scholarly journals Modeling of working environment and coverage path planning method of combine harvesters

Author(s):  
En Lu ◽  
◽  
Lizhang Xu ◽  
Yaoming Li ◽  
Zhong Tang ◽  
...  
2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Ruiqing Mao ◽  
Xiliang Ma

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.


2019 ◽  
Vol 10 (1) ◽  
pp. 305
Author(s):  
Yong Tao ◽  
Chaoyong Chen ◽  
Tianmiao Wang ◽  
Youdong Chen ◽  
Hegen Xiong ◽  
...  

A re-entry path planning method in omitting areas for service robots is suggested based on dynamic Inver-Over evolutionary algorithms after the robot automatically avoids obstacles. The complete coverage path planning is researched for cleaning service robots. Combined with features of dynamic travelling salesmen problem (DTSP), a local operator is employed for the path planning to enhance real-time dynamic properties of the Inver-Over algorithm. The method addresses the path planning problem that a number of cells undergo dynamic changes over time under work environment of cleaning robots. With simulations and experiments performed, it is discovered that the average relative error is 2.2% between the re-entry path planning and the best path, which validates the effectiveness and feasibility of the method.


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