Artificial Potential Field Algorithm Implementation for Quadrotor Path Planning

Potential field algorithm introduced by Khatib is well-known in path planning for robots. The algorithm is very simple yet provides real-time path planning and effective to avoid robot’s collision with obstacles. The purpose of the paper is to implement and modify this algorithm for quadrotor path p...

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Bibliographic Details
Published in:International journal of advanced computer science & applications Vol. 10; no. 8
Main Authors: Iswanto, Iswanto, Ma’arif, Alfian, Wahyunggoro, Oyas, Imam, Adha
Format: Journal Article
Language:English
Published: West Yorkshire Science and Information (SAI) Organization Limited 2019
Subjects:
Algorithms
Barriers
Collision avoidance
Path planning
Potential fields
Robots
ISSN:2158-107X, 2156-5570
Online Access:Get full text
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Summary:Potential field algorithm introduced by Khatib is well-known in path planning for robots. The algorithm is very simple yet provides real-time path planning and effective to avoid robot’s collision with obstacles. The purpose of the paper is to implement and modify this algorithm for quadrotor path planning. The conventional potential method is firstly applied to introduce challenging problems, such as not reachable goals due to local minima solutions or nearby obstacles (GNRON). This will be solved later by proposed modified algorithms. The first proposed modification is by adding virtual force to the repulsive potential force to prevent local minima solutions. Meanwhile, the second one is to prevent GNRON issue by adding virtual force and considering quadrotor’s distance to goal point on the repulsive potential force. The simulation result shows that the second modification is best applied to environment with GNRON issue whereas the first one is suitable only for environment with local minima traps. The first modification is able to reach goals in six random tests with local minima environment. Meanwhile, the second one is able to reach goals in six random tests with local minima environment, six random tests with GNRON environment, and six random tests with both local minima and GNRON environment.
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ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2019.0100876