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Статті в журналах з теми "Wall following robot"
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.
Повний текст джерелаSuherman, 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.
Повний текст джерелаToibero, 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.
Повний текст джерелаSuwoyo, 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.
Повний текст джерелаMuthugala, 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.
Повний текст джерелаLarasati, 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.
Повний текст джерелаTeng, 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.
Повний текст джерелаAndo, 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.
Повний текст джерелаSoetedjo, 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.
Повний текст джерелаZenita, 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.
Повний текст джерелаДисертації з теми "Wall following robot"
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.
Повний текст джерелаVožda, 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.
Повний текст джерелаChien, Chung-Wei, and 簡宗緯. "Mobile-Robot Wall-Following Control Design." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/21392174241916514565.
Повний текст джерела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.
Dai-Hua, Jinag, and 江岱樺. "Wall-Following Fuzzy Control for Mobile Robot." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/79334524305267305766.
Повний текст джерела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.
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.
Повний текст джерела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.
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.
Повний текст джерела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.
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.
Повний текст джерела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.
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.
Повний текст джерела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.
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.
Повний текст джерела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.
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.
Повний текст джерела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.
Книги з теми "Wall following robot"
Wang, Chih-Ming. A robust estimator for wall following. Warren, Mich: General Motors Research Laboratories, 1987.
Знайти повний текст джерелаЧастини книг з теми "Wall following robot"
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.
Повний текст джерелаMadi, 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.
Повний текст джерелаPaykari, 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.
Повний текст джерелаIglesias, 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.
Повний текст джерелаDing, 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.
Повний текст джерелаWu, 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.
Повний текст джерелаZhu, 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.
Повний текст джерелаPinheiro, 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.
Повний текст джерелаLai, 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.
Повний текст джерелаNarvydas, 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.
Повний текст джерелаТези доповідей конференцій з теми "Wall following robot"
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.
Повний текст джерелаAmir 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.
Повний текст джерелаJoo, 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.
Повний текст джерелаPurnama, 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.
Повний текст джерелаYang, 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.
Повний текст джерелаDash, 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.
Повний текст джерелаKarambakhsh, 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.
Повний текст джерелаWardana, 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.
Повний текст джерелаPaul, 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.
Повний текст джерелаLi, 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.
Повний текст джерела