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Investigating Reduced Path Planning Strategy for Differential Wheeled Mobile Robot

Published online by Cambridge University Press:  14 May 2019

Raouf Fareh,
Mohammed Baziyad,
Mohammad H. Rahman,
Tamer Rabie and
Maamar Bettayeb
Raouf Fareh*
Affiliation:
Electrical and Computer Engineering Department, University of Sharjah, UAE E-mails: trabie@sharjah.ac.ae, maamar@sharjah.ac.ae
Mohammed Baziyad
Affiliation:
Research Institute of Sciences and Engineering (RISE), University of Sharjah, UAE E-mail: mbaziyad@sharjah.ac.ae
Mohammad H. Rahman
Affiliation:
Mechanical/Biomedical Engineering Department, University of Wisconsin-Milwaukee, Milwaukee, USA E-mail: rahmanmh@uwm.edu
Tamer Rabie
Affiliation:
Research Institute of Sciences and Engineering (RISE), University of Sharjah, UAE E-mail: mbaziyad@sharjah.ac.ae
Maamar Bettayeb
Affiliation:
Research Institute of Sciences and Engineering (RISE), University of Sharjah, UAE E-mail: mbaziyad@sharjah.ac.aeMCEIES, King Abdulaziz University, Jeddah, KSA
*
*Corresponding author. E-mail: rfareh@sharjah.ac.ae
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Summary

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This paper presents a vision-based path planning strategy that aims to reduce the computational time required by a robot to find a feasible path from a starting point to the goal point. The proposed algorithm presents a novel strategy that can be implemented on any well-known path planning algorithm such as A*, D* and probabilistic roadmap (PRM), to improve the swiftness of these algorithms. This path planning algorithm is suitable for real-time scenarios since it reduces the computational time compared to the basis and traditional algorithms. To test the proposed path planning strategy, a tracking control strategy is implemented on a mobile platform. This control strategy consists of three major stages. The first stage deals with gathering information about the surrounding environment using vision techniques. In the second stage, a free-obstacle path is generated using the proposed reduced scheme. In the final stage, a Lyapunov kinematic tracking controller and two Artificial Neural Network (ANN) based-controllers are implemented to track the proposed path by adjusting the rotational and linear velocity of the robot. The proposed path planning strategy is tested on a Pioneer P3-DX differential wheeled mobile robot and an Xtion PRO depth camera. Experimental results prove the efficiency of the proposed path planning scheme, which was able to reduce the computational time by a large percentage which reached up to 88% of the time needed by the basis and traditional scheme, without significant adverse effect on the workability of the basis algorithm. Moreover, the proposed path planning algorithm has improved the path efficiency, in terms of the path length and trackability, challenging the traditional trade-off between swiftness and path efficiency.

Keywords

Path planningImage processingDepth sensorMobile robotKinematic controlProbabilistic roadmapObstacle avoidanceStabilityTrajectory tracking

Information

Type
Articles
Information
Robotica , Volume 38 , Issue 2 , February 2020, pp. 235 - 255
Copyright
© Cambridge University Press 2019 

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