scholarly journals Path planning for coal mine robot to avoid obstacle in gas distribution area

2018 ◽  
Vol 15 (1) ◽  
pp. 172988141775150 ◽  
Author(s):  
Xiliang Ma ◽  
Ruiqing Mao
Keyword(s):  
Path Planning ◽  
Coal Mine ◽  
Ant Colony Algorithm ◽  
Dynamic Environment ◽  
Planning Method ◽  
Distribution Area ◽  
Dijkstra Algorithm ◽  
Safety Level ◽  
Planning Methods ◽  
Local Path

As the explosion-proof safety level of coal mine robot has not yet reached the level of intrinsic safety “ia,” therefore, path planning methods for coal mine robot to avoid the dangerous area of gas are necessary. To avoid a secondary explosion when the coal mine robot passes through gas hazard zones, a path planning method is proposed, considering the gas concentration distributions. The path planning method is composed of two steps in total: the global path planning and the local path adjustment. First, the global working path for coal mine robot is planed based on the Dijkstra algorithm and the ant colony algorithm. Second, with consideration of the dynamic environment, when hazardous gas areas distribute over the planed working path again, local path adjustments are carried out with the help of a proposed local path adjustment method. Lastly, experiments are conducted in a roadway after accident, which verify the effectiveness of the proposed path planning method.

scholarly journals Research on Path Planning Method of Coal Mine Robot to Avoid Obstacle in Gas Distribution Area

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Ruiqing Mao ◽  
Xiliang Ma
Keyword(s):  
Path Planning ◽  
Coal Mine ◽  
Ant Colony Algorithm ◽  
Planning Method ◽  
Working Environment ◽  
Distribution Area ◽  
Dijkstra Algorithm ◽  
Gas Distribution ◽  
Safety Level ◽  
Planning Methods

As the explosion-proof safety level of a coal mine robot has not yet reached the level of intrinsic safety “ia” and it cannot work in a dangerous gas distribution area, therefore, path planning methods for coal mine robot to avoid the dangerous area of gas are necessary. In this paper, to avoid a secondary explosion when the coal mine robot passes through gas hazard zones, a path planning method is proposed with consideration of gas concentration distributions. First, with consideration of gas distribution area and obstacles, MAKLINK method is adopted to describe the working environment network diagram of the coal mine robot. Second, the initial working paths for the coal mine robot are obtained based on Dijkstra algorithm, and then the global optimal working path for the coal mine robot is obtained based on ant colony algorithm. Lastly, experiments are conducted in a roadway after an accident, and results by different path planning methods are compared, which verified the effectiveness of the proposed path planning method.

A novel method of unmanned surface vehicle autonomous cruise

2016 ◽  
Vol 43 (1) ◽  
pp. 121-130 ◽  
Author(s):  
Shaorong Xie ◽  
Peng Wu ◽  
Hengli Liu ◽  
Peng Yan ◽  
Xiaomao Li ◽  
...  
Keyword(s):  
Path Planning ◽  
Obstacle Detection ◽  
Planning Method ◽  
Dijkstra Algorithm ◽  
Complex Environment ◽  
Unmanned Surface Vehicle ◽  
Content Type ◽  
Global Path Planning ◽  
Local Path Planning ◽  
Local Path

Purpose – This paper aims to propose a new method for combining global path planning with local path planning, to provide an efficient solution for unmanned surface vehicle (USV) path planning despite the changeable environment. Path planning is the key issue of USV navigation. A lot of research works were done on the global and local path planning. However, little attention was given to combining global path planning with local path planning. Design/methodology/approach – A search of shortcut Dijkstra algorithm was used to control the USV in the global path planning. When the USV encounters unknown obstacles, it switches to our modified artificial potential field (APF) algorithm for local path planning. The combinatorial method improves the approach of USV path planning in complex environment. Findings – The method in this paper offers a solution to the issue of path planning in changeable or unchangeable environment, and was confirmed by simulations and experiments. The USV follows the global path based on the search of shortcut Dijkstra algorithm. Both USV achieves obstacle avoidances in the local region based on the modified APF algorithm after obstacle detection. Both the simulation and experimental results demonstrate that the combinatorial path planning method is more efficient in the complex environment. Originality/value – This paper proposes a new path planning method for USV in changeable environment. The proposed method is capable of efficient navigation in changeable and unchangeable environment.

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.

A Review of Path Planning Method for Mobile Robot

2014 ◽  
Vol 1030-1032 ◽  
pp. 1588-1591 ◽  
Author(s):  
Zong Sheng Wu ◽  
Wei Ping Fu
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Optimal Path ◽  
Grid Method ◽  
Field Method ◽  
Planning Method ◽  
Artificial Potential Field ◽  
Planning Methods ◽  
Basic Ideas ◽  
Artificial Potential Field Method

The ability of a mobile robot to plan its path is the key task in the field of robotics, which is to find a shortest, collision free, optimal path in the various scenes. In this paper, different existing path planning methods are presented, and classified as: geometric construction method, artificial intelligent path planning method, grid method, and artificial potential field method. This paper briefly introduces the basic ideas of the four methods and compares them. Some challenging topics are presented based on the reviewed papers.

A path planning method using adaptive polymorphic ant colony algorithm for smart wheelchairs

2018 ◽  
Vol 25 ◽  
pp. 50-57 ◽  
Author(s):  
Zhuqing Jiao ◽  
Kai Ma ◽  
Yiling Rong ◽  
Peng Wang ◽  
Hongkai Zhang ◽  
...  
Keyword(s):  
Path Planning ◽  
Ant Colony Algorithm ◽  
Planning Method ◽  
Ant Colony ◽  
Smart Wheelchairs

A method for avoiding obstacles of a self-driving carin the dynamic environment based on model predictive control

2021 ◽  
Author(s):  
N.P. Demenkov ◽  
Kai Zou
Keyword(s):  
Path Planning ◽  
Model Predictive Control ◽  
Urban Environment ◽  
Predictive Control ◽  
Potential Field ◽  
Dynamic Environment ◽  
Field Method ◽  
Artificial Potential Field ◽  
Physical Constraints ◽  
Local Path

The paper discusses the problem of obstacle avoidance of a self-driving car in urban road conditions. The artificial potential field method is used to simulate traffic lanes and cars in a road environment. The characteristics of the urban environment, as well as the features and disadvantages of existing methods based on the structure of planning-tracking, are analyzed. A method of local path planning is developed, based on the idea of an artificial potential field and model predictive control in order to unify the process of path planning and tracking to effectively cope with the dynamic urban environment. The potential field functions are introduced into the path planning task as constraints. Based on model predictive control, a path planning controller is developed, combined with the physical constraints of the vehicle, to avoid obstacles and execute the expected commands from the top level as the target for the task. A joint simulation was performed using MATLAB and CarSim programs to test the feasibility of the proposed path planning method. The results show the effectiveness of the proposed method.

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