Path planning of decentralized multi-quadrotor based on fuzzy-cell decomposition algorithm

2017 ◽  
Author(s):  
Iswanto ◽  
Oyas Wahyunggoro ◽  
Adha Imam Cahyadi
Keyword(s):  
Path Planning ◽  
Cell Decomposition ◽  
Decomposition Algorithm ◽  
Fuzzy Cell

scholarly journals RCD: Radial Cell Decomposition Algorithm For Mobile Robot Path Planning

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Omnia A. A. Salama ◽  
Mohamed E. H. Eltaib ◽  
Hany A. Mohamed ◽  
Omar Salah
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Cell Decomposition ◽  
Decomposition Algorithm ◽  
Robot Path Planning ◽  
Radial Cell ◽  
Robot Path

scholarly journals Expanded Douglas–Peucker Polygonal Approximation and Opposite Angle-Based Exact Cell Decomposition for Path Planning with Curvilinear Obstacles

2019 ◽  
Vol 9 (4) ◽  
pp. 638 ◽  
Author(s):  
Jin-Woo Jung ◽  
Byung-Chul So ◽  
Jin-Gu Kang ◽  
Dong-Woo Lim ◽  
Yunsik Son
Keyword(s):  
Approximation Algorithm ◽  
Path Planning ◽  
Cell Decomposition ◽  
Decomposition Algorithm ◽  
Planning Problem ◽  
Polygonal Approximation ◽  
Robot Path Planning ◽  
Path Planning Problem ◽  
Robot Path ◽  
Vertical Cell

The Expanded Douglas–Peucker (EDP) polygonal approximation algorithm and its application method for the Opposite Angle-Based Exact Cell Decomposition (OAECD) are proposed for the mobile robot path-planning problem with curvilinear obstacles. The performance of the proposed algorithm is compared with the existing Douglas–Peucker (DP) polygonal approximation and vertical cell decomposition algorithm. The experimental results show that the path generated by the OAECD algorithm with EDP approximation appears much more natural and efficient than the path generated by the vertical cell decomposition algorithm with DP approximation.

scholarly journals Path Planning Methods in an Environment with Obstacles (A Review)

2018 ◽  
pp. 15-58 ◽  
Author(s):  
W. Liu
Keyword(s):  
Path Planning ◽  
Incomplete Information ◽  
Cell Decomposition ◽  
Reference Points ◽  
Mobile Robot Navigation ◽  
Problem Solution ◽  
Local Path Planning ◽  
Starting Point ◽  
Planning Methods ◽  
Local Path

Planning the path is the most important task in the mobile robot navigation. This task involves basically three aspects. First, the planned path must run from a given starting point to a given endpoint. Secondly, it should ensure robot’s collision-free movement. Thirdly, among all the possible paths that meet the first two requirements it must be, in a certain sense, optimal.Methods of path planning can be classified according to different characteristics. In the context of using intelligent technologies, they can be divided into traditional methods and heuristic ones. By the nature of the environment, it is possible to divide planning methods into planning methods in a static environment and in a dynamic one (it should be noted, however, that a static environment is rare). Methods can also be divided according to the completeness of information about the environment, namely methods with complete information (in this case the issue is a global path planning) and methods with incomplete information (usually, this refers to the situational awareness in the immediate vicinity of the robot, in this case it is a local path planning). Note that incomplete information about the environment can be a consequence of the changing environment, i.e. in a dynamic environment, there is, usually, a local path planning.Literature offers a great deal of methods for path planning where various heuristic techniques are used, which, as a rule, result from the denotative meaning of the problem being solved. This review discusses the main approaches to the problem solution. Here we can distinguish five classes of basic methods: graph-based methods, methods based on cell decomposition, use of potential fields, optimization methods, фтв methods based on intelligent technologies.Many methods of path planning, as a result, give a chain of reference points (waypoints) connecting the beginning and end of the path. This should be seen as an intermediate result. The problem to route the reference points along the constructed chain arises. It is called the task of smoothing the path, and the review addresses this problem as well.

Pursuit Algorithm for Robot Trash Can Based on Fuzzy-Cell Decomposition

2016 ◽  
Vol 6 (6) ◽  
pp. 2863
Author(s):  
Tatiya Padang Tunggal ◽  
Andi Supriyanto ◽  
Nur Mukhammad Zaidatur Rochman ◽  
Ibnu Faishal ◽  
Imam Pambudi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Shortest Path ◽  
Cell Decomposition ◽  
Robot Path Planning ◽  
Shortest Distance ◽  
Artificial Intelligence Algorithm ◽  
Time Required ◽  
Fuzzy Cell ◽  
Destination Point ◽  
Robot Path

<p>Scooby Smart Trash can is a trash can equipped with artificial intelligence algorithms that is able to capture and clean up garbages thrown by people who do not care about the environment. The can is called smart because it acts like scoobydoo in a children's cartoon in that the can will react if there is garbage thrown and it catches and cleans them up. This paper presents pursuit algorithm that uses cell decomposition algorithm in which algorithms are used to create a map of the robot's path and fuzzy algorithm as one of the artificial intelligence algorithm for robot path planning. By using the combined algorithms, the robot is able to pursuit and chases the trash carelessly discarded, but it has not been able to find the shortest distance. Therefore, this paper considers a second modification of the algorithm by adding a potential field algorithm used to add weight values on the map, so that the robot can pursue trash by finding the shortest path. The proposed algorithm shows that the robot can avoid obstacles and find the shortest path so that the time required to get to the destination point is fast.</p>

Sign in / Sign up

Export Citation Format

Share Document