Real-time Optimal Control of Multi-wheeled Combat Vehicles - using Artificial Neural Network and 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.

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“…A modified artificial potential field algorithm is represented in [21] for obstacle avoidance to solve the local minimum problem. Artificial neural network and APF are combined in [22] to control path planning of an autonomous multi-wheeled combat vehicle in real time. Although APF can achieve good performance in the above literatures, it is designed from a human perspective.…”