Relevant bibliographies by topics / Wall following algorithm

Academic literature on the topic 'Wall following algorithm'

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Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Wall following algorithm"

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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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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.

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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.
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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.

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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.
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Wiegmann, Aaron Lee, David A. Hill, Thomas Q. Xu, Anuja K. Antony, and Keith C. Hood. "Chest wall reconstruction following iatrogenic Eloesser-type wounds: The rush algorithm." Journal of Plastic, Reconstructive & Aesthetic Surgery 72, no. 10 (October 2019): 1700–1738. http://dx.doi.org/10.1016/j.bjps.2019.06.038.

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Losken, A., V. H. Thourani, G. W. Carlson, G. E. Jones, J. H. Culbertson, J. I. Miller, and K. A. Mansour. "A reconstructive algorithm for plastic surgery following extensive chest wall resection." British Journal of Plastic Surgery 57, no. 4 (June 2004): 295–302. http://dx.doi.org/10.1016/j.bjps.2004.02.004.

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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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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.

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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.
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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.

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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.
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Maulidin, Indra, Muliady Muliady, and Yohana Susanthi. "Rancang Bangun Quadcopter untuk Terbang Mengikuti Dinding Menggunakan Sensor Jarak Ultrasonik HC-SR04." TELKA - Telekomunikasi Elektronika Komputasi dan Kontrol 6, no. 2 (November 24, 2020): 75–84. http://dx.doi.org/10.15575/telka.v6n2.75-84.

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Makalah ini memaparkan perancangan dan realisasi sebuah quadcopter menggunakan sensor jarak ultrasonic HC-SR04 agar dapat terbang mengikuti dinding. Algoritma yang digunakan pada realisasi quadcopter adalah algoritma Wall Follower. Fokus pembahasan sistem tersebut adalah mengimplementasikan algoritma Wall Follower pada quadcopter agar dapat terbang navigasi dalam ruang dengan cara mengikuti dinding. Kontrol pergerakan roll menggunakan mode pengontrol ON-OFF Hysteresis sedangkan pada pergerakan yaw digunakan mode pengontrol open-loop. Nilai batas bawah dan batas atas yang digunakan pada kontrol ON-OFF Hysteresis adalah 70 cm dan 100 cm. Hasil pengujian menunjukkan quadcopter berhasil terbang mengikuti dinding sejauh 10,2 m dengan ketinggian maksimum 2,14 m. This paper explained the design and realization of a quadcopter using the HC-SR04 ultrasonic distance sensor so that it can fly following the wall. The algorithm used in the quadcopter design is the Wall Follower algorithm. The focus of the discussion of the system is implementing a Wall Follower algorithm to the quadcopter so that it can navigate following the wall. The roll movement control was using Hysteresis ON-OFF control and the yaw movement control was using open-loop control. The lower and upper limit values that were used in the Hysteresis ON-OFF control are 70 cm and 100 cm. The test results show the quadcopter can fly following the wall as far as 10.2 m and reaches a maximum height of 2,14 m.
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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.

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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.
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Dissertations / Theses on the topic "Wall following algorithm"

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Lin, Shih-Hao, and 林士豪. "Mobile Robot Wall-Following Control Using An Improved Artificial Bee Colony Algorithm for A Compensatory Fuzzy Logic Controller Design." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/33v3xs.

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碩士
國立虎尾科技大學
電機工程研究所
102
This dissertation proposes an improved artificial bee colony (IABC) algorithm for designing a compensatory fuzzy logic controller (CLFC) in order to achieve an actual mobile robot wall-following task. During the wall-following task, the CFLC inputs measure the distance between the ultrasonic sensors and the wall, and the outputs of the CFLC are the robot''s left-wheel and right-wheel speeds. A cost function is defined to evaluate the performance of the CFLC in the wall-following task. The cost function includes three control factors (CF) which are defined as follows: maintaining a user-defined robot-wall distance, avoiding robot-wall collision, and ensuring that the robot can successfully negotiate the venue. The original artificial bee colony algorithm (ABC) simulates the intelligent foraging behavior of honey-bee swarms, which are good at exploration but poor at exploitation. An improved ABC algorithm, the IABC algorithm, is proposed that adopts the mutation strategies of differential evolution to balance exploration and exploitation. The IABC algorithm applies a new reward-based roulette wheel selection where an obtained a better solution by gains a reward during the learning stage. To demonstrate the performance of the IABC designed CFLC, the method was compared with other population-based algorithms with respect to the efficiency of the wall-following task. To demonstrate the feasibility of the design, experiments carried out on an actual mobile robot (PIONEER 3-DX) are included in this research.
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Book chapters on the topic "Wall following algorithm"

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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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Meel, Priyanka, Ritu Tiwari, and Anupam Shukla. "Optimization of Focused Wave Front Algorithm in Unknown Dynamic Environment for Multi-Robot Navigation." In Rapid Automation, 553–81. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8060-7.ch026.

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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.
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Penrose, Roger, and Martin Gardner. "Algorithms and Turing Machines." In The Emperor's New Mind. Oxford University Press, 1989. http://dx.doi.org/10.1093/oso/9780198519737.003.0010.

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What Precisely is an algorithm, or a Turing machine, or a universal Turing machine? Why should these concepts be so central to the modern view of what could constitute a ‘thinking device’? Are there any absolute limitations to what an algorithm could in principle achieve? In order to address these questions adequately, we shall need to examine the idea of an algorithm and of Turing machines in some detail. In the various discussions which follow, I shall sometimes need to refer to mathematical expressions. I appreciate that some readers may be put off by such things, or perhaps find them intimidating. If you are such a reader, I ask your indulgence, and recommend that you follow the advice I have given in my ‘Note to the reader’ on p. viii! The arguments given here do not require mathematical knowledge beyond that of elementary school, but to follow them in detail, some serious thought would be required. In fact, most of the descriptions are quite explicit, and a good understanding can be obtained by following the details. But much can also be gained even if one simply skims over the arguments in order to obtain merely their flavour. If, on the other hand, you are an expert, I again ask your indulgence. I suspect that it may still be worth your while to look through what I have to say, and there may indeed be a thing or two to catch your interest. The word ‘algorithm’ comes from the name of the ninth century Persian mathematician Abu Ja’far Mohammed ibn Mûsâ alKhowârizm who wrote an influential mathematical textbook, in about 825 AD, entitled ‘Kitab al-jabr wa’l-muqabala’. The way that the name ‘algorithm’ has now come to be spelt, rather than the earlier and more accurate ‘algorism’, seems to have been due to an association with the word ‘arithmetic’. (It is noteworthy, also, that the word ‘algebra’ comes from the Arabic ‘al-jabr’ appearing in the title of his book.) Instances of algorithms were, however, known very much earlier than al-Khowârizm’s book.
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Donovan, Therese M., and Ruth M. Mickey. "The White House Problem Revisited: MCMC with the Metropolis–Hastings Algorithm." In Bayesian Statistics for Beginners, 224–46. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198841296.003.0015.

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The “White House Problem” of Chapter 10 is revisited in this chapter. Markov Chain Monte Carlo (MCMC) is used to build the posterior distribution of the unknown parameter p, the probability that a famous person could gain access to the White House without invitation. The chapter highlights the Metropolis–Hastings algorithm in MCMC analysis, describing the process step by step. The posterior distribution generated in Chapter 10 using the beta-binomial conjugate is compared with the MCMC posterior distribution to show how successful the MCMC method can be. By the end of this chapter, the reader will have a firm understanding of the following concepts: Monte Carlo, Markov chain, Metropolis–Hastings algorithm, Metropolis–Hastings random walk, and Metropolis–Hastings independence sampler.
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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 algorithm"

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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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Behrje, Ulrich, Cedric Isokeit, Benjamin Meyer, Kristian Ehlers, and Erik Maehle. "AUV-based Quay Wall Inspection Using a Scanning Sonar-based Wall Following Algorithm." In OCEANS 2022 - Chennai. IEEE, 2022. http://dx.doi.org/10.1109/oceanschennai45887.2022.9775411.

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Wei, Xin, Erbao Dong, Chunshan Liu, Guangming Han, and Jie Yang. "A wall-following algorithm based on dynamic virtual walls for mobile robots navigation." In 2017 IEEE International Conference on Real-time Computing and Robotics (RCAR). IEEE, 2017. http://dx.doi.org/10.1109/rcar.2017.8311834.

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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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Suwoyo, Heru, Yingzhong Tian, Chenwei Deng, and Andi Adriansyah. "Improving a Wall-Following Robot Performance with a PID-Genetic Algorithm Controller." In 2018 5th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI). IEEE, 2018. http://dx.doi.org/10.1109/eecsi.2018.8752907.

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Prayudhi, Lafin Hari, Augie Widyotriatmo, and Keum-Shik Hong. "Wall following control algorithm for a car-like wheeled-mobile robot with differential-wheels drive." In 2015 15th International Conference on Control, Automation and Systems (ICCAS). IEEE, 2015. http://dx.doi.org/10.1109/iccas.2015.7364726.

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Madi, Sarah, and Riadh Baba-Ali. "Comparison of Classification Techniques for Wall Following Robot Navigation and Improvements to the KNN Algorithm." In 9th International Conference on Computer Science, Engineering and Applications. Aircc publishing Corporation, 2019. http://dx.doi.org/10.5121/csit.2019.91806.

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Chen, C., H. Du, and S. Lin. "Mobile robot wall-following control by improved artificial bee colony algorithm to design a compensatory fuzzy logic controller." In 2017 14th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). IEEE, 2017. http://dx.doi.org/10.1109/ecticon.2017.8096373.

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Whitney, Jon, Harry Dorn, Chris Rylander, Tom Campbell, David Geohegan, and Marissa Nichole Rylander. "Spatiotemporal Temperature and Cell Viability Measurement Following Laser Therapy in Combination With Carbon Nanohorns." In ASME 2010 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2010. http://dx.doi.org/10.1115/sbc2010-19619.

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Cancer remains one of the most deadly diseases today. Laser-induced photothermal therapy can provide a minimally invasive treatment alternative to surgical resection. The selectivity and effectiveness of laser therapy can be greatly enhanced when photoabsorbing nanoparticles such as nanoshells, single walled carbon nanotubes, multi-walled carbon nanotubes, or single wall carbon nanohorns (SWNHs) are introduced into the tissue. Prior studies have effectively used SWNHs combined with near infrared (NIR) laser light to target and destroy microbes [1]. We have previously reported increased tumor cell destruction when SWNHs were used in combination with laser therapy. The present work provides more extensive characterization of cell viability in response to laser therapy alone or in combination with SWNHs. Furthermore, the spatiotemporal temperature and cell viability in vitro in response to combinatorial SWNH-mediated laser therapies is determined using infrared thermometry and a novel viability algorithm, respectively. These new measurements will be critical for planning SWNH-mediated laser treatments where knowledge of the geometric distribution of temperature and cell death are critical to achieving the goal of selectively eliminating a tumor with specific spatial margins with minimal damage to surrounding healthy tissue.
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Sandberg, R. D., R. Tan, J. Weatheritt, A. Ooi, A. Haghiri, V. Michelassi, and G. Laskowski. "Applying Machine Learnt Explicit Algebraic Stress and Scalar Flux Models to a Fundamental Trailing Edge Slot." In ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/gt2018-75444.

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A form of supervised machine learning was applied to highly resolved large-eddy simulation (LES) data to develop non linear turbulence stress and heat flux closures with increased prediction accuracy for trailing-edge cooling slot cases. The LES data were generated for a thick and a thin trailing-edge slot and shown to agree well with experimental data, thus providing suitable training data for model development. A Gene Expression Programming (GEP) based algorithm was used to symbolically regress novel nonlinear Explicit Algebraic Stress Models (EASM) and heat-flux closures based on either the gradient diffusion or the generalized gradient diffusion approaches. Following a-priori assessment, the new models were used for steady RANS calculations of both thin and thick trailing-edge slot geometries, testing their performance and robustness. Overall, the best agreement with LES data was found when training the RANS model in the near wall region where high levels of anisotropy exist and using the mean squared error of the anisotropy tensor as cost function. In the case of the thin lip geometry, combining an improved EASM model with the standard eddy-diffusivity model predicted the adiabatic wall effectiveness in good agreement with the LES and experimental data. Crucially, the obtained model was also applied to different blowing ratios of the thin lip geometry and a significant improvement in the predictive accuracy of adiabatic wall effectiveness was observed for those cases not previously seen in the training process. For the thick lip case the match with reference values deteriorated due to the presence of large-scale, relative to the slot height, vortex shedding. The machine-learning algorithm was therefore also used to ‘learn’ an appropriate closure for the turbulent heat flux vector. The constructed scalar flux model, in conjunction with a trained RANS model, was found to have the capability to further improve the prediction of the adiabatic wall effectiveness.
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