Relevant bibliographies by topics / Wall following robot

Academic literature on the topic 'Wall following robot'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Wall following robot"

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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.

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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.
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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.

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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
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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.

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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.
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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.

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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.
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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.

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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.
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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.

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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.
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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.

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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.
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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.

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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.
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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.

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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%.
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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.

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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
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Dissertations / Theses on the topic "Wall following robot"

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Daltorio, Kathryn A. "Obstacle Navigation Decision-Making: Modeling Insect Behavior for Robot Autonomy." Case Western Reserve University School of Graduate Studies / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=case1365157897.

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Vožda, Ondřej. "Řízení invalidního vozíku." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2013. http://www.nusl.cz/ntk/nusl-220145.

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This thesis describes development of control algorithm for a wheelchair. Wheelchair should be capable of tracking and following a wall or a similar flat surface. Thesis is supposed to be an extension of the previous concept, whose purpose was to allow remote telepresence control of this wheelchair. SRF08 ultrasonic range finders are used to measure distance from the wall. Furthermore, image processing for mark detection is discussed. Purpose of these marks is to increase precision during final phase of the parking.
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Chien, Chung-Wei, and 簡宗緯. "Mobile-Robot Wall-Following Control Design." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/21392174241916514565.

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碩士
國立暨南國際大學
電機工程學系
102
A mechanism of robot vehicle that can walk along the wall was proposed in this thesis. The main part of the robot vehicle is consists of five SHARP short-distance infrared sensors ,two PWM servo motor and ALTERA DE0-Nano experimental board. One of the short-distance infrared sensors was installed on the front, the other two infrared sensors were in the left and right sides which has 45 degree angle from the front, and the last two sensor were also in both sides but they are vertical from the front. By the cooperation of these infrared sensors which were considered about their position and angle. The mobile-robot’s scanning range could up to 180 degrees. By receiving signal of infrared sensors from different directions and deliver them to CPU which is developed by ourselves. After a series of calculation and with the writing of firmware. The best executing mode could be choice exactly and then make the corresponding action to control motor's rotation. Finally, we implement this system and test in the actual environment. The results showed that the robot vehicle can achieve functions which we look forward to.
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Dai-Hua, Jinag, and 江岱樺. "Wall-Following Fuzzy Control for Mobile Robot." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/79334524305267305766.

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碩士
國立暨南國際大學
電機工程學系
103
This thesis proposes wall-following fuzzy control for mobile robots. The goal is to design a mobile robot that can follow the wall in various environments so that the mobile robot has to equip five infrared sensors to collect data. The W5 processor processes the obtained information from sensors into fuzzy states. Each fuzzy state is related to some specific behaviors. Fuzzy states are correlated to their matching behaviors via if-then rules which are designed by human reasoning. In order to achieve our goal, state registers are needed to record behavior state. It was proved that, using the fuzzy control, the mobile robot is able to deal with different wall conditions includes straight, concave and convex shapes. The result shows that the wall-following fuzzy control is successful for the mobile robot.
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HUNG, YOU-JIA, and 洪佑嘉. "Speed-Controller-Based Mobile Robot for Wall-Following Control." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/56881208377383619476.

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碩士
國立暨南國際大學
電機工程學系
104
A mechanism of robot vehicle that can walk along the wall is proposed in this thesis. The main part of the robot vehicle consists of eight SHARP middle-distance infrared sensors, two direct current (DC) motor, one DC motor driver board and ALTERA DE0-Nano experimental board. Two of the middle-distance infrared sensors is installed on the front, the other three infrared sensors were on the left and right sides. By the cooperation of these infrared sensors which are considered about their position and angle. The mobile robot's scanning range can up to 180 degrees. After receiving signal of infrared sensors from various directions and transmit them to CPU which is developed by ourselves, then process with the coding of firmware. The best executing mode can be chosen exactly and then make the corresponding action to control motor's rotation. Finally, this system is implemented and tested in the actual environment. The results show that the robot vehicle can achieve a goal which is following wall properly.
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WANG, HAO-HSUAN, and 王皓暄. "Narrow-Tunnel Wall-Following Control Design for Mobile Robot." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/66wz2k.

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碩士
國立暨南國際大學
電機工程學系
107
In recent years, reduce human labor force become a critical issue worldwide. There has been an increase need for automated manufacturing. Major companies around the world have also begun to invest in automation-related industries. Since Taiwan is a famous foundry country in the world, the requirements for automated facilities have also created the demand for automated robots. In the meantime, robot industry become a popular topic. In order to let the robot can move freely in any terrain. Sensor and algorithm of robot is crucial for this problem. This thesis propose a new algorithm for robot to enter a narrower tunnel then previous version.
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Chen, Ying-Han, and 陳盈翰. "Hexapod Robot Wall-Following Control Using Fuzzy Controller with Advanced Differential Evolution." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/08498478739892016808.

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碩士
國立中興大學
電機工程學系所
100
This thesis proposes advanced species differential evolution (ASDE) algorithm designed fuzzy controller (FC) to perform hexapod robot wall following. The ASDE uses the concept of the species (clustering). The solution vectors are clustered into different species based on their performances at each iteration. The ASDE dynamically generates a species-based mutant vector or a general mutant vector in the mutation operation according to an iteration-based adaptive probability value. The ASDE is applied to design an FC for robot wall-following control. All of the free parameters in the FC are learned through ASDE, which avoids the time-consuming manual design task. The FC inputs are three infrared distance sensor values. The FC controls the swing angle changes of the left- and right-middle legs of the hexapod robot to perform a suitable turning direction while moving forward at the same time. A new cost function is defined to quantitatively evaluate the performance an FC. Two different training environments are created for building this wall-following behavior without an exhaustive collection of input-output training pairs in advance. Simulations are conducted to verify the effectiveness of the evolutionary wall-following learning approach. Comparisons with other advanced differential evolution algorithms show that the ASDE achieves better performance in the wall-following control task.
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Hung, Yi-Jan, and 洪翊展. "Wall-Following Hexapod Walking Robot Using Fuzzy Neural Network and Locomotion Control." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/a4326y.

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碩士
國立中央大學
電機工程學系
105
This thesis applies a fuzzy neural network controller and Kalman filter to control the hexapod for wall following and efficiently adjust the gait to realize stability locomotion. According to the angle position, measured by ultrasonic sensor, between the robot and the wall, the fuzzy neural network controller can control the swing amplitude of the left and right legs of the robot, so that the robot can walk in the complex environment successfully. In addition to walking in an unknown environment, the stability of the hexapod is also a very important theme. The Kalman filter uses an accelerometer and a gyroscope to obtain the real-time robot body attitude, while the tilt angles are separated to the leg directions to change the amplitude by inverse kinematics. Thus, the robot can move forward, and instantly restore horizontal body attitude when walking on oblique terrain. The experimental results show that the method proposed in this research can successfully applied to a real hexapod robot control.
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Du, Hong-Yi, and 杜宏逸. "Mobile Robot Wall-Following Control Using Fuzzy Controller with Evolutionary Reinforcement Learning." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/jhbjga.

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碩士
國立虎尾科技大學
電機工程系碩士班
105
In this thesis, an improved differential search algorithm (IDS) is proposed to optimize the fuzzy logic controller (FLC) with the reinforcement learning (FLC_R-IDS). Using the reward and punishment mechanism of reinforcement learning to train the mobile robot wall-following control. The differential search algorithm has the ability to search for solution space, but often fall into the local best solution. Therefore, this study proposes an improved differential search algorithm, which uses parameter adaptation to adjust the control parameters (p1, p2).Change the number of superorganism involved in the stopover site to improve the exploration ability of the algorithm. In the training environment, the four sensors on the right side of the mobile robot are the input of the FLC. The output is the left wheel and right wheel speed. This study uses reinforcement learning to guide the behavior of the robot. Design three reward conditions as whether correct of robot behavior. These three conditions are for the distance follow the wall, judge the road ahead and avoid robots stop. When the mobile robot satisfy three reward conditions at the same time, get reward +1. On the contrary, once the mobile robot violates the three reward conditions, stop the current controller training, the accumulated reward value is used as the basis for the controller evaluation. And replace the next controller training. As a controller of the reward value of the cumulative amount of 6000, decided to learn finished. This study trained mobile robots along the wall in a simple environment, testing the learning controller in complex environments. Each algorithm independently trained 30 controllers for testing and comparison. The experimental results show that the improved differential search algorithm optimize the FLC has a better learning rate than the original differential search algorithm optimize the FLC. The performance better and stable than the original differential search algorithm optimize the FLC and chaos differential search algorithm optimize the FLC. Finally, the completed learn controller is tested in the training environment and the test environment. The experimental results show that the mobile robot using the fuzzy controller with evolutionary reinforcement learning can effectively implement the behavior along the wall.
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HSU, Cheng-You, and 徐承佑. "Design of a Fuzzy Controller for the Wall Following Behavior of a Robot." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/44540250570460183083.

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碩士
國立中興大學
電機工程學系所
95
The aim of this thesis is to design a fuzzy controller for a robot system, which is capable of executing the wall-following behavior and the adaptation under varying conditions, such as obstacle avoidance, making turns, and wall-searching. With these capabilities, the proposed robot is competent to walk along the walls in any environment. Furthermore, with the concept of neural network, an adaptive fuzzy controller is designed based on the back-propagation algorithm. Using the information obtained from the infrared sensor, the control output is computed. To improve the performance of the system, the error of the system is utilized to modify the parameters of the adaptive fuzzy controller according to the gradient descent updating rule. Through the simulation and experiments, the proposed robot system has been proved to be reliable and effective. Through the simulation and experiments, the robot system proposed has been proved to be reliable and effective.
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Books on the topic "Wall following robot"

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Wang, Chih-Ming. A robust estimator for wall following. Warren, Mich: General Motors Research Laboratories, 1987.

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Book chapters on the topic "Wall following robot"

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Bräunl, Thomas. "Wall Following." In Robot Adventures in Python and C, 65–70. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38897-3_6.

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Madi, Sarah, and Riadh Baba-Ali. "Classification Techniques for Wall-Following Robot Navigation: A Comparative Study." In Advances in Intelligent Systems and Computing, 98–107. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99010-1_9.

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Paykari, Nasim, Seyed Hamidreza Abbasi, and Faridoon Shabaninia. "Design of MIMO Mamdani Fuzzy Logic Controllers for Wall Following Mobile Robot." In Soft Computing Applications, 155–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-33941-7_16.

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Iglesias, R., C. V. Regueiro, J. Correa, and S. Barro. "Supervised reinforcement learning: Application to a wall following behaviour in a mobile robot." In Tasks and Methods in Applied Artificial Intelligence, 300–309. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/3-540-64574-8_416.

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Ding, Cheng-jun, Ping Duan, Ming-lu Zhang, and Yan-hui Han. "Wall Following of Mobile Robot Based on Fuzzy Genetic Algorithm of Linear Interpolating." In Advances in Soft Computing, 1579–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03664-4_167.

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Wu, Peipei, Menglin Fang, and Zuohua Ding. "Wall-Following Navigation for Mobile Robot Based on Random Forest and Genetic Algorithm." In Intelligent Computing Theories and Application, 122–31. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-84529-2_11.

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Zhu, Kongtao, Chensheng Cheng, Can Wang, and Feihu Zhang. "Wall-Following Control of Multi-robot Based on Moving Target Tracking and Obstacle Avoidance." In Communications in Computer and Information Science, 534–41. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7983-3_47.

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Pinheiro, Pedro Gabriel Calíope Dantas, Maikol Magalhães Rodrigues, João Paulo Agostinho Barrozo, Jose Pacelli Moreira de Oliveira, Plácido Rogério Pinheiro, and Raimir Holanda Filho. "A Mobile Terrestrial Surveillance Robot Using the Wall-Following Technique and a Derivative Integrative Proportional Controller." In Intelligent Systems in Cybernetics and Automation Control Theory, 276–86. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00184-1_26.

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Lai, Min-Ge, Chia-Feng Juang, and I.-Fang Chung. "Evolutionary Fuzzy Control of Three Robots Cooperatively Carrying an Object for Wall Following Through the Fusion of Continuous ACO and PSO." In Lecture Notes in Computer Science, 225–32. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61833-3_23.

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Narvydas, Gintautas, Vidas Raudonis, and Rimvydas Simutis. "Expert Guided Autonomous Mobile Robot Learning." In Knowledge-Based Intelligent System Advancements, 216–31. IGI Global, 2011. http://dx.doi.org/10.4018/978-1-61692-811-7.ch011.

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In the control of autonomous mobile robots there exist two types of control: global control and local control. The requirement to solve global and local tasks arises respectively. This chapter concentrates on local tasks and shows that robots can learn to cope with some local tasks within minutes. The main idea of the chapter is to show that, while creating intelligent control systems for autonomous mobile robots, the beginning is most important as we have to transfer as much as possible human knowledge and human expert-operator skills into the intelligent control system. Successful transfer ensures fast and good results. One of the most advanced techniques in robotics is an autonomous mobile robot on-line learning from the experts’ demonstrations. Further, the latter technique is briefly described in this chapter. As an example of local task the wall following is taken. The main goal of our experiment is to teach the autonomous mobile robot within 10 minutes to follow the wall of the maze as fast and as precisely as it is possible. This task also can be transformed to the obstacle circuit on the left or on the right. The main part of the suggested control system is a small Feed-Forward Artificial Neural Network. In some particular cases – critical situations – “If-Then” rules undertake the control, but our goal is to minimize possibility that these rules would start controlling the robot. The aim of the experiment is to implement the proposed technique on the real robot. This technique enables to reach desirable capabilities in control much faster than they would be reached using Evolutionary or Genetic Algorithms, or trying to create the control systems by hand using “If-Then” rules or Fuzzy Logic. In order to evaluate the quality of the intelligent control system to control an autonomous mobile robot we calculate objective function values and the percentage of the robot work loops when “If-Then” rules control the robot.
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Conference papers on the topic "Wall following robot"

1

Imhof, Agnes, Moritz Oetiker, and Bjorn Jensen. "Wall following for autonomous robot navigation." In 2012 2nd International Conference on Applied Robotics for the Power Industry (CARPI 2012). IEEE, 2012. http://dx.doi.org/10.1109/carpi.2012.6473370.

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Amir and R. Iraji. "Robot path planning usingwavefront approach with wall-following." In 2009 2nd IEEE International Conference on Computer Science and Information Technology. IEEE, 2009. http://dx.doi.org/10.1109/iccsit.2009.5234918.

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Joo, Kyeong-Jin, Sang-Hyeon Bae, Arpan Ghosh, Hyun-Jin Park, and Tae-Yong Kuc. "Wall following navigation algorithm for a disinfecting robot." In 2022 19th International Conference on Ubiquitous Robots (UR). IEEE, 2022. http://dx.doi.org/10.1109/ur55393.2022.9826258.

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Purnama, Hendril Satrian, Tole Sutikno, Nuryono Satya Widodo, and Srinivasan Alavandar. "Efficient PID Controller based Hexapod Wall Following Robot." In 2019 6th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI). IEEE, 2019. http://dx.doi.org/10.23919/eecsi48112.2019.8976964.

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Yang, Zhi-Yuan, Chia-Feng Juang, and Yue-Hua Jhan. "Hexapod robot wall-following control using a fuzzy controller." In 2014 11th IEEE International Conference on Control & Automation (ICCA). IEEE, 2014. http://dx.doi.org/10.1109/icca.2014.6870982.

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Dash, Tirtharaj, Soumya Ranjan Sahu, Tanistha Nayak, and Goutam Mishra. "Neural network approach to control wall-following robot navigation." In 2014 International Conference on Advanced Communication, Control and Computing Technologies (ICACCCT). IEEE, 2014. http://dx.doi.org/10.1109/icaccct.2014.7019262.

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Karambakhsh, A., M. Yousefi Azar Khanian, M. R. Meybodi, and A. Fakharian. "Robot navigation algorithm to wall following using fuzzy Kalman filter." In 2011 9th IEEE International Conference on Control and Automation (ICCA). IEEE, 2011. http://dx.doi.org/10.1109/icca.2011.6138043.

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Wardana, Ananta Adhi, Augie Widyotriatmo, Suprijanto, and Arjon Turnip. "Wall following control of a mobile robot without orientation sensor." In 2013 3rd International Conference on Instrumentation Control and Automation (ICA). IEEE, 2013. http://dx.doi.org/10.1109/ica.2013.6734074.

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Paul, Sujni, and C. Beulah Christalin Latha. "Shortest path traversal in a maze with wall following robot." In 2ND INTERNATIONAL CONFERENCE ON ENERGETICS, CIVIL AND AGRICULTURAL ENGINEERING 2021 (ICECAE 2021). AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0116117.

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Li, Xiao, and Dan Wang. "Behavior-based mamdani fuzzy controller for mobile robot wall-following." In 2015 International Conference on Control, Automation and Robotics (ICCAR). IEEE, 2015. http://dx.doi.org/10.1109/iccar.2015.7166006.

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