In this paper, a modified path planning method based on the APF (artificial potential field) is proposed for the mobile robot navigation system. The path planning method is very important to the robot navigation system. The APF is a commonly used path planning method because of its advantages of simple processing and online realization in the unknown environment. However, the T-APF (traditional-APF) has shortcomings such as local minima and path inefficiency problems. To overcome these drawbacks, the NP-APF (new point-APF) is proposed. NP-APF is a specialized method for using the LiDAR. The key idea of the NP-APF is that it creates the new point of the attractive force what can improve performance of the APF by solving its drawbacks. The new point is created when the obstacles block a path which makes straight-line from the mobile robot to the goal, and it helps the mobile robot overcome local minima and path inefficiency problems. The new point is created at the most reasonable place where no obstacle is detected by the LiDAR. The simulation results show the improvement of performance compared with the T-APF.
|Title of host publication||2017 2nd International Conference on Robotics and Automation Engineering, ICRAE 2017|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|Publication status||Published - 2018 Feb 13|
|Event||2nd International Conference on Robotics and Automation Engineering, ICRAE 2017 - Shanghai, China|
Duration: 2017 Dec 29 → 2017 Dec 31
|Name||2017 2nd International Conference on Robotics and Automation Engineering, ICRAE 2017|
|Other||2nd International Conference on Robotics and Automation Engineering, ICRAE 2017|
|Period||17/12/29 → 17/12/31|
Bibliographical noteFunding Information:
ACKNOWLEDGMENT This project was partially supported by Microsoft Research and was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) (No. NRF-2015R1A2A2A01007545).
© 2017 IEEE.
All Science Journal Classification (ASJC) codes
- Control and Optimization
- Mechanical Engineering
- Control and Systems Engineering