Sample maze used in computer simulations 

Sample maze used in computer simulations 

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An algorithm that enables efficient maze exploration is presented in the paper. The algorithm involves two phases: first the whole maze is explored in an ordered way and then, the shortest possible way out is determined. The algorithm has been derived in a way that combines main advantages of the two known labirynth-exploration algorithms: “Wall fo...

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... Through proper mathematical modeling and interpretation of graph theory into a maze, it is possible to find the shortest path between any two nodes [2]. Till date, various maze-solving algorithms have been designed and implemented such as wall follower algorithm, pledge algorithm, flood-fill algorithm, deadend filling algorithm, Tremaux algorithm, and so on [3]. The selection of algorithms depends on various factors such as rules for maze solving, the complexity of mazes, time limitation, hardware constraints, etc. ...
Conference Paper
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This paper discusses the furtherance of the Tremaux algorithm by employing a potential value algorithm in conjunction, for improving search in a micromouse. The fused algorithm running on the STM32 Bluepill microcontroller explores and finds the shortest path in a 16x16 maze. Proximity sensors, gyroscope, and magnetometer together with encoder motors aid the micromouse to understand its surrounding hindrances and make precise movements while traversing through a maze. The optimized algorithm eliminates any paths that may lead the micromouse further away from the center of the maze during the initial run itself and saves a significant amount of time while solving a maze.
... Pledge Algorithm (PA) is an improved algorithm of WFA [27]. PA has two types as well, i.e., Left PA and Right PA. ...
... Therefore, TA requires robots to record their paths in memory [28]. The paths can be distinguished into three types [27]: ...
... This marking procedure makes unnecessary multiple visits to the same path eliminated. At the end of exploration, TA leaves a continuous once-passed mark on a path connecting the starting and the finish point [27]. ...
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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.
... Pledge Algorithm (PA) is an improved algorithm of WFA [27]. PA has two types as well, i.e., Left PA and Right PA. ...
... Therefore, TA requires robots to record their paths in memory [28]. The paths can be distinguished into three types [27]: ...
... This marking procedure makes unnecessary multiple visits to the same path eliminated. At the end of exploration, TA leaves a continuous once-passed mark on a path connecting the starting and the finish point [27]. ...
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Full-text available
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.
... This method had a drawback of getting stuck in endless loop. Pledge algorithm uses similar concept used in wall follower algorithm when the robot hits an obstacle, after reaching the end of the obstacle boundary the algorithm continued to follow the original direction (Bienias et al., 2016). By adapting this algorithm the robot may not know its exact location in wide open environment after starting from its initial location. ...
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Path planning for a human being is very easy to reach a desired location in a room, by avoiding obstacles on the way, by generating a mental map and uses this map to find the optimal path. This is a difficult task in case of a robot. In order to make the robot adapt, the system is fed with different obstacle arrangement in the same room and allow the robot to finalise the optimal path avoiding the obstacles. To achieve, the gradient map rendering algorithm is proposed with successful simulation results in MATLAB. The new map produced has given a gradient again using the same algorithm. After the rendering process is completed, the robot climbs up or down the gradient using the maximum or minimum local gradient technique respectively, to find its way to the destination cell. Gradient surface plots are obtained for a variety of mazes to give a visualisation of how the gradient is being formed. Results are obtained after maze simulation successfully shows the most optimised path in any kind of maze.
... Later, in 2016 Bienias [6] joined two labyrinth exploration algorithms; "Wall follower" and "Trémaux's algorithm." The algorithm involves two phases: first, the whole maze is explored in an ordered way, and then, the shortest possible way out is determined. ...
... On All Japan 2019's maze (presented in Figure 5, 6,7,8), the robot took one extra trip: the shortest path was found after returning to the starting position for the second time. The visited cell are represented by light-blue color. ...
Conference Paper
Micromouse is one of the most popular competitions among mobile robotics researchers. This competition brings together several challenges in the field of mobile robotics. It represents an excellent tool in competition field since it stimulates the development and multidisciplinary knowledge as well as group cooperation to carry out the best approach. This work presents a contribution to this issue on exploring the unknown environment and the robot location to obtain an optimized trajectory in the maze, using the modified Floodfill algorithm that considers the cost for the robot rotations around its axis. A comparison is conducted between the modified algorithm and the traditional Floodfill procedure.
... Paper [7] worked on developing maze solving algorithms that can be used for small and mobile devices. Their key contributions were a solution to arbitrary mazes and systematic maze exploration, among many others. ...
Chapter
The most vital element in making a non-playable car AI in racing games is finding the shortest path between two locations (the start and the finish lines) by making an efficient algorithm for the car to follow it. Usually, both the lines are predetermined for each race in most of the popular games. However, there has not been any game where the player gets the option to select those. Hence, in this work, an effort will be given to make sure that the player can choose the start line (source) and the finish line (destination) while flood-fill algorithm calculates the shortest path for the car at a shortest possible time and helps the car to follow it fast enough. The completed game has been tested, and from the user review, it seems to be quite difficult to defeat the opponent.
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