Name
Title | ||
|---|---|---|
Convolutionally evaluated gradient first search path planning algorithm without prior global maps |
Abstract | ||
|---|---|---|
Most of the existing path planning algorithms require a prior global map. Although there have been some algorithms proposed for unknown environments, they can only deal with those which just have several hidden obstacles in a roughly known global map. In order to improve the efficiency of robot’s path planning without prior global maps, this paper proposes a Convolutionally Evaluated Gradient First Search (CE-GFS) path planning algorithm. It allows the robot to collect environmental information and complete path planning simultaneously. Firstly, the Gradient First Search (GFS) algorithm is proposed based on the gradient score parameter, with which the conventional cost function is replaced. The GFS can adapt to any moving direction through the environmental information surrounding the mobile robot and computing the gradient score parameter. Secondly, CE-GFS path planning algorithm is proposed based on GFS and convolutional evaluation method. The CE-GFS helps the robots to evaluate the efficiency of the global path and get global perception ability, so that they are prevented from going astray, which can significantly improve the efficiency of path planning. Finally, several simulation tests and field tests have been carried out. The test results show that convolutional evaluation improves the efficiency of CE-GFS by 86.94% on average compared with GFS at the price of 0.18% decrease in path planning success rate. Moreover, the time cost of the proposed CE-GFS algorithm is 42.18% less than that of FAR-Planner in some special cases. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.robot.2021.103985 | Robotics and Autonomous Systems |
Keywords | DocType | Volume |
Path planning,Mobile robot,Gradient descent,Unknown environments | Journal | 150 |
ISSN | Citations | PageRank |
0921-8890 | 0 | 0.34 |
References | Authors | |
0 | 6 | |