This paper presents the development and implementation of a drone system designed for efficient path planning and collision avoidance. Utilizing Unreal Engine to create a simulation environment and Deep Q-Learning (DQN) for decision-making, we detail our approach to building a robust drone navigation system. The methodology includes developing a 3D environmental design, drone and environment configuration, algorithm development for collision avoidance, and optimization of DQN training parameters. Through a comprehensive evaluation, we were able to demonstrate promising improvements in the drone’s ability to navigate complex environments while effectively avoiding obstacles. Performance evaluation showed a steady increase in reward values during training. Cumulative rewards rose from 1.92 to 8.18, with a maximum possible reward of 10 per episode. This indicates successful navigation toward the goal coordinate as well as it reflects improvement over time as the drone learned from its environment. As training concluded and parameters were tuned, testing metrics validated the drone’s ability. The majority of testing episodes yielded the maximum reward of 10. This indicates that the drone successfully reached its goal. As a result, these findings provide a strong foundation for our future research and advancements in autonomous drone systems.
Advanced path planning and collision avoidance for quadcopter drones using deep Q-learning in 3D simulation
Sensors and Systems for Space Applications XVIII ; 2025 ; Orlando, Florida, United States
Proc. SPIE ; 13483
2025-05-21
Conference paper
Electronic Resource
English
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