Real-time Optimal Control of Multi-wheeled Combat Vehicles – using Artificial Neural Network and Potential Fields

Authors

  • Amr Mohamed University of Ontario Institute of Technology, Oshawa, Canada
  • Moustafa EI-Gindy University of Ontario Institute of Technology, Oshawa, Canada
  • Xishi Huang Istuary Innovation Group, Markham, Canada
  • Jing Ren University of Ontario Institute of Technology, Oshawa, Canada
  • Haoxiang Lang University of Ontario Institute of Technology, Oshawa, Canada

DOI:

https://doi.org/10.3849/aimt.01237

Keywords:

real‐time, optimal control theory, path planning, autonomous multi‐wheeled combat vehicle, Artificial Neural Network, artificial potential fields

Abstract

This paper presents a real‐time path planning algorithm for autonomous multi‐wheeled combat vehicles using Artificial Neural Network (ANN), Artificial Potential Fields (APFs) and optimal control theory. Real‐time navigation of autonomous vehicles is a very complex problem and it is crucial for many military operations. This paper proposes an optimal control and ANN approach for a dynamic model of the multi‐wheeled combat vehicle to generate the possible optimal paths that cover every part of the workspace. Consequently, the obtained paths are used to train the proposed ANN model. The trained ANN has the capability to control the movement of combat vehicle in real time from any starting point to the desired goal position within the area of interest. The vehicle path is autonomously generated from the previous vehicle location parameter in terms of lateral velocity, heading angle and yaw rate of the vehicle. APF is proposed for preventing the vehicle from colliding with obstacles in border destination. The effectiveness and efficiency of the proposed approach are demonstrated in the simulation results, which show that the proposed ANN model is capable of navigating the multi‐wheeled combat vehicle in real time.

Author Biography

Amr Mohamed, University of Ontario Institute of Technology, Oshawa, Canada

Amr Mohamed, is a PhD graduate student in the Electrical Engineering Program at the Faculty of Engineering and Applied Science of the University of Ontario Institute Of Technology. Amr obtained her bachelor degree from Military Technical College, Egypt. He received his Master’s degree in control systems from the Military Technical College, Egypt. His master thesis versed on design robust controller for guided Missiles.

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Published

03-06-2018

How to Cite

Mohamed, A., EI-Gindy, M., Huang, X., Ren, J., & Lang, H. (2018). Real-time Optimal Control of Multi-wheeled Combat Vehicles – using Artificial Neural Network and Potential Fields. Advances in Military Technology, 13(2), 193–207. https://doi.org/10.3849/aimt.01237

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