scholarly journals Using coverage path planning methods for car park exploration

ACTA IMEKO ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 15
Author(s):  
Anna Barbara Ádám ◽  
László Kocsány ◽  
Emese Gincsainé Szádeczky-Kardoss
Keyword(s):  
Path Planning ◽  
Cell Decomposition ◽  
Coverage Path Planning ◽  
The Core ◽  
Core Concepts ◽  
Planning Methods ◽  
Car Park ◽  
Number Of Visits ◽  
The Cost ◽  
Step Number

With the increasing number of vehicles on the roads, finding a free parking space has become a time-consuming problem. Traditional car parks are not equipped with occupancy sensors, so planning a systematic traversal of a car park can ease and shorten the search. Since car park exploration is similar to coverage path planning (CPP) problems, the core concepts of CPP algorithms can be used. This paper presents a method that divides maps into smaller cells using trapezoidal cell decomposition and then plans the traversal using wavefront algorithm core concepts. This method can be used for multi-storey car parks by planning the traversal of each floor separately and then the path from one floor to the next. Several alternative explorational paths can be generated by taking different personal preferences into account, such as the length of the driven route and the proximity to preferred locations. The planned traversals are compared by step number, the cell visitedness ratio, the number of visits to each cell and the cost function. The comparison of the methods is based on simulation results.

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.

Scan matching online cell decomposition for coverage path planning in an unknown environment

2013 ◽  
Vol 14 (9) ◽  
pp. 1551-1558 ◽  
Author(s):  
Batsaikhan Dugarjav ◽  
Soon-Geul Lee ◽  
Donghan Kim ◽  
Jong Hyeong Kim ◽  
Nak Young Chong
Keyword(s):  
Path Planning ◽  
Cell Decomposition ◽  
Scan Matching ◽  
Coverage Path Planning ◽  
Unknown Environment

scholarly journals Efficient robotic path planning algorithm based on artificial potential field

2021 ◽  
Vol 11 (6) ◽  
pp. 4840
Author(s):  
Elia Nadira Sabudin ◽  
Rosli Omar ◽  
Sanjoy Kumar Debnath ◽  
Muhammad Suhaimi Sulong
Keyword(s):  
Computational Complexity ◽  
Path Planning ◽  
Potential Field ◽  
Path Length ◽  
Computation Time ◽  
Cell Decomposition ◽  
Target Point ◽  
Planning Methods ◽  
Planning Algorithm ◽  
Path Planning Algorithm

<span lang="EN-US">Path planning is crucial for a robot to be able to reach a target point safely to accomplish a given mission. In path planning, three essential criteria have to be considered namely path length, computational complexity and completeness. Among established path planning methods are voronoi diagram (VD), cell decomposition (CD), probability roadmap (PRM), visibility graph (VG) and potential field (PF). The above-mentioned methods could not fulfill all three criteria simultaneously which limits their application in optimal and real-time path planning. This paper proposes a path PF-based planning algorithm called dynamic artificial PF (DAPF). The proposed algorithm is capable of eliminating the local minima that frequently occurs in the conventional PF while fulfilling the criterion of path planning. DAPF also integrates path pruning to shorten the planned path. In order to evaluate its performance, DAPF has been simulated and compared with VG in terms of path length and computational complexity. It is found that DAPF is consistent in generating paths with low computation time in obstacle-rich environments compared to VG. The paths produced also are nearly optimal with respect to VG.</span>

scholarly journals The cost of inaction

2017 ◽  
Author(s):  
Chris Lacinak
Keyword(s):  
Organization Size ◽  
New Model ◽  
Audiovisual Media ◽  
Media Companies ◽  
Government Institutions ◽  
The Core ◽  
Core Concepts ◽  
Future Value ◽  
The Subject ◽  
The Cost

Anon University is a hypothetical university representing a conglomeration of organizations with holdings of legacy physical audiovisual media. It exemplies a universal conundrum that poses a serious threat to the future value derived from content stored on physical audiovisual legacy media. This paper proposes a new model and application for quantifying the nancial and intellectual implications of decisions regarding digitization of physical audiovisual media holdings. Cost of inaction (COI) calculates the return on savings of previous investment in collections while recognizing that the window of possible return is limited because audiovisual media degrade or become obsolete. While the subject of this example is a university, the issue is not speci c to academic institutions. It manifests in organizations of all types and sizes, including government institutions, corporations, non-pro ts, museums, media companies and more. Some of the arguments and positioning may di er based on organization size and type, but the core concepts and calculations are the same.

Research on Coverage Path Planning of Mobile Robot Based on Genetic Algorithm

2013 ◽  
Vol 819 ◽  
pp. 379-383 ◽  
Author(s):  
San Peng Deng ◽  
Zhong Min Wang ◽  
Peng Zhou ◽  
Hong Bing Wu
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Mobile Robot ◽  
Cell Decomposition ◽  
Global Motion ◽  
Complete Coverage ◽  
Regional Environment ◽  
Coverage Path Planning ◽  
Environment Model ◽  
Local Space

This paper presents a complete coverage path planning method, which combines local space coverage with global motion planning. It is realized by modeling mobile robot environment based on Boustrophedon cell decomposition method; and according to the characteristics of regional environment model, the connectivity of the traversing space is represented by a complete weighted connected matrix. Then Genetic algorithm (GA) is used to optimize the subspace traversal distance to obtain the shortest global traversal sequence of mobile robot.

The Core Concepts, Skills, and Components Curriculum: Increasing Trauma Expertise in Practitioners

2008 ◽  
Author(s):  
Christopher Layne ◽  
Abigail Gewirtz ◽  
Chandra Ghosh Ippen ◽  
Renee Dominguez ◽  
Robert Abramovitz ◽  
...  
Keyword(s):  
The Core ◽  
Core Concepts

A Novel Static Path Planning Method for Mobile Anchor-Assisted Localization in Wireless Sensor Networks

2020 ◽  
Vol 10 ◽  
Author(s):  
Abdelhady M. Naguib ◽  
Shahzad Ali
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Path Planning ◽  
Sensor Node ◽  
Large Scale ◽  
Performance Metrics ◽  
Wireless Sensor ◽  
Anchor Node ◽  
Mobile Anchor ◽  
Planning Methods

Background: Many applications of Wireless Sensor Networks (WSNs) require awareness of sensor node’s location but not every sensor node can be equipped with a GPS receiver for localization, due to cost and energy constraints especially for large-scale networks. For localization, many algorithms have been proposed to enable a sensor node to be able to determine its location by utilizing a small number of special nodes called anchors that are equipped with GPS receivers. In recent years a promising method that significantly reduces the cost is to replace the set of statically deployed GPS anchors with one mobile anchor node equipped with a GPS unit that moves to cover the entire network. Objectives: This paper proposes a novel static path planning mechanism that enables a single anchor node to follow a predefined static path while periodically broadcasting its current location coordinates to the nearby sensors. This new path type is called SQUARE_SPIRAL and it is specifically designed to reduce the collinearity during localization. Results: Simulation results show that the performance of SQUARE_SPIRAL mechanism is better than other static path planning methods with respect to multiple performance metrics. Conclusion: This work includes an extensive comparative study of the existing static path planning methods then presents a comparison of the proposed mechanism with existing solutions by doing extensive simulations in NS-2.

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