Relevant bibliographies by topics / Maze Solving Robot

Academic literature on the topic 'Maze Solving Robot'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Maze Solving Robot"

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., Akib Islam. "SHORTEST DISTANCE MAZE SOLVING ROBOT." International Journal of Research in Engineering and Technology 05, no. 07 (July 25, 2016): 253–59. http://dx.doi.org/10.15623/ijret.2016.0507038.

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Latha gade, Mary swarna, GAjitha GAjitha, and Deepthi S. "Design and Implementation of Swam Robotics using Flood Fill Algorithm." IAES International Journal of Robotics and Automation (IJRA) 6, no. 4 (December 1, 2017): 269. http://dx.doi.org/10.11591/ijra.v6i4.pp269-276.

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<p>Swam Intelligence provides a basis with which it is possible to explore collective (or distributed) problem solving without centralized control or the provision of a global model. This paper presents design and implementation of swam robotics in a multi-agent environment. At the beginning, robot agents are ignorant of the maze. The robots are programmed with Flood fill algorithm to solve maze. The robot scans maze and stores the values in EEPROM. The robot agent shares the information to other robot agents through wireless communication. The proposed flood fill algorithm is found to be effective tool for solving maze of moderate size.</p>
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Bansal, Arun, and Sunil Agrawal. "A Robust Maze Solving Algorithm for a Micromouse Robot." Advanced Materials Research 403-408 (November 2011): 4762–68. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.4762.

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The problem of micromouse is 30 years old but its importance in the field of robotics is unparalleled, as it requires a complete analysis & proper planning to solve the problem. This paper proposes two computationally efficient algorithms for maze solving. Most of the reported algorithms explore complete maze before finding the shortest path to the center of the maze, thereby taking large exploration time. Our proposed algorithms take less time in maze exploration by exploring the maze partially and by traversing less number of cells to reach the center of the maze. An optimal method for maze solving with partial exploration of the maze can hardly be found applicable to all type of mazes, but our algorithms are found to be efficient in most of the cases. Simulation of our proposed algorithms on the some standard mazes show that one of the two algorithms described in this paper gives the better solution by only partially exploring the maze and the other algorithm also gives the comparative results when compared with the algorithms reported in the literature.
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Kumar, Rahul, Peni Jitoko, Sumeet Kumar, Krishneel Pillay, Pratish Prakash, Asneet Sagar, Ram Singh, and Utkal Mehta. "Maze Solving Robot with Automated Obstacle Avoidance." Procedia Computer Science 105 (2017): 57–61. http://dx.doi.org/10.1016/j.procs.2017.01.192.

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del Rosario, Jay Robert B., Jefferson G. Sanidad, Allimzon M. Lim, Pierre Stanley L. Uy, Allan Jeffrey C. Bacar, Mark Anthony D. Cai, and Alec Zandrae A. Dubouzet. "Modelling and Characterization of a Maze-Solving Mobile Robot Using Wall Follower Algorithm." Applied Mechanics and Materials 446-447 (November 2013): 1245–49. http://dx.doi.org/10.4028/www.scientific.net/amm.446-447.1245.

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This paper is about a robot that would be able to solve mazes or labyrinths and look for the exit. The project will utilize the PIC microcontroller. This is like those in the micromouse competitions since it resembles a mouse put in a labyrinth searching for its cheese. This would also implement the Wall Follower algorithm to solve the maze and will use proximity sensors to detect the walls of the labyrinth. The robot would be as small as possible as to make its navigation of the maze more efficient in terms of turning left or right, or even doing a full U-turn since a bulky robot would have a hard time navigating the turns in the maze. This project would also require an 8x8-tiled square maze to effectively apply the algorithm and to have a test labyrinth to work on.
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Fahmi, Ismu Rijal, and Dwi Joko Suroso. "A Simulation-Based Study of Maze-Solving-Robot Navigation for Educational Purposes." Journal of Robotics and Control (JRC) 3, no. 1 (August 18, 2021): 48–54. http://dx.doi.org/10.18196/jrc.v3i1.12241.

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The point of education in the early stage of studying robotics is understanding its basic principles joyfully. Therefore, this paper creates a simulation program of indoor navigations using an open-source code in Python to make navigation and control algorithms easier and more attractive to understand and develop. We propose the maze-solving-robot simulation as a teaching medium in class to help students imagine and connect the robot theory to its actual movement. The simulation code is built for free to learn, improve, and extend in robotics courses or assignments. A maze-solving robot study case is then done as an example of implementing navigation algorithms. Five algorithms are compared, such as Random Mouse, Wall Follower, Pledge, Tremaux, and Dead-End Filling. Each algorithm is simulated a hundred times in every type of the proposed mazes, namely mazes with dead ends, loops only, and both dead ends and loops. The observed indicators of the algorithms are the success rate of the robots reaching the finish lines and the number of steps taken. The simulation results show that each algorithm has different characteristics that should be considered before being chosen. The recommendation of when-to-use the algorithms is discussed in this paper as an example of the output simulation analysis for studying robotics.
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Zhang, Hao Ming, Lian Soon Peh, and Ying Hai Wang. "Study on Flood-Fill Algorithm Used in Micromouse Solving Maze." Applied Mechanics and Materials 599-601 (August 2014): 1981–84. http://dx.doi.org/10.4028/www.scientific.net/amm.599-601.1981.

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Micromouse is a small autonomous electronic-mechanical robot, which is able to navigate through an unknown maze from the start to the destination. The main challenge for micromouse is to work out the maze after searching, find the optimum path for the shortest fast-run time and control the robot to win. Flood-fill algorithm is used in this project to develop a solver which is able to help the robot for finding the ‘best’ route from the starting position to the ‘goal’, maze solving result shows the correctness.
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Hamdikatama, Bimantyoso, Arif Setyanto, and Ferry Wahyu Wibowo. "Analisis Software Robot Sterilisasi Ruangan Menggunakan Kendali Maze Solving." RESEARCH : Journal of Computer, Information System & Technology Management 4, no. 2 (June 10, 2021): 142. http://dx.doi.org/10.25273/research.v4i2.8214.

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<p><em><span>Ruangan ibadah berjamaah adalah tempat yang sering digunakan banyak orang secara bersamaan dan menjadi tempat potensial guna penyebaran Corona.Virus yang di bawa oleh orang terinfeksi dapat menempel pada lantai , tembok kursi dan isi ruangan sehingga terhirup oleh jamaah saat melakukan ibadah, sehingga diperlukan alat guna membantu mensterilkan ruangan ibadah secara efisien. Dengan robot yang dilengkapi dengan sinar UVC diharapkan dapat membantu proses sterilisiasi secara efektif. Tujuan penelitian ini adalah guna menganalisa kinerja sistem robot sterilisasi dengan kendali Maze Solving, guna dapat bekerja dengan gerak lurus dan berputar. Pembaruan pada penelitian ini adalah penerapan kendali menggunakan algoritma Maze Solving pada robot sterilisasi ruangan. Penelitian ini dilakukan dengan tahap analisa, pengkodean, dan uji coba. Algoritma Maze Solving dengan KP=10, Ki=0 dan Kd=120 dengan kombinasi tambahan rotary encoder, sensor kompas dan sensor jarak. Hasil uji memperlihatkan peningkatan kerja robot dengan nilai standart deviasi tertinggi guna poin kelurusan gerak 10,48 menjadi 0,34 ketepatan jarak tempuh dari 10,4 menjadi 0,42 . Waktu yang dibutuhkan robot sterilisasi pada ruangan 10 m x 10 m adalah 54,5 menit dan pembacaan halangan rintang dengan sensor jarak akurat dibawah 60cm.</span></em></p>
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Zhang, Hao Ming, Lian Soon Peh, and Ying Hai Wang. "Software Design of Micromouse Motion Control." Applied Mechanics and Materials 602-605 (August 2014): 989–92. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.989.

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Micromouse is a small autonomous robot, which must navigate itself through an unknown maze from the start to the destination.One of the main challenges for micromouse is to control its two motors, which are used to control micromouse’s motion in solving the maze. Detailed design of an improved PD controller is given,which has to update the velocity and acceleration values of the motors when it wants to control the robot. Motion profile generator and motion command execution routine is designed to output a smooth motion for the robots.
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Lee, Chyi Shyong, Juing Huei Su, Hsin Hsiung Huang, and Sin Mao Fu. "Line Maze Robot Training Course for the Senior High School Students." Applied Mechanics and Materials 278-280 (January 2013): 2156–59. http://dx.doi.org/10.4028/www.scientific.net/amm.278-280.2156.

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A line maze robot training course, which contains the hardware circuit implementation, firmware programming skills and the shortest path algorithm, is presented in this paper. The training course is used for vocation senior high school teachers and students. The major topic for students is to develop the proper firmware programming to achieve the better performance and students are encouraged to pass the small contest in the end of the workshop. The low-cost educational platform is easily by the economically disadvantaged students. Students, who join the workshop can learn the basic C language, the firmware programming skills for microcontrollers, the concept of the shortest path algorithm for line maze robot, just spend less $40 USD to buy the robots. The contributions of this training course are as follows: 1) studying the hardware implementation knowledge, 2) developing microcontroller-related firmware programming skills, 3) learning motion control skills of the line maze robot, and 4) learning line-maze solving algorithms. Few students, who join this training workshop and learn the related hardware, firmware and algorithm, can find the shortest path form source to target in the line map. The feedback of the most students shows that the training course gives them the motivation to actively learn the necessary practical skills to design the firmware programming to guide the line maze robot.
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Dissertations / Theses on the topic "Maze Solving Robot"

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Ottosson, Joachim, and Niklas Renström. "aMAZEing robot : A method for automatic maze solving." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-264491.

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The purpose of this report was to examine a method for automatic maze solving by developing a robot that can find a specific target within a maze. On this basis, a Arduino based robot was constructed. The steering mechanism was constructed in a way that supports differential steering which enables zero radius turns which is preferred in narrow spaces. The intersection detection as well as the method for defining which type of intersection occurring was based on three distance sensor mounted in front and on the left and right side of the robot. The target detection was based on a IR sensor. A feedback controller was applied on the left Ultrasonic sensor enabling the robot to keep a reference distance to the wall. The feedback system also enabled the robot to straighten up when taking to big or small turns. The robot was able to both detect and define what kind of intersection occurring as well as detect the specific target. The execution of the correct operation by detected intersection was calculated to 80% - 100% and the target was found 100% out of the test made. The definition of ”correct operation” in this report was when the robot detected a intersection, and executed the operation that was in accordance with the implemented algorithm. The rotational error, that is the quantity of degrees from the desired turn angle, occurring when the robot executed different operations was calculated to 3.5◦ - 9.5◦ . The robot started to oscillate due to angles grater than 19.5◦ when the left distance sensor was facing the wall which made the robot less able to follow a path and straighten up.
Syftet med denna rapport var att undersöka en metod för automatiserad labyrintlösning genom att utveckla en robot som kan finna ett specifikt mål i en labyrint. På dessa grunder konstruerades en arduinobaserad robot. Styrmekanismen konstruerades på sådant sätt att differentialstyrning var möjlig vilket tillät svängar utan svängradie, vilket är att föredra vid trånga utrymmen. Vägkorsningsdetekteringen samt metoden för att bestämma vilken typ av vägkorsning som förekommer baserades på tre distansmätare. Dessa var monterade på robotens front samt på höger och vänster sida. Måldetekteringen var baserad på en IR sensor. Ett system medåterkoppling implementerades på den vänstra distansmätaren för att få roboten att hålla ett referensavstånd till väggen. Detta möjliggjorde även så att vägen kunde följas på ett optimalt sätt samt att roboten kunde rätas upp vid för stora eller för små tagna svängar. Roboten lyckades både detektera samt definiera vilken typ av vägkorsning som uppstod likväl som att detektera det specifika målet. Andelen utförda korrekta operationer vid detekterande av vägkorsning beräknades till 80% - 100% av fallen och målet detekterades 100% av gångerna vid det gjorda testerna. Definitionen av ”korrekt operaton ”i denna rapport var då roboten upptäckte en vägkorsning och utförde den operation som var i enlighet med den implementerade algoritmen. Rotationsfelet, det vill säga antalet grader från den önskade svängvinkeln, som uppstod vid de olika operationerna beräknades till 3.5◦ - 9.5◦ . Roboten började att oscillera vid vinklar större än 19.5◦ då den vänstra distansmätaren var vänd mot väggen vilket gjorde roboten mindre duglig att följa väggen samt att rätas upp.
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Tang, Yuan-Shiun, and 湯杬勳. "Experimental Study of Maze Solving Algorithms in a Line Following Robot." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/ku6mz3.

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碩士
國立東華大學
電機工程學系
105
Since the artificial intelligence techniques developed, there have been a number of different maze solving algorithms in many publications. In this thesis, experimental results of the selected five maze solving algorithms performed in a line-following maze-solving micromouse robot are illustrated with comparison. The selected five algorithms contain the rules of the left-hand side, right-hand side, forward-first and left, forward-first and right, and left-first and right. All these algorithms are performed in the maze-solving micromouse robot. For practical experiments, an Arduino-based micromouse robot with the selected five maze-solving algorithms were developed. To give comparative discussion, experimental results from the developed micromouse robot are illustrated at last.
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Chandra, Alex, and Alex Chandra. "A Study on Maze Solving Algorithm Using Improved Potential Value Method for a Micromouse Robot." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/9cv753.

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碩士
國立臺北科技大學
機電整合研究所
98
Robotic technology is developing quickly in the last few decades. Robots in early generations could only do simple and repeatable works, but now robots need to be smarter. One of many different kind robots is an autonomous mobile robot whose task is making decisions to find its own moving path. A micromouse is a mini, mobile, and autonomous robot which is specifically designed to find a way from a starting point to a destination point in an unknown maze as fast as possible. An efficient algorithm developed for an intelligent robot is highly required to avoid being trapped in the maze and find the shortest path as soon as possible. Herein, an improved maze solving algorithm employing potential value method is proposed in this thesis. Simulation results show that the proposed algorithm is capable of identifying the path by giving different maze configurations and has better performance than the original algorithm from a conference paper based on student’s t-test results. Furthermore, the simulation program can simulate all stages in a micromouse competition, including path finding from the starting cell to the destination cell, from the destination cell to the starting cell, and running back reversely on the shortest path. After testing the algorithms using a simulation program, the algorithms are implemented into a real micromouse robot AIRAT 2, which is a commercial mouse. The evaluation for this implementation is mainly based on the maze solving algorithms to verify the shorter way to the target, while wall sensing, motor control and movement speed are not the major concerns. The evaluation result shows that AIRAT 2 can solve not all types of maze can be solved properly; it is not because a defect in the searching algorithm but the navigation method needs to be improved.
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Books on the topic "Maze Solving Robot"

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Sundström, Göran. Administrative Reform. Edited by Jon Pierre. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199665679.013.45.

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This chapter contains an analysis of Swedish administrative reform from the mid-1970s until today. It shows that Sweden has embraced most New Public Management ideas. Regarding management ideas Sweden was an early mover, whereas the neoliberal part of the NPM package took root quite late, from around 1990. In recent years Sweden has also embraced some “post-NPM” ideas. The chapter shows that the development partly can be understood as rational problem-solving. However, there are also observations supporting the argument that the development partly should be understood as rule-following and partly in terms of a path dependency. Regarding effects, critics argue that the reforms have brought about a more fragmented state, created a low-trust culture, made the pubic officials more silent, and generated paperwork which precludes the agencies from carrying out their ordinary work.
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Book chapters on the topic "Maze Solving Robot"

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Ferrari, Mario, Giulio Ferrari, and Ralph Hempel. "Solving a Maze." In Building Robots With Lego Mindstorms, 371–90. Elsevier, 2002. http://dx.doi.org/10.1016/b978-192899467-1/50067-7.

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"Solving a Maze." In Building Robots with LEGO Mindstorms NXT, 327–48. Elsevier, 2007. http://dx.doi.org/10.1016/b978-159749152-5/50022-x.

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Singh, Akanksha. "Modified Expression to Evaluate the Correlation Coefficient of Dual Hesitant Fuzzy Sets and Its Application to Multi-Attribute Decision Making." In Fuzzy Systems [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.96474.

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The main objective of this paper is to understand all the existing correlation coefficients (CoCfs) to determine the relation and dependency between two variables of the fuzzy sets and its extensions for solving decision-making (DM) problems. To study the weighted CoCfs between two variables the environment chosen here is dual hesitant fuzzy set (DHFS) which is a generalization of a fuzzy set which considers the hesitant value of both the membership and non-membership elements of a set. Although there exists CoCfs for DHFS but a detailed mathematical analysis suggests that there exists some shortcomings in the existing CoCfs for DHFS. Thus, an attempt has been made to properly understand the root cause of the posed limitation in the weighted CoCfs for DHFS and hence, modified weighted CoCfs for DHFS has been proposed for solving DHFS multi-attribute decision making (MADM) problems i.e., DM problems in which rating value of each alternative over each criterion is represented by a DHFS in the real-life. Also, to validate the proposed expressions of weighted CoCfs for solving DHFS MADM problems, an existing real-life problem is evaluated and a systematic comparison of the solution is presented for clarification.
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Sreenivasan, Gopal. "Emotion." In Emotion and Virtue, 36–66. Princeton University Press, 2020. http://dx.doi.org/10.23943/princeton/9780691134550.003.0003.

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This chapter talks about Ronald de Sousa's dramatic claim that emotions are species of determinate patterns of salience among objects of attention, lines of inquiry, and inferential strategies. It provides an adequate background on emotion and arguments on the integral view of the role of emotion in virtue. It also analyzes the psychology of emotion, a judicious smattering of philosophy that harness Christine Tappolet's overview. The chapter investigates the biological hypothesis on the function of emotion in solving the philosopher's frame problem, meaning the problem of how to make use of just what is needed from the stupendous quantity of knowledge and how not to retrieve what is not needed. It narrates a story from Daniel Dennett about a robot that is informed a bomb is set to go off in its vicinity that resents a moral that pure reason is not sufficient to solve de Sousa's problem.
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Tiwari, Ritu, Anupam Shukla, and Rahul Kala. "Hybrid Evolutionary Methods." In Rapid Automation, 295–336. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8060-7.ch014.

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The limitations of single algorithm approaches lead to an attempt to hybridize or fuse multiple algorithms in the hope of removing the underlying limitations. In this chapter, the authors study the evolutionary algorithms for problem solving and try to use them in a unique manner so as to get a better performance. In the first approach, they use an evolutionary algorithm for solving the problem of motion planning in a static environment. An additional factor called momentum is introduced that controls the granularity with which a robotic path is traversed to compute its fitness. By varying the momentum, the map may be treated finer or coarser. The path evolves along the generations, with each generation adding to the maximum possible complexity of the path. Along with complexity (number of turns), the authors optimize the total path length as well as the minimum distance from the obstacle in the robotic path. The requirement of evolutionary parameter individuals as well as the maximum complexity is less at the start and more at the later stages of the algorithm. Momentum is made to decrease as the algorithm proceeds. This makes the exploration vague at the start and detailed at the later stages. As an extension to the same work, in the second approach of the chapter, the authors show the manner in which a hybrid algorithm may be used in place of simple genetic algorithm for solving the problem with momentum. A Hybrid Genetic Algorithm Particle Swarm Optimization (HGAPSO) algorithm, which is a hybrid of a genetic algorithm and particle swarm optimization, is used in the same modeling scenario. In the third and last approach, the authors present a hierarchical evolutionary algorithm that operates in two hierarchies. The coarser hierarchy finds the path in a static environment consisting of the entire robotic map. The resolution of the map is reduced for computational speed. The finer hierarchy takes a section of the map and computes the path for both static and dynamic environments. Both these hierarchies carry optimization as the robot travels in the map. The static environment path gets more and more optimized along with generations. Hence, an extra setup cost is not required like other evolutionary approaches. The finer hierarchy makes the robot easily escape from the moving obstacle, almost following the path shown by the coarser hierarchy. This hierarchy extrapolates the movements of the various objects by assuming them to be moving with same speed and direction.
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Bonabeau, Eric, Marco Dorigo, and Guy Theraulaz. "Cooperative Transport by Insects and Robots." In Swarm Intelligence. Oxford University Press, 1999. http://dx.doi.org/10.1093/oso/9780195131581.003.0011.

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Collective robotics is a booming field, and cooperative transport—particularly cooperative box-pushing—has been an important benchmark in testing new types of robotic architecture. Although this task in itself is not especially exciting, it does provide insight into the design of collective problem-solving robotic systems. One of the swarm-based robotic implementations of cooperative transport that seems to work well is one that is closely inspired by cooperative prey retrieval in social insects. Ants of various species are capable of collectively retrieving large prey that are impossible for a single ant to retrieve. Usually, a single ant finds a prey item and tries to move it alone; when successful, the ant moves the item back to the nest. When unsuccessful, the ant recruits nestmates through direct contact or trail laying. If a group of ants is still unable to move the prey item for a certain time, specialized workers with large mandibles may be recruited in some species to cut the prey into smaller pieces. Although this scenario seems to be fairly well understood in the species where it has been studied, the mechanisms underlying cooperative transport—that is, when and how a group of ants move a large prey item to the nest—remain unclear. No formal description of the biological phenomenon has been developed, and, surprisingly, roboticists went further than biologists in trying to model cooperative transport: perhaps the only convincing model so far is one that has been introduced and studied by roboticists [207] and, although this model was not aimed at describing the behavior of real ants, few adjustments would be required to make it biologically plausible. This chapter first describes empirical research on cooperative transport in ants, and then describes the work of Kube and Zhang [205, 206,207, 209]. A small prey or food item is easily carried by a single ant.
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"validity of adopting the outcome suggested. In the court room, both parties put forward arguments and the judge chooses the argument that is either the most persuasive or that is the closest to the judge’s own belief concerning the outcome of the case. So far, in this text, there have been opportunities to read judgments and the judges have presented their decisions in the form of reasoned responses to the questions posed by the case. In the classroom, students are constantly called upon to practise and refine their skills in legal problem solving by engaging in reasoning processes leading to full scale argument construction. For the practising lawyer, a valid argument is of the utmost importance. Decisions as to right action can only be made by people who are able to distinguish between competing arguments and determine that, in a given set of circumstances, one argument is more valid than another. Judges are, of course, the ultimate arbiters of the acceptable decision. Sometimes, this decision is quite subjective. 7.7.1 Logic It is generally believed that academic and professional lawyers and, indeed, law students, are well skilled in the art of reasoning. Furthermore, it is believed that they are people who argue ‘logically’. To most, the term ‘logical’ indicates a person who can separate the relevant from the irrelevant, and come to an objective view, based often on supposedly objective formula. Colloquially, people accuse others, who change their mind or who are emotional in their arguing, of allowing their emotions to get the better of them, of ‘not being logical’. The dictionary defines logic as the science of reasoning, thinking, proof or inference. More than that, logic is defined as a science in its own right—a subsection of philosophy dealing with scientific method in argument and the uses of inference. Hegel called logic the fundamental science of thought and its categories. It certainly claims to be an accurate form of reasoning: its root is found in the Greek word logos meaning reason. Figure 7.7: a definition of logic." In Legal Method and Reasoning, 227. Routledge-Cavendish, 2012. http://dx.doi.org/10.4324/9781843145103-172.

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Conference papers on the topic "Maze Solving Robot"

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Covaci, Rares, Gabriel Harja, and Ioan Nascu. "Autonomous Maze Solving Robot *." In 2020 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR). IEEE, 2020. http://dx.doi.org/10.1109/aqtr49680.2020.9129943.

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Chang, Kuo-Chi, Yuwen Zhou, Akram Muhammad Shoaib, Kai-Chun Chu, Muhammad Izhar, Shafi Ullah, and Yuh-Chung Lin. "Shortest Distance Maze Solving Robot." In 2020 IEEE International Conference on Artificial Intelligence and Information Systems (ICAIIS). IEEE, 2020. http://dx.doi.org/10.1109/icaiis49377.2020.9194913.

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Kathe, Omkar, Varsha Turkar, Apoorv Jagtap, and Girish Gidaye. "Maze solving robot using image processing." In 2015 IEEE Bombay Section Symposium (IBSS). IEEE, 2015. http://dx.doi.org/10.1109/ibss.2015.7456635.

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Dang, Hongshe, Jinguo Song, and Qin Guo. "An Efficient Algorithm for Robot Maze-Solving." In 2010 2nd International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC). IEEE, 2010. http://dx.doi.org/10.1109/ihmsc.2010.119.

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Gupta, Bhawna, and Smriti Sehgal. "Survey on techniques used in Autonomous Maze Solving Robot." In 2014 5th International Conference- Confluence The Next Generation Information Technology Summit. IEEE, 2014. http://dx.doi.org/10.1109/confluence.2014.6949354.

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Campbell, Thomas, and James M. Hereford. "Scalability of robot swarms when applied to maze solving." In SoutheastCon 2015. IEEE, 2015. http://dx.doi.org/10.1109/secon.2015.7132906.

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Selvakumar, R., R. V. S. Abhiram, and K. Pranay Reddy. "Experimental analysis of maze solving robot using LSRB algorithm." In 2022 International Conference on Computer Communication and Informatics (ICCCI). IEEE, 2022. http://dx.doi.org/10.1109/iccci54379.2022.9740916.

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Lin, Shih-Wei, Yao-Lin Huang, and Wen-Kuei Hsieh. "Solving Maze Problem with Reinforcement Learning by a Mobile Robot." In 2019 IEEE International Conference on Computation, Communication and Engineering (ICCCE). IEEE, 2019. http://dx.doi.org/10.1109/iccce48422.2019.9010768.

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9

Tiwari, Alarsh, Tapas Badal, and Gaurav Singal. "Maze Solving with humanoid robot NAO using Real-Time object detection." In 2021 International Conference on Computer Communication and Informatics (ICCCI). IEEE, 2021. http://dx.doi.org/10.1109/iccci50826.2021.9402304.

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Chivarov, Nayden, Stanislav Yovkov, Stefan Chivarov, Iva Tosheva, Matus Pleva, and Daniel Hladek. "NITRO Educational Mobile Robot Platform for Maze Solving and Obstacle Avoidance." In 2021 19th International Conference on Emerging eLearning Technologies and Applications (ICETA). IEEE, 2021. http://dx.doi.org/10.1109/iceta54173.2021.9726663.

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