A Master-Slave Approach for Simultaneously Controlling Two Drones when Carrying an Object

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Ardehali Seyyed Mohammad Ali; Faraji Amin; Abdoos Monireh; Salimi-Badr Armin

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Journal Paper

Paper Information

Journal: Journal of Artificial Intelligence, Applications, and Innovations Year:2024 | Volume:2 | Issue:1 Page(s): 76-82

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Information Journal Paper

Title

A Master-Slave Approach for Simultaneously Controlling Two Drones when Carrying an Object

Author(s)

Ardehali Seyyed Mohammad Ali | Faraji Amin | Abdoos Monireh | Salimi-Badr Armin | Issue Writer Certificate

Keywords

Index Terms— Reinforcement Learning

Double Deep Q-Learning

master-slave approach

Abstract

Abstract - - Abstract - - Abstract - This paper proposes a master-slave approach to simultaneously control two drones with the aim of carrying an object toward a goal. The proposed method utilizes the Double Deep Q-Learning (DDQN) technique to train a master agent to be able to carry an object toward a goal with the help of a slave agent. This procedure is implemented such that the master agent gathers the observations and specifies the actions to be made by itself and the slave agent. Indeed, the slave agent just applies a predefined action and does not process any input for producing the output. This manner of learning, leads to a unified convergence to an optimal solution compared to the situation in which each agent is trained separately. To verify the functionality of the proposed method, the algorithm is examined in the webots simulation environment. The simulations show that the introduced method has a good performance when controlling the drones to reach to the goal. The introduced method, other than algorithmic benefits which leads to a faster convergence of the model, suggests some reduction in the processing demand. The reason is that the learning procedure is guided by one of the agents and consequently only one of the agents is responsible for doing the calculations that lead to choosing the action. In this scenario, the slave agent does not require any processing resources for choosing the action and just simply applies a predefined action dictated by the master agent.

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