DOI : https://doi.org/10.18517/ijaseit.6.4.832

An Integrated Artificial Potential Field Path Planning with Kinematic Control for Nonholonomic Mobile Robot

Hendri Himawan Triharminto (1), Oyas Wahyunggoro (2), Teguh Bharata Adji (3), Adha Imam Cahyadi (4)
(1) Electrical Engineering and Information Technology Department, Grafika Street 2, Gadjah Mada University, Yogyakarta, Indonesia
(2) Electrical Engineering and Information Technology Department, Grafika Street 2, Gadjah Mada University, Yogyakarta, Indonesia
(3) Electrical Engineering and Information Technology Department, Grafika Street 2, Gadjah Mada University, Yogyakarta, Indonesia
(4) Electrical Engineering and Information Technology Department, Grafika Street 2, Gadjah Mada University, Yogyakarta, Indonesia
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How to cite (IJASEIT) :
Triharminto, Hendri Himawan, et al. “An Integrated Artificial Potential Field Path Planning With Kinematic Control for Nonholonomic Mobile Robot”. International Journal on Advanced Science, Engineering and Information Technology, vol. 6, no. 4, July 2016, pp. 410-8, doi:10.18517/ijaseit.6.4.832.
Citation Format :
In this paper, path planning which is based on Artificial Potential Field (APF) and the kinematic based control is integrated in order to solve an issue in the APF. Usually, the APF assumes the robot is modeled as a point mass. It means that the robot can move in any direction and neglect the nonholonomic constraint. In order to solve such a problem, the APF should be considered as part of the control system. This research proposed an approach integrating APF and control system under nonholonomic constraint. Naturally, the force of the APF can be used as linear velocity in the control system. Then, waypoint of APF is used as equilibrium point of kinematic control. In order to validate the proposed method, the experimental setup conducted on loop simulation. The scenario is that the robot moves along the certain trajectory to reach the goal point. The obstacle was set in between the robot and the goal point. The initial, goal, and the obstacles are set randomly.  The experiments show that the integration of the proposed method can be implemented successfully. The real obstacle avoidance method and fulfilling the nonholonomic constraint are the proof that the method is running well. The results show that the integrated proposed method meets convergent and stable.

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