Automated Foreign Object Debris Detection System based on UAV

M'Sila, Chadi; Ayad, Redouane; Ait-Oufroukh, N.

doi:10.1109/icnsc55942.2022.10004050

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…Path determination in robotics involves finding a safe and efficient path for a robot to follow in order to complete a task. This can be a challenging problem, as robots need to navigate through unknown environments while avoiding obstacles and minimizing the risk of collisions [21][22][23][24][25][26][27][28].…”

Section: Related Workmentioning

confidence: 99%

Autonomous Navigation of Robots: Optimization with DQN

et al. 2023

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In the field of artificial intelligence, control systems for mobile robots have undergone significant advancements, particularly within the realm of autonomous learning. However, previous studies have primarily focused on predefined paths, neglecting real-time obstacle avoidance and trajectory reconfiguration. This research introduces a novel algorithm that integrates reinforcement learning with the Deep Q-Network (DQN) to empower an agent with the ability to execute actions, gather information from a simulated environment in Gazebo, and maximize rewards. Through a series of carefully designed experiments, the algorithm’s parameters were meticulously configured, and its performance was rigorously validated. Unlike conventional navigation systems, our approach embraces the exploration of the environment, facilitating effective trajectory planning based on acquired knowledge. By leveraging randomized training conditions within a simulated environment, the DQN network exhibits superior capabilities in computing complex functions compared to traditional methods. This breakthrough underscores the potential of our algorithm to significantly enhance the autonomous learning capacities of mobile robots.

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