Journal articles: 'Wall following robot'

1

Sang, Ash Wan Yaw, Chee Gen Moo, S. M. Bhagya P. Samarakoon, M. A. Viraj J. Muthugala, and Mohan Rajesh Elara. "Design of a Reconfigurable Wall Disinfection Robot." Sensors 21, no. 18 (September 11, 2021): 6096. http://dx.doi.org/10.3390/s21186096.

Full text

Abstract:

During a viral outbreak, such as COVID-19, autonomously operated robots are in high demand. Robots effectively improve the environmental concerns of contaminated surfaces in public spaces, such as airports, public transport areas and hospitals, that are considered high-risk areas. Indoor spaces walls made up most of the indoor areas in these public spaces and can be easily contaminated. Wall cleaning and disinfection processes are therefore critical for managing and mitigating the spread of viruses. Consequently, wall cleaning robots are preferred to address the demands. A wall cleaning robot needs to maintain a close and consistent distance away from a given wall during cleaning and disinfection processes. In this paper, a reconfigurable wall cleaning robot with autonomous wall following ability is proposed. The robot platform, Wasp, possess inter-reconfigurability, which enables it to be physically reconfigured into a wall-cleaning robot. The wall following ability has been implemented using a Fuzzy Logic System (FLS). The design of the robot and the FLS are presented in the paper. The platform and the FLS are tested and validated in several test cases. The experimental outcomes validate the real-world applicability of the proposed wall following method for a wall cleaning robot.

APA, Harvard, Vancouver, ISO, and other styles

2

Suherman, Aan. "Fire Search and Obstcale Avoidance Robot." Telekontran : Jurnal Ilmiah Telekomunikasi, Kendali dan Elektronika Terapan 3, no. 2 (July 22, 2015): 37–46. http://dx.doi.org/10.34010/telekontran.v3i2.1881.

Full text

Abstract:

Abstract - Fire search and obstacle avoidance robot are types of mobile robots that can find targets in the form of fire by tracing walls. For this robot, the navigation system uses navigation using walls. Navigation using walls is an algorithm to guide robots by navigating along walls. This system works by adjusting the distance from the wall to the robot. If a change occurs, the robot moves to adjust the distance again. This robot consists of several main components to support it when navigating through walls to reach the target. This robot consists of a flame sensor placed on the front that serves as a detector for targets in the form of fire. In addition to the flame sensor, three ultrasonic sensors are located on the left, front and right of the robot. These three ultrasonic sensors function as wall detectors. Based on the test, the percentage of success of the robot reaches the target of fire by tracing the wall of the right side is 100% in room II, in room III 70%, in room IV 70% Whereas by tracing the left wall, the percentage of success in room II is 60%, in room III 70%, in room IV 100%. The success percentage of robots reaching the target with the right search method is 80% and the left is 76.667%. Keyword : Navigation wall following, obstacle avoidance robot, mobile robot, target search robot in the form of fire

APA, Harvard, Vancouver, ISO, and other styles

3

Toibero, Juan Marcos, Flavio Roberti, and Ricardo Carelli. "Stable contour-following control of wheeled mobile robots." Robotica 27, no. 1 (January 2009): 1–12. http://dx.doi.org/10.1017/s026357470800444x.

Full text

Abstract:

SUMMARYThis paper presents a continuous wall-following controller for wheeled mobile robots based on odometry and distance information. The reference for this controller is the desired distance from the robot to the wall and allows the robot to follow straight wall contour as well as smoothly varying wall contours by including the curvature of the wall into the controller. The asymptotic stability of the control system is proved using a Lyapunov analysis. The controller is designed so as to avoid saturation of the angular velocity command to the robot. A novel switching scheme is also proposed that allows the robot to follow discontinuous contours allowing the robotic system to deal with typical problems of continuous wall-following controllers such as open corners and possible collisions. This strategy overcomes these instances by switching between dedicated behavior-based controllers. The stability of the switching control system is discussed by considering Lyapunov concepts. The proposed control systems are verified experimentally in laboratory and office environments to show the feasibility and good performance of the control algorithms.

APA, Harvard, Vancouver, ISO, and other styles

4

Suwoyo, Heru, Yingzhong Tian, and Muhammad Hafizd Ibnu Hajar. "ENHANCING THE PERFORMANCE OF THE WALL-FOLLOWING ROBOT BASED ON FLC-GA." SINERGI 24, no. 2 (April 17, 2020): 141. http://dx.doi.org/10.22441/sinergi.2020.2.008.

Full text

Abstract:

Determination of the improper speed of the wall-following robot will produce a wavy motion. This common problem can be solved by adding a Fuzzy Logic Controller (FLC) to the system. The usage of FLC is very influential on the performance of the wall-following robot. Accuracy in the determination of speed is largely based on the setting of the membership function that becomes the value of its input. So manual setting on membership function can still be enhanced by approaching the certain optimization method. This paper describes an optimization method based on Genetic Algorithm (GA). It is used to improving the ability of FLC to control the wall-following robot controlled by FLC. To provide clarity, the wall-following robot that controlled using an FLC with manual settings will be simulated and compared with the performance of wall-following robots controlled by a fuzzy logic controller optimized by a Genetic Algorithm (FLC-GA). According to comparative results, the proposed method has been showing effectiveness in terms of stability indicated by a small error.

APA, Harvard, Vancouver, ISO, and other styles

5

Muthugala, M. A. Viraj J., S. M. Bhagya P. Samarakoon, Madan Mohan Rayguru, Balakrishnan Ramalingam, and Mohan Rajesh Elara. "Wall-Following Behavior for a Disinfection Robot Using Type 1 and Type 2 Fuzzy Logic Systems." Sensors 20, no. 16 (August 9, 2020): 4445. http://dx.doi.org/10.3390/s20164445.

Full text

Abstract:

Infectious diseases are caused by pathogenic microorganisms, whose transmission can lead to global pandemics like COVID-19. Contact with contaminated surfaces or objects is one of the major channels of spreading infectious diseases among the community. Therefore, the typical contaminable surfaces, such as walls and handrails, should often be cleaned using disinfectants. Nevertheless, safety and efficiency are the major concerns of the utilization of human labor in this process. Thereby, attention has drifted toward developing robotic solutions for the disinfection of contaminable surfaces. A robot intended for disinfecting walls should be capable of following the wall concerned, while maintaining a given distance, to be effective. The ability to operate in an unknown environment while coping with uncertainties is crucial for a wall disinfection robot intended for deployment in public spaces. Therefore, this paper contributes to the state-of-the-art by proposing a novel method of establishing the wall-following behavior for a wall disinfection robot using fuzzy logic. A non-singleton Type 1 Fuzzy Logic System (T1-FLS) and a non-singleton Interval Type 2 Fuzzy Logic System (IT2-FLS) are developed in this regard. The wall-following behavior of the two fuzzy systems was evaluated through simulations by considering heterogeneous wall arrangements. The simulation results validate the real-world applicability of the proposed FLSs for establishing the wall-following behavior for a wall disinfection robot. Furthermore, the statistical outcomes show that the IT2-FLS has significantly superior performance than the T1-FLS in this application.

APA, Harvard, Vancouver, ISO, and other styles

6

Larasati, Neta, Tresna Dewi, and Yurni Oktarina. "Object Following Design for a Mobile Robot using Neural Network." Computer Engineering and Applications Journal 6, no. 1 (March 1, 2017): 5–14. http://dx.doi.org/10.18495/comengapp.v6i1.189.

Full text

Abstract:

Deciding the best method for robot navigation is the most important tasks in mobile robot design, defined as the robot's ability to reach the target or/and move around its environment safely using the installed sensors and/or predefined map. To achieve this objective, wall or object detection can be considered. It is common to derive kinematics and dynamics to design the controls system of the robot, however by giving intelligence system to the robot, the control system will provide better performance for robot navigation. One of the most applied artificial intelligence is neural networks, a good approach for sensors of mobile robot system that is difficult to be modeled with an accurate mathematical equations. Mostly discussed basic navigation of a mobile robot is wall following. Wall following robot has been used for many application not only in industrial as a transport robot but also in domestic or hospital. Two behaviors are designed in this paper, wall following and object following. Object following behavior is developed from wall following by utilizing data from 4 installed distance sensors. The leader robot as the target for the follower robot, therefore the follower robot will keep on trying reaching for the leader in a safe distance. The novelty of this research is in the sense of the simplicity of proposed method. The feasibility of our proposed design is proven by simulation where all the results shows the effectiveness of the proposed method.

APA, Harvard, Vancouver, ISO, and other styles

7

Teng, Tey Wee, Prabakaran Veerajagadheswar, Balakrishnan Ramalingam, Jia Yin, Rajesh Elara Mohan, and Braulio Félix Gómez. "Vision Based Wall Following Framework: A Case Study With HSR Robot for Cleaning Application." Sensors 20, no. 11 (June 10, 2020): 3298. http://dx.doi.org/10.3390/s20113298.

Full text

Abstract:

Periodic cleaning of all frequently touched social areas such as walls, doors, locks, handles, windows has become the first line of defense against all infectious diseases. Among those, cleaning of large wall areas manually is always tedious, time-consuming, and astounding task. Although numerous cleaning companies are interested in deploying robotic cleaning solutions, they are mostly not addressing wall cleaning. To this end, we are proposing a new vision-based wall following framework that acts as an add-on for any professional robotic platform to perform wall cleaning. The proposed framework uses Deep Learning (DL) framework to visually detect, classify, and segment the wall/floor surface and instructs the robot to wall follow to execute the cleaning task. Also, we summarized the system architecture of Toyota Human Support Robot (HSR), which has been used as our testing platform. We evaluated the performance of the proposed framework on HSR robot under various defined scenarios. Our experimental results indicate that the proposed framework could successfully classify and segment the wall/floor surface and also detect the obstacle on wall and floor with high detection accuracy and demonstrates a robust behavior of wall following.

APA, Harvard, Vancouver, ISO, and other styles

8

Ando, Yoshinobu, Takashi Tsubouchi, and Shin’ichi Yuta. "A Reactive Wall Following Algorithm and Its Behavior of an Autonomous Mobile Robot with Sonar Ring." Journal of Robotics and Mechatronics 8, no. 1 (February 20, 1996): 33–39. http://dx.doi.org/10.20965/jrm.1996.p0033.

Full text

Abstract:

A robust wall following algorithm for an autonomous mobile robot with a sonar ring is presented. A sonar ring consists of multiple ultrasonic range sensors which are arranged on a disc. The proposed wall-following algorithm has an ability to make a robot follow walls in various shapes. The algorithm is described as a collection of reactions based on the data from the sonar ring. The autonomous mobile robot ""Yamabico"" is used to demonstrate the experimental behaviors of the proposed algorithm. Several experimental examples of the behaviors with this autonomous mobile robot are illustrated in this paper.

APA, Harvard, Vancouver, ISO, and other styles

9

Soetedjo, Aryuanto, M. Ibrahim Ashari, and Cosnas Eric Septian. "Implementation of Fuzzy Logic Controller for Wall Following and Obstacle Avoiding Robot." Journal of Applied Intelligent System 4, no. 1 (July 16, 2019): 9–21. http://dx.doi.org/10.33633/jais.v4i1.2168.

Full text

Abstract:

This paper presents the development of wall following and obstacle avoiding robot using a Fuzzy Logic Controller. The ultrasonic sensors are employed to measure the distances between robot and the wall, and between the robot and the obstacle. A low cost Raspberry Pi camera is employed to measure the left/right distance between the robot and the obstacle. The Fuzzy Logic Controller is employed to steer the mobile robot to follow the wall and avoid the obstacle according to the multi sensor inputs. The outputs of Fuzzy Logic Controller are the speeds of left motor and right motor. The experimental results show that the developed mobile robot could be controlled properly to follow the different wall positions and avoid the different obstacle positions with the high successful rate of 83.33%.

APA, Harvard, Vancouver, ISO, and other styles

10

Zenita, Nurisma. "Implementation of a 3-wheeled Wall Following Robot Navigation System using Coppelia." JASEE Journal of Application and Science on Electrical Engineering 3, no. 01 (March 29, 2022): 63–77. http://dx.doi.org/10.31328/jasee.v3i01.4.

Full text

Abstract:

This article will design a controller for a three-wheeled wall-following robot based on Copalia software. The problem with the wall-following robot is how to control a follower robot to move constantly along the wall in the intended direction. The robot controller uses Sugeno fuzzy logic as a rule base for stationary and moving states. This controller is created through distance and orientation navigation. Both are estimated by the robot model and corrected by the sensor measurement results. In cases where the wall is not available, for example, an open door, the robot will stop then there will be feedback to take the next step. The designed controller has been verified experimentally, where the results show an error rate of five millimeters from the actual distance

APA, Harvard, Vancouver, ISO, and other styles

11

Yunardi, Riky Tri, Arief Muchadin, Kurnia Latifa Priyanti, and Deny Arifianto. "INVERSE KINEMATICS AND PID CONTROLLER IMPLEMENTATION OF HEXAPOD ROBOT FOR WALL FOLLOWER NAVIGATION." INAJEEE Indonesian Journal of Electrical and Eletronics Engineering 2, no. 2 (August 30, 2019): 57. http://dx.doi.org/10.26740/inajeee.v2n2.p23-28.

Full text

Abstract:

Wall following is one of the methods used in navigating the movement of robot applications. Because the robot moves along the wall, the ultrasonic sensor is used as a barrier detector capable of measuring the distance between the robot and the wall. The six-legged robot is a hexapod robot has six pieces of legs and each leg has three joints that are used to move. The leg movement is based on the inverse kinematics to obtain the angle value of each joint. Nevertheless, a six-legged robot requires stability in order to move smoothly while following the wall. In this work, a robot was developed using a proportional derivative controller to implemented on wall follower navigation. The PID controller is determined using analytic tuning to produce the controller parameters that are used to make the robot move straighten and keep the position against the wall. Overall, the application of inverse kinematics and PID control on the wall following robot navigation can improve the stability of the robot with a set point value of 8-16 cm on the wall length of 1.5 within 92–96 % of average success rate.

APA, Harvard, Vancouver, ISO, and other styles

12

Iqbal, Muhamad Hamam, and Wahyu Sapto Aji. "Wall Following Control System with PID Control and Ultrasonic Sensor for KRAI 2018 Robot." International Journal of Robotics and Control Systems 1, no. 1 (February 22, 2021): 1–14. http://dx.doi.org/10.31763/ijrcs.v1i1.206.

Full text

Abstract:

Indonesian Abu Robot Contest (KRAI) in 2018 with the theme "Throwing a Blessing Ball". The main purpose of this robot is to be able to navigate automatically in an area that is bordered by walls to complete the mission. The main problem with the robot is the navigation system. The application of PID control in the wall following system has been able to make robot movements smooth, responsive, and fast. In this study, PID control aims to smooth the movement of the robot while walking along the wall in the race arena. The PID parameter is obtained from the results of tuning with the trial and error method, the values of KP = 3, KI = 0, and KD = 5. At the PWM 150 set point the value of the ultrasonic sensor distance reading to the object in the form of a wall with an average error of 4.4. cm. At the PWM 200 set point the value of the ultrasonic sensor distance reading to the object in the form of a wall with an average error of 0.4 cm. At the PWM 250 set point the value of the ultrasonic sensor distance reading to the object in the form of a wall with an average error of 0.8 cm. This error does not have an effect on the performance of the wall following system, because the system only uses the distance value reading data with a decimal value in front of the comma. So it can be concluded that the wall following system which is designed using ultrasonic sensors with measurement error that occurs is zero.

APA, Harvard, Vancouver, ISO, and other styles

13

Bemporad, Alberto, Mauro Di Marco, and Alberto Tesi. "Sonar-Based Wall-Following Control of Mobile Robots." Journal of Dynamic Systems, Measurement, and Control 122, no. 1 (December 2, 1998): 226–29. http://dx.doi.org/10.1115/1.482468.

Full text

Abstract:

In this paper, the wall-following problem for low-velocity mobile robots, equipped with incremental encoders and one sonar sensor, is considered. A robust observer-based controller, which takes into account explicit constraints on the orientation of the sonar sensor with respect to the wall and the velocity of the wheels, is designed. The feedback controller provides convergence and fulfillment of the constraints, once an estimate of the position of the mobile robot, is available. Such an estimate is given by an Extended Kalman Filter (EKF), which is designed via a sensor fusion approach merging the velocity signals from the encoders and the distance measurements from the sonar. Some experimental tests are reported to discuss the robustness of the control scheme. [S0022-0434(00)01101-1]

APA, Harvard, Vancouver, ISO, and other styles

14

Kim, Dong-Hyung, Kyoosik Shin, Chang-Soo Han, and Ji Yeong Lee. "Sensor-based navigation of a car-like robot based on Bug family algorithms." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 227, no. 6 (September 12, 2012): 1224–41. http://dx.doi.org/10.1177/0954406212458202.

Full text

Abstract:

This article presents a sensor-based navigation algorithm for 3-degree-of-freedom car-like robots with a nonholonomic constraint. Similar to the classical Bug family algorithms, the proposed algorithm enables the car-like robot to navigate in a completely unknown environment by only using range sensor information. The car-like robot uses the local range sensor view to determine the local path so that it moves toward the goal. To guarantee that the car-like robot can approach the goal, the two modes of motion are repeated, termed as motion-to-goal and wall-following. The motion-to-goal behavior lets the robot directly move toward the goal and the wall-following behavior makes the car-like robot circumnavigate the obstacle boundary until it meets the leaving condition. For each behavior, the nonholonomic motion for the car-like robot is planned in terms of the turning radius at each step. The proposed algorithm is implemented with a real robot and the experimental results show the performance of the proposed algorithm.

APA, Harvard, Vancouver, ISO, and other styles

15

Paryanta, Paryanta, Robby Rachmatullah, and Yohana Kusuma Kristiani. "Prototipe Robot Pemadam Api Berbasis Mikrokontroler ATMEGA16 dengan Sistem Navigasi Wall Following." Go Infotech: Jurnal Ilmiah STMIK AUB 24, no. 1 (June 10, 2018): 69. http://dx.doi.org/10.36309/goi.v24i1.95.

Full text

Abstract:

<p><em>Perkembangan teknologi komputer dengan tujuan untuk mempermudah pekerjaan manusia mengalami suatu perkembangan, salah satu bentuk teknologi yang mempermudah pekerjaan manusia adalah robot. Dari sekian banyak jenis robot, wall follower yang paling populer. Wall follower dapat dikembangkan menjadi robot pemadam api, yakni sebuah robot yang dapat mengikuti tembok dan memadamkan api yang berasal dari lilin. Pembuatan robot pemadam api dilakukan dengan merakit komponen seperti mikrokontroler atmega 16 sebagai pengendali robot, sensor ultrasonik sebagai pendeteksi halangan berupa tembok, flame sensor sebagai sensor api, dan kipas sebagai pemadam api lilin. Langkah selanjutnya dari pembuatan robot adalah memasukkan program ke dalam mikrokontroler atmega 16 dengan menggunakan bahasa pemrograman arduino. Dengan menggunakan flame sensor yang berfungsi dapat mendeteksi api, mikrokontroler atmega 16 akan memberikan perintah kepada robot untuk berjalan ke arah sumber api, kipas akan bekerja dan memadamkan api lilin. Seluruh perangkat keras dan perangkat lunak dikontrol dan dikendalikan oleh mikrokontroler atmega 16. Dengan diciptakannya robot pemadam api ini akan mempermudah pekerjaan manusia dan menemukan titik api sehingga api tidak menyebar.</em></p>

APA, Harvard, Vancouver, ISO, and other styles

16

Anđelić, Nikola, Sandi Baressi Šegota, Matko Glučina, and Ivan Lorencin. "Classification of Wall Following Robot Movements Using Genetic Programming Symbolic Classifier." Machines 11, no. 1 (January 12, 2023): 105. http://dx.doi.org/10.3390/machines11010105.

Full text

Abstract:

The navigation of mobile robots throughout the surrounding environment without collisions is one of the mandatory behaviors in the field of mobile robotics. The movement of the robot through its surrounding environment is achieved using sensors and a control system. The application of artificial intelligence could potentially predict the possible movement of a mobile robot if a robot encounters potential obstacles. The data used in this paper is obtained from a wall-following robot that navigates through the room following the wall in a clockwise direction with the use of 24 ultrasound sensors. The idea of this paper is to apply genetic programming symbolic classifier (GPSC) with random hyperparameter search and 5-fold cross-validation to investigate if these methods could classify the movement in the correct category (move forward, slight right turn, sharp right turn, and slight left turn) with high accuracy. Since the original dataset is imbalanced, oversampling methods (ADASYN, SMOTE, and BorderlineSMOTE) were applied to achieve the balance between class samples. These over-sampled dataset variations were used to train the GPSC algorithm with a random hyperparameter search and 5-fold cross-validation. The mean and standard deviation of accuracy (ACC), the area under the receiver operating characteristic (AUC), precision, recall, and F1−score values were used to measure the classification performance of the obtained symbolic expressions. The investigation showed that the best symbolic expressions were obtained on a dataset balanced with the BorderlineSMOTE method with ACC¯±SD(ACC), AUC¯macro±SD(AUC), Precision¯macro±SD(Precision), Recall¯macro±SD(Recall), and F1−score¯macro±SD(F1−score) equal to 0.975×1.81×10−3, 0.997±6.37×10−4, 0.975±1.82×10−3, 0.976±1.59×10−3, and 0.9785±1.74×10−3, respectively. The final test was to use the set of best symbolic expressions and apply them to the original dataset. In this case the ACC¯±SD(ACC), AUC¯±SD(AUC), Precision¯±SD(Precision), Recall¯±SD(Recall), and F1−score¯±SD(F1−Score) are equal to 0.956±0.05, 0.9536±0.057, 0.9507±0.0275, 0.9809±0.01, 0.9698±0.00725, respectively. The results of the investigation showed that this simple, non-linearly separable classification task could be solved using the GPSC algorithm with high accuracy.

APA, Harvard, Vancouver, ISO, and other styles

17

Lin, Tzu-Chao, Chao-Chun Chen, and Cheng-Jian Lin. "Navigation control of mobile robot using interval type-2 neural fuzzy controller optimized by dynamic group differential evolution." Advances in Mechanical Engineering 10, no. 1 (January 2018): 168781401775248. http://dx.doi.org/10.1177/1687814017752483.

Full text

Abstract:

This study developed and effectively implemented an efficient navigation control of a mobile robot in unknown environments. The proposed navigation control method consists of mode manager, wall-following mode, and towards-goal mode. The interval type-2 neural fuzzy controller optimized by the dynamic group differential evolution is exploited for reinforcement learning to develop an adaptive wall-following controller. The wall-following performance of the robot is evaluated by a proposed fitness function. The mode manager switches to the proper mode according to the relation between the mobile robot and the environment, and an escape mechanism is added to prevent the robot falling into the dead cycle. The experimental results of wall-following show that dynamic group differential evolution is superior to other methods. In addition, the navigation control results further show that the moving track of proposed model is better than other methods and it successfully completes the navigation control in unknown environments.

APA, Harvard, Vancouver, ISO, and other styles

18

Ramadhan, Rizky, Indrazno Siradjuddin, and Denda Dewatama. "Sistem Navigasi Wall Following Robot Omnidirectional Dengan 4 Penggerak Mekanum Menggunakan PID Berbasis myRIO." Jurnal Elektronika dan Otomasi Industri 9, no. 2 (July 31, 2022): 76. http://dx.doi.org/10.33795/elk.v9i2.263.

Full text

Abstract:

Intisari— Navigasi wall following merupakan salah satu sistem navigasi dengan cara mengikuti dinding. Tugas dari robot ini adalah mengikuti dinding sesuai dengan setpoint yang diberikan dengan cara mempertahankan jarak robot dengan dinding agar robot tetap dalam jarak aman dan tidak menyentuh dinding. Pada robot ini menggunakan sensor infrared sebagai masukan untuk mengukur jarak antara robot dengan dinding. Dimana nantinya data dari sensor diolah pada kontroller myRIO dengan menggunakan metode PID yang nantinya data keluarannya akan diolah lagi menggunakan inverse kinematic. Untuk keluaran dari robot ini adalah 4 buah motor mekanum. Robot ini menggunakan software LabVIEW untuk menampilkan dan mengatur PID, dimana PID yang diterapkan pada robot ini dapat membuat pergerakan robot ini lebih cepat dan stabil. Penentuan parameter kendali PID dalam penelitian ini diperoleh menggunakan 2 metode, yaitu ziegler Nichols dan trial and error, pada metode ziegler nichols didapatkan respon yang kurang baik, sehingga dilanjutkan menggunakan metode trial and error yang mendapatkan repon yang cepat dan stabil dengan nilai Kp = 1.5, dan Kd = 0.004

APA, Harvard, Vancouver, ISO, and other styles

19

Chung, Tan Lam, Trong Hieu Bui, Sang Bong Kim, Myung Suck Oh, and Tan Tien Nguyen. "Wall-following control of a two-wheeled mobile robot." KSME International Journal 18, no. 8 (August 2004): 1288–96. http://dx.doi.org/10.1007/bf02984242.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Lin, Cheng-Jian, and Hsueh-Yi Lin. "Mobile robot wall-following control using a fuzzy cerebellar model articulation controller with group-based strategy bacterial foraging optimization." International Journal of Advanced Robotic Systems 14, no. 4 (July 1, 2017): 172988141772087. http://dx.doi.org/10.1177/1729881417720872.

Full text

Abstract:

In this study, a fuzzy cerebellar model articulation controller based on group-based strategy bacterial foraging optimization is proposed for mobile robot wall-following control. In fuzzy cerebellar model articulation controller, the inputs are the distance between the sonar and the wall, and the outputs are the angular velocity of two wheels. The proposed group-based strategy bacterial foraging optimization learning algorithm is used to adjust the parameters of fuzzy cerebellar model articulation controller model. The proposed group-based strategy bacterial foraging optimization has the advantages of global search, evolutionary strategies, and group evolution to speed up the convergent rate. A new fitness function is defined to evaluate the performance of mobile robot wall-following control. The fitness function includes four assessment factors which are defined as follows: (1) maintaining safe distance between the mobile robot and the wall, (2) ensuring successfully running a cycle, (3) avoiding mobile robot collisions, and (4) mobile robot running at a maximum speed. The experimental results show that the proposed group-based strategy bacterial foraging optimization obtains a better wall-following control than other methods in unknown environments.

APA, Harvard, Vancouver, ISO, and other styles

21

Suwoyo, Heru, and Ferryawan Harris Kristanto. "Performance of a Wall-Following Robot Controlled by a PID-BA using Bat Algorithm Approach." International Journal of Engineering Continuity 1, no. 1 (December 10, 2022): 56–71. http://dx.doi.org/10.58291/ijec.v1i1.39.

Full text

Abstract:

A wall-following robot needs a controller that applies the closed-loop concept to move actively without hindrance. Some controllers with good capabilities can act as controllers for wall follower robots, such as PID controllers. Conceptually, this controller's good performance depends on tuning the three gains before use. Instead of giving the expected and appropriate output, wrong settings will provide inaccuracies for the controller, so applying the manual method at the tuning stage is not recommended. For this reason, PID controllers are often implemented in a system supported by appropriate optimization methods, such as Genetic Algorithm or Particle Swarm Optimization. Furthermore, different from this, in this study, the Bath Algorithm is used as an alternative optimization algorithm. Its application begins with a realistic simulation of a wall-following robot. This is done to provide the possibility to implement online PID controllers and BAs. In the end, several methods are compared to find out the performance of this type of approach. Moreover, based on the observed comparative results, the proposed method gives a better value in accumulative error and convergence speed in the PID optimization process.

APA, Harvard, Vancouver, ISO, and other styles

22

Suwoyo, Heru, Zhou Thong, Yingzhong Tian, Andi Adriansyah, and Muhammad Hafizd Ibnu Hajar. "THE ACA-BASED PID CONTROLLER FOR ENHANCING A WHEELED-MOBILE ROBOT." TEKNOKOM 5, no. 1 (April 7, 2022): 103–12. http://dx.doi.org/10.31943/teknokom.v5i1.74.

Full text

Abstract:

Wall-following control of mobile robot is an important topic in the mobile robot researches. The wall-following control problem is characterized by moving the robot along the wall in a desired direction while maintaining a constants distance to the wall. The existing control algorithms become complicated in implementation and not efficient enough. Ant colony algorithm (ACA), in terms of optimizing parameters, has a faster convergence speed and features that are easy to integrate with other methods. This paper adopts ant colony algorithm to optimize PID controller, and then selects ideal control parameters. The simulation results based on MATLAB show that the control system optimized by ant colony algorithm has higher efficiency than the traditional control systems in term of RMSE.

APA, Harvard, Vancouver, ISO, and other styles

23

Suwoyo, Heru, Yingzhong Tian, Andi Adriansyah, Muhammad Hafizd Ibnu Hajar, and Tong Zhou. "The ACA-based PID Controller for Enhancing a Wheeled-Mobile Robot." Journal FORTEI-JEERI 1, no. 2 (December 28, 2020): 19–23. http://dx.doi.org/10.46962/forteijeeri.v1i2.15.

Full text

Abstract:

Wall-following control of mobile robot is an important topic in the mobile robot researches. The wall-following control problem is characterized by moving the robot along the wall in a desired direction while maintaining a constants distance to the wall. The existing control algorithms become complicated in implementation and not efficient enough. Ant colony algorithm (ACA), in terms of optimizing parameters, has a faster convergence speed and features that are easy to integrate with other methods. This paper adopts ant colony algorithm to optimize PID controller, and then selects ideal control parameters. The simulation results based on MATLAB show that the control system optimized by ant colony algorithm has higher efficiency than the traditional control systems in term of RMSE.

APA, Harvard, Vancouver, ISO, and other styles

24

Han, Seunghan, Yongrae Choi, Jaepil Yang, Hyungjun Hwang, Kihun Kim, Juhye Shin, Dong-Young Jang, and Dongha Shim. "Improved Wall-following Driving for Robot Operating System-based High-speed Autonomous Mobile Robot." Journal of the Korean Society of Manufacturing Technology Engineers 28, no. 3 (June 15, 2019): 156–65. http://dx.doi.org/10.7735/ksmte.2019.28.3.156.

Full text

APA, Harvard, Vancouver, ISO, and other styles

25

Tashtoush, Yahya, Israa Haj-Mahmoud, Omar Darwish, Majdi Maabreh, Belal Alsinglawi, Mahmoud Elkhodr, and Nasser Alsaedi. "Enhancing Robots Navigation in Internet of Things Indoor Systems." Computers 10, no. 11 (November 15, 2021): 153. http://dx.doi.org/10.3390/computers10110153.

Full text

Abstract:

In this study, an effective local minima detection and definition algorithm is introduced for a mobile robot navigating through unknown static environments. Furthermore, five approaches are presented and compared with the popular approach wall-following to pull the robot out of the local minima enclosure namely; Random Virtual Target, Reflected Virtual Target, Global Path Backtracking, Half Path Backtracking, and Local Path Backtracking. The proposed approaches mainly depend on changing the target location temporarily to avoid the original target’s attraction force effect on the robot. Moreover, to avoid getting trapped in the same location, a virtual obstacle is placed to cover the local minima enclosure. To include the most common shapes of deadlock situations, the proposed approaches were evaluated in four different environments; V-shaped, double U-shaped, C-shaped, and cluttered environments. The results reveal that the robot, using any of the proposed approaches, requires fewer steps to reach the destination, ranging from 59 to 73 m on average, as opposed to the wall-following strategy, which requires an average of 732 m. On average, the robot with a constant speed and reflected virtual target approach takes 103 s, whereas the identical robot with a wall-following approach takes 907 s to complete the tasks. Using a fuzzy-speed robot, the duration for the wall-following approach is greatly reduced to 507 s, while the reflected virtual target may only need up to 20% of that time. More results and detailed comparisons are embedded in the subsequent sections.

APA, Harvard, Vancouver, ISO, and other styles

26

Chen, Cheng-Hung, Shiou-Yun Jeng, and Cheng-Jian Lin. "Mobile Robot Wall-Following Control Using Fuzzy Logic Controller with Improved Differential Search and Reinforcement Learning." Mathematics 8, no. 8 (July 31, 2020): 1254. http://dx.doi.org/10.3390/math8081254.

Full text

Abstract:

In this study, a fuzzy logic controller with the reinforcement improved differential search algorithm (FLC_R-IDS) is proposed for solving a mobile robot wall-following control problem. This study uses the reward and punishment mechanisms of reinforcement learning to train the mobile robot wall-following control. The proposed improved differential search algorithm uses parameter adaptation to adjust the control parameters. To improve the exploration of the algorithm, a change in the number of superorganisms is required as it involves a stopover site. This study uses reinforcement learning to guide the behavior of the robot. When the mobile robot satisfies three reward conditions, it gets reward +1. The accumulated reward value is used to evaluate the controller and to replace the next controller training. Experimental results show that, compared with the traditional differential search algorithm and the chaos differential search algorithm, the average error value of the proposed FLC_R-IDS in the three experimental environments is reduced by 12.44%, 22.54% and 25.98%, respectively. Final, the experimental results also show that the real mobile robot using the proposed method can effectively implement the wall-following control.

APA, Harvard, Vancouver, ISO, and other styles

27

Adriansyah, Andi, and Shamsudin H. Mohd Amin. "WALL-FOLLOWING BEHAVIOR-BASED MOBILE ROBOT USING PARTICLE SWARM FUZZY CONTROLLER." Jurnal Ilmu Komputer dan Informasi 9, no. 1 (February 15, 2016): 9. http://dx.doi.org/10.21609/jiki.v9i1.367.

Full text

Abstract:

Behavior-based control architecture has been broadly recognized due to their compentence in mobile robot development. Fuzzy logic system characteristics are appropriate to address the behavior design problems. Nevertheless, there are problems encountered when setting fuzzy variables manually. Consequently, most of the efforts in the field, produce certain works for the study of fuzzy systems with added learning abilities. This paper presents the improvement of fuzzy behavior-based control architecture using Particle Swarm Optimization (PSO). A wall-following behaviors used on Particle Swarm Fuzzy Controller (PSFC) are developed using the modified PSO with two stages of the PSFC process. Several simulations have been accomplished to analyze the algorithm. The promising performance have proved that the proposed control architecture for mobile robot has better capability to accomplish useful task in real office-like environment.

APA, Harvard, Vancouver, ISO, and other styles

28

Prescott, Tony J., and Carl Ibbotson. "A Robot Trace Maker: Modeling the Fossil Evidence of Early Invertebrate Behavior." Artificial Life 3, no. 4 (October 1997): 289–306. http://dx.doi.org/10.1162/artl.1997.3.4.289.

Full text

Abstract:

The study of trace fossils, the fossilized remains of animal behavior, reveals interesting parallels with recent research in behavior-based robotics. This article reports robot simulations of the meandering foraging trails left by early invertebrates that demonstrate that such trails can be generated by mechanisms similar to those used for robot wall-following. We conclude with the suggestion that the capacity for intelligent behavior shown by many behavior-based robots is similar to that of animals of the late Precambrian and early Cambrian periods approximately 530 to 565 million years ago.

APA, Harvard, Vancouver, ISO, and other styles

29

Budhana, I. Made Arya, Ida Bagus Alit Swamardika, and Yoga Divayana. "Rancang Bangun Robot 6WD Dengan Sensor Gas TGS2600 Menggunakan Metode Wall Following Berbasis Arduino Mega 2560." Majalah Ilmiah Teknologi Elektro 17, no. 1 (July 6, 2017): 51. http://dx.doi.org/10.24843/mite.2018.v17i01.p07.

Full text

Abstract:

Intisari— Perkembangan teknologi khususnya dibidang robotika saat ini sangat pesat, Salah satu bentuk aplikasi dari teknologi robotika yang erat kaitannya dengan sistem kontrol adalah wheel mobile robot. Beberapa metode dapat dilakukan untuk mendistribusikan gas alam salah satunya dengan pipa. Distribusi gas alam dengan menggunakan pipa sering mengalami kendala kebocoran yang disebabkan usia dari pipa distribusi yang sudah cukup tua. Untuk mempermudah pemantauan pipa gas yang berada di bawah tanah digunakan robot 6 WD (wheel drive) yang memiliki 6 roda dan penggerak pada setiap rodanya untuk mengatasi medan yang berat. Pergerakan dari robot 6 WD mengacu pada sensor ultrasonik SRF HC-SR04, metode ini dinamakan wall following. Sensor gas tipe TGS dari figaro dimanfaatkan untuk mengetahui adanya kebocoran gas pada pipa atau tidak. Selain itu, robot ini juga dilengkapi dengan kamera untuk mengirim gambar kerusakan pipa pada user agar dapat segera dilakukan perbaikan. Arduino Mega 2560 digunakan sebagai otak pada robot 6 WD yang bertugas untuk mengolah data yang masuk dan memberikan instruksi pada robot 6WD. Pengiriman data dari robot 6 WD pada pengguna meliputi, data sensor gas, data sensor kompas, data sensor jarak dan gambar kerusakan pada pipa. Seluruh data dapat dilihat pada GCS (Ground Control Station). [TRUNITIN CHECK 20%, 26042017]

APA, Harvard, Vancouver, ISO, and other styles

30

Kimura, Shogo, Hisaya Tanaka, and Hideto Ide. "Automapping by Autonomous Mobile Robot using Ultrasonic Range Sensor." Journal of Robotics and Mechatronics 9, no. 4 (August 20, 1997): 310–16. http://dx.doi.org/10.20965/jrm.1997.p0310.

Full text

Abstract:

For an autonomous mobile robot, how make and describe a environment map is very important problem. I made a robot that using ultrasonic range sensor and autonomous mapping by this robot at wall following algorithm. This experiment shows only using ultrasonic range sensor Mapping problem.

APA, Harvard, Vancouver, ISO, and other styles

31

Katsev, Max, Anna Yershova, Benjamín Tovar, Robert Ghrist, and Steven M. LaValle. "Mapping and Pursuit-Evasion Strategies For a Simple Wall-Following Robot." IEEE Transactions on Robotics 27, no. 1 (February 2011): 113–28. http://dx.doi.org/10.1109/tro.2010.2095570.

Full text

APA, Harvard, Vancouver, ISO, and other styles

32

Huang, L. "Wall-following control of an infrared sensors guided wheeled mobile robot." International Journal of Intelligent Systems Technologies and Applications 7, no. 1 (2009): 106. http://dx.doi.org/10.1504/ijista.2009.025110.

Full text

APA, Harvard, Vancouver, ISO, and other styles

33

Chia-Feng Juang and Chia-Hung Hsu. "Reinforcement Ant Optimized Fuzzy Controller for Mobile-Robot Wall-Following Control." IEEE Transactions on Industrial Electronics 56, no. 10 (October 2009): 3931–40. http://dx.doi.org/10.1109/tie.2009.2017557.

Full text

APA, Harvard, Vancouver, ISO, and other styles

34

Lius, Dyky Agustiono, Totok Winarno, and Sidik Nurcahyo. "IMPLEMENTASI AUTO THRESHOLD PADA SENSOR KAMERA UNTUK WALL FOLLOWING ROBOT KRPAI BERKAKI." Jurnal Elektronika dan Otomasi Industri 3, no. 1 (November 23, 2020): 31. http://dx.doi.org/10.33795/elkolind.v3i1.63.

Full text

Abstract:

Pada pengolahan citra (Image Processing) salah satu metode yang sederhana untuk digunakan adalah metode segmentasi hitam putih (threshold). Threshold adalah citra digital yang hanya memiliki dua kemungkinan nilai pixel yaitu hitam dan putih. Namun dalam penentuan nilai ambang threshold akan berbeda-beda tergantung pada intensitas cahaya yang ditangkap oleh kamera. Oleh karena itu, diperlukan proses lanjutan dari threshold untuk mendapatkan nilai ambang threshold secara otomatis pada kondisi cahaya yang berbeda-beda yaitu yang disebut dengan Auto Threshold. Auto Threshold merupakan algoritma untuk mendapatkan nilai ambang threshold secara otomatis dengan cara mencari nilai threshold awal sebagai referensi lalu memperbaikinya menggunakan informasi dari sebaran intensitas warna abu-abu. Salah satu objek yang memiliki intensitas cahaya yang berbeda adalah arena robot KRPAI berkaki. Arena tersebut berbentuk seperti labirin yang memiliki dinding berwarna putih dan lantai berwarna hitam, namun intensitas warna tersebut berbeda-beda tiap ruangnya. Dengan menggunakan beaglebone black sebagai embedded system untuk melakukan proses pengolahan citra didapatkan bahwa ketika kondisi cahaya redup pada intensitas cahaya 42 lux diperoleh nilai ambang threshold 52, sedangkan untuk kondisi cahaya terang dengan intensitas 50 lux diperoleh nilai ambang threshold 85 dan untuk kondisi cahaya sangat terang dengan intensitas 53,5 lux diperoleh nilai ambang threshold 94. Untuk mengontrol pergerakan robot dalam wall following menggunakan kontrol PID dan diperoleh nilai dari parameter PID adalah kp=5, ki=0.3, kd=0,5 dimana robot untuk dapat stabil dalam menemukan jalan setelah berhadapan dengan dinding memerlukan waktu selama 8 detik.

APA, Harvard, Vancouver, ISO, and other styles

35

Rehman Khan, Abdul, Ameer Tamoor Khan, Masood Salik, and Sunila Bakhsh. "An Optimally Configured HP-GRU Model Using Hyperband for the Control of Wall Following Robot." International Journal of Robotics and Control Systems 1, no. 1 (March 10, 2021): 66–74. http://dx.doi.org/10.31763/ijrcs.v1i1.281.

Full text

Abstract:

In this paper, we presented an autonomous control framework for the wall following robot using an optimally configured Gated Recurrent Unit (GRU) model with the hyperband algorithm. GRU is popularly known for the time-series or sequence data, and it overcomes the vanishing gradient problem of RNN. GRU also consumes less memory and is computationally more efficient than LSTMs. The selection of hyper-parameters of the GRU model is a complex optimization problem with local minima. Usually, hyper-parameters are selected through hit and trial, which does not guarantee an optimal solution. To come around this problem, we used a hyperband algorithm for the selection of optimal parameters. It is an iterative method, which searches for the optimal configuration by discarding the least performing configurations on each iteration. The proposed HP-GRU model is used on a dataset of SCITOS G5 robots with 24 sensors mounted. The results show that HP-GRU has a mean accuracy of 0.9857 and a mean loss of 0.0810, and it is comparable with other deep learning algorithms.

APA, Harvard, Vancouver, ISO, and other styles

36

Kakikura, Masayoshi, Masaru Amano, and Osamu Okamoto. "Evaluation of Removed Quantity for Wall Coating Removing Robot Using Image Processing." Journal of Robotics and Mechatronics 7, no. 5 (October 20, 1995): 397–403. http://dx.doi.org/10.20965/jrm.1995.p0397.

Full text

Abstract:

The surfaces of buildings are covered with various wall coatings for beauty and protection. When a wall surface is to be repainted, its coating has to be removed completely. Almost every worker wants to avoid such work since it is very hard and dangerous. In order to solve this problem, we have developed a ""wall coating removing robot."" This robot has a sucking disk, a rotatable water jet nozzle for removing work, and four driving wheels. Coating-removed wall surfaces can be classified into three states: properly removed surface (P surface); over-removed surface (0 surface); and under-removed surface (U surface). The wall surfaces are classified by using data represented in HLS space. It seems that each surface has the following features: a P surface is a concrete surface which looks like a smooth surface; an 0 surface shows some aggregates in concrete, which presents the appearance of a coarse surface; and a U surface has non-removed coating areas. Images from a CCD camera fixed on the robot can be classified into three groups based on a newly developed method. This method uses the following values: (a) color differences between pixels in HLS space and (b) percentage of nonremoved areas in processed images.

APA, Harvard, Vancouver, ISO, and other styles

37

TANEV, Ivan, and Katsunori SHIMOHARA. "Evolution of Active Sensing for Wall-Following Navigation of Snake-Like Robot." SICE Journal of Control, Measurement, and System Integration 2, no. 4 (2009): 222–28. http://dx.doi.org/10.9746/jcmsi.2.222.

Full text

APA, Harvard, Vancouver, ISO, and other styles

38

Hsu, Chia-Hung, and Chia-Feng Juang. "Multi-Objective Continuous-Ant-Colony-Optimized FC for Robot Wall-Following Control." IEEE Computational Intelligence Magazine 8, no. 3 (August 2013): 28–40. http://dx.doi.org/10.1109/mci.2013.2264233.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Braunstingl, Reinhard, and Aníbal Ollero. "Evaluating the Wall Following Behaviour of a Mobile Robot with Fuzzy Logic." IFAC Proceedings Volumes 29, no. 7 (November 1996): 79–83. http://dx.doi.org/10.1016/s1474-6670(17)43702-5.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

Chen, Yen-Lun, Jun Cheng, Chuan Lin, Xinyu Wu, Yongsheng Ou, and Yangsheng Xu. "Classification-based learning by particle swarm optimization for wall-following robot navigation." Neurocomputing 113 (August 2013): 27–35. http://dx.doi.org/10.1016/j.neucom.2012.12.037.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

Sendari, Siti, Shingo Mabu, Andre Tjahjadi, and Kotaro Hirasawa. "Fuzzy Genetic Network Programming with Noises for Mobile Robot Navigation." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 7 (September 20, 2011): 767–76. http://dx.doi.org/10.20965/jaciii.2011.p0767.

Full text

Abstract:

Genetic Network Programming (GNP) has been proposed as one of the evolutionary algorithms, which is represented by graph structures. It was extended to GNP with Reinforcement Learning (GNP-RL) which combines online learning and evolution. GNP-RL succeeded in implementing the wall following behaviors of a Khepera robot. The objective of this paper is to improve the robustness of GNP-RL by introducing fuzzy GNP with noises. Fuzzy GNP overcomes the sharp boundary problem using the probabilistic transition on fuzzy judgment nodes, which improves the exploration ability. Furthermore, the robustness of fuzzy GNP can be improved by adding Gaussian noises to the sensors during the training phase. In order to evaluate the robustness of fuzzy GNP with noises, the wall following of a Khepera robot is simulated. Simulation results show that fuzzy GNP with noises is superior to GNP-RL.

APA, Harvard, Vancouver, ISO, and other styles

42

Wang, Minghui, Bi Zeng, and Qiujie Wang. "Research on Motion Planning Based on Flocking Control and Reinforcement Learning for Multi-Robot Systems." Machines 9, no. 4 (April 1, 2021): 77. http://dx.doi.org/10.3390/machines9040077.

Full text

Abstract:

Robots have poor adaptive ability in terms of formation control and obstacle avoidance control in unknown complex environments. To address this problem, in this paper, we propose a new motion planning method based on flocking control and reinforcement learning. It uses flocking control to implement a multi-robot orderly motion. To avoid the trap of potential fields faced during flocking control, the flocking control is optimized, and the strategy of wall-following behavior control is designed. In this paper, reinforcement learning is adopted to implement the robotic behavioral decision and to enhance the analytical and predictive abilities of the robot during motion planning in an unknown environment. A visual simulation platform is developed in this paper, on which researchers can test algorithms for multi-robot motion control, such as obstacle avoidance control, formation control, path planning and reinforcement learning strategy. As shown by the simulation experiments, the motion planning method presented in this paper can enhance the abilities of multi-robot systems to self-learn and self-adapt under a fully unknown environment with complex obstacles.

APA, Harvard, Vancouver, ISO, and other styles

43

Prasetyo, Teddy Hero, Indrazno Siradjuddin, and Sungkono Sungkono. "Sistem Kendali Wall Following Pada Mobile Robot Dengan Penggerak Mekanum Menggunakan Metode Fuzzy." Jurnal Elektronika dan Otomasi Industri 8, no. 3 (October 1, 2021): 214. http://dx.doi.org/10.33795/elk.v8i3.268.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

Duan, Ping, Cheng-jun Ding, Guang-ming Yuan, and Ming-lu Zhang. "Wall-Following of Mobile Robot Based on Fuzzy Genetic Algorithm to Linear Interpolating." Fuzzy Information and Engineering 2, no. 2 (June 2010): 201–11. http://dx.doi.org/10.1007/s12543-010-0045-6.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

Poncela, Alberto. "PCA-based method to fuse behaviors from place characterization for robot navigation." Robotica 35, no. 2 (June 19, 2015): 254–70. http://dx.doi.org/10.1017/s0263574715000478.

Full text

Abstract:

SUMMARYThis paper presents a method to calculate the fusing rule among three reactive behaviors, Wall Following, Corridor Following and Door Crossing, from place characterization for robot navigation. The technique is supported by a local grid of the closest area to the robot, which is built from sonar readings. The contour of this grid is extracted, represented by itsFFTand, finally, it is reduced to a short feature vector with a principal component analysis (PCA). This feature vector is used to decide the fusing rule among the three behaviors. The algorithm is very fast in terms of its time performance, being then valid to be used in robot navigation, since the robot would rapidly react to new situations. It has also been successfully tested in simulated and real environments, with a Pioneer robot equipped with eight frontal sonar sensors, both in manually driven tasks and autonomous navigation tasks, proving its feasibility and effectiveness.

APA, Harvard, Vancouver, ISO, and other styles

46

Tokizawa, M., Y. Ando, and M. Mizukawa. "Sound Source Following by an Autonomous Mobile Robot with Condenser Microphones : Sound source following along the wall." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2003 (2003): 7. http://dx.doi.org/10.1299/jsmermd.2003.7_6.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Jhang, Jyun-Yu, Cheng-Jian Lin, and Kuu-Young Young. "Cooperative Carrying Control for Multi-Evolutionary Mobile Robots in Unknown Environments." Electronics 8, no. 3 (March 6, 2019): 298. http://dx.doi.org/10.3390/electronics8030298.

Full text

Abstract:

This study provides an effective cooperative carrying and navigation control method for mobile robots in an unknown environment. The manager mode switches between two behavioral control modes—wall-following mode (WFM) and toward-goal mode (TGM)—based on the relationship between the mobile robot and the unknown environment. An interval type-2 fuzzy neural controller (IT2FNC) based on a dynamic group differential evolution (DGDE) is proposed to realize the carrying control and WFM control for mobile robots. The proposed DGDE uses a hybrid method that involves a group concept and an improved differential evolution to overcome the drawbacks of the traditional differential evolution algorithm. A reinforcement learning strategy was adopted to develop an adaptive WFM control and achieve cooperative carrying control for mobile robots. The experimental results demonstrated that the proposed DGDE is superior to other algorithms at using WFM control. Moreover, the experimental results demonstrate that the proposed method can complete the task of cooperative carrying, and can realize navigation control to enable the robot to reach the target location.

APA, Harvard, Vancouver, ISO, and other styles

48

Lin, Cheng-Jian, Shiou-Yun Jeng, Hsueh-Yi Lin, and Cheng-Yi Yu. "Design and Verification of an Interval Type-2 Fuzzy Neural Network Based on Improved Particle Swarm Optimization." Applied Sciences 10, no. 9 (April 27, 2020): 3041. http://dx.doi.org/10.3390/app10093041.

Full text

Abstract:

In this study, we proposed an interval type-2 fuzzy neural network (IT2FNN) based on an improved particle swarm optimization (PSO) method for prediction and control applications. The noise-suppressing ability of the proposed IT2FNN was superior to that of the traditional type-1 fuzzy neural network. We proposed dynamic group cooperative particle swarm optimization (DGCPSO) with superior local search ability to overcome the local optimum problem of traditional PSO. The proposed model and related algorithms were verified through the accuracy of prediction and wall-following control of a mobile robot. Supervised learning was used for prediction, and reinforcement learning was used to achieve wall-following control. The experimental results demonstrated that DGCPSO exhibited superior prediction and wall-following control.

APA, Harvard, Vancouver, ISO, and other styles

49

Shindo, Tomokazu, Hiroshi Yokoi, and Yukinori Kakazu. "Adaptive Logic Circuits Based on Net-list Evolution." Journal of Robotics and Mechatronics 12, no. 2 (April 20, 2000): 144–49. http://dx.doi.org/10.20965/jrm.2000.p0144.

Full text

Abstract:

We discuss adaptive hardware evolution, evolvable hardware, describing system functions as logic circuits rather than programs. Evolution occurs in circuit formation and then functions are generated from the formation. In general approaches, possibility of formation is restricted by device structure, so we propose net-list evolution and apply it a wall following problem with a Khepera robot, confirming solution of the interface problem between the robot and environment using the proposed method.

APA, Harvard, Vancouver, ISO, and other styles

50

Meel, Priyanka, Ritu Tiwari, and Anupam Shukla. "Optimization of Focused Wave Front Algorithm in Unknown Dynamic Environment for Multi-Robot Navigation." International Journal of Intelligent Mechatronics and Robotics 3, no. 4 (October 2013): 1–29. http://dx.doi.org/10.4018/ijimr.2013100101.

Full text

Abstract:

Robotics is a field which includes multiple disciplines such as environment mapping, localization, path planning, path execution, area exploration etc. Path planning is the elementary requirement for all the above mentioned diversified fields. This paper presents a new method for motion planning of mobile robots which carry forward the best features of Focused Wave Front and other wave front based path planners, at the same time optimizes the algorithm in terms of path length, energy consumption and memory requirements. This research introduces a method of choosing every next step in grid based environment and also proposes a backtracking procedure to minimize turns by means of identifying landmark points in the path. Further, the authors have enhanced the functionality of Focused Wave Front algorithm by applying it in uncertain dynamic environment. The proposed method is a combination of global and local path planning as well as online and offline navigation process. A new method based on bidirectional wave propagation along the walls of obstacle and wall following behavior is being proposed for avoiding uncertain static obstacles. Considering the criticalness of moving obstacles a colored safety zone is assumed to have around them and the robot is equipped with color sensitivity. Based on the particular color (red, green, yellow) that has sensed the robot will make intelligent decisions to avoid them. The simulation result reflects how the proposed method has efficiently and safely navigates a robot towards its destination by avoiding all known and unknown obstacles. Finally the algorithms are extended for multi-robot environment.

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Wall following robot'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles