A Comparative Study Between Hopfield Neural Network and A* Path Planning Algorithms for Mobile Robot

Kodgule, Suhit Atul; Das, Arka; Doss, Arockia Selvakumar Arockia

doi:10.1007/978-981-10-3174-8_4

Cited by 2 publications

(1 citation statement)

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“…Real-time path planning for autonomous using ANN has many distinct advantages. ANNs have previously been applied in autonomous driving [10][11][12]. For example, [13] used Bioinspired neural network to perform Real-time path planning for autonomous underwater vehicle in a three-dimensional (3D) unknown environment.…”

Section: Introductionmentioning

confidence: 99%

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

Mohamed

EI-Gindy

Huang

et al. 2018

AiMT

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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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