Impact of Resampling and Deep Learning to Detect Anomaly in Imbalance Time-Series Data

Saripuddin, Mulyana; Suliman, Azizah; Sameon, Sera Syarmilla

doi:10.1109/iccrd54409.2022.9730424

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…Over time, the robot can use the feedback it receives to learn the optimal path to take in different environments. This allows the robot to adapt to changes in its environment and make decisions in real time based on the current situation [38][39][40][41].…”

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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Section: Related Workmentioning

confidence: 99%