. The omnidirectional robot had successfully controlled the movement of its four wheels at low speed on the trajectory tracking with a performance criterion value of 0.1 for the IAEH, 4.0 for MAEH, 0.01 for RMSEH, 0.00 for RMSEXY, and 0.06 for REBS. According to the experiment results, the robot's linear velocity error rate is 2%, with an average test value of 1.3 percent. The robot heading effective error value on all trajectories is 0.6%. The robot's direction can be monitored and be maintained at the planned trajectory. Doi: 10.28991/esj-2021-SPER-13 Full Text: PD

Using a Combination of PID Control and Kalman Filter to Design of IoT-based Telepresence Self-balancing Robots during COVID-19 Pandemic

COVID-19 is a very dangerous respiratory disease that can spread quickly through the air. Doctors, nurses, and medical personnel need protective clothing and are very careful in treating COVID-19 patients to avoid getting infected with the COVID-19 virus. Hence, a medical telepresence robot, which resembles a humanoid robot, is necessary to treat COVID-19 patients. The proposed self-balancing COVID-19 medical telepresence robot is a medical robot that handles COVID-19 patients, which resembles a stand-alone humanoid soccer robot with two wheels that can maneuver freely in hospital hallways. The proposed robot design has some control problems; it requires steady body positioning and is subjected to disturbance. A control method that functions to find the stability value such that the system response can reach the set-point is required to control the robot's stability and repel disturbances; this is known as disturbance rejection control. This study aimed to control the robot using a combination of Proportional-Integral-Derivative (PID) control and a Kalman filter. Mathematical equations were required to obtain a model of the robot's characteristics. The state-space model was derived from the self-balancing robot's mathematical equation. Since a PID control technique was used to keep the robot balanced, this state-space model was converted into a transfer function model. The second Ziegler-Nichols's rule oscillation method was used to tune the PID parameters. The values of the amplifier constants obtained were Kp=31.002, Ki=5.167, and Kd=125.992128. The robot was designed to be able to maintain its balance for more than one hour by using constant tuning, even when an external disturbance is applied to it. Doi: 10.28991/esj-2021-SP1-016 Full Text: PD