An Adaptive Conversion Speed Q-Learning Algorithm for Search and Rescue UAV Path Planning in Unknown Environments

Wu, Jiehong; Sun, Yanan; Li, Danyang; Shi, Junling; Li, Xianwei; Gao, Lijun; Yu, Li; Han, Guangjie; Wu, Jinsong

doi:10.1109/tvt.2023.3297837

Cited by 10 publications

(1 citation statement)

References 32 publications

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“…SARSA and Q-learning methods can enhance the obstacle avoidance ability of UAVs and optimize the calculation of the shortest path. Both methods are model-free algorithms that aim to eliminate reliance on the environmental model and make action selections based on the values associated with all available actions [13,14] . Despite possessing distinctive characteristics and benefits, classical RL methods demonstrate significant limitations when confronted with high-dimensional motion environments and multi-action inputs, particularly as the complexity of the mission environment for UAVs increases.…”

Section: Introductionmentioning

confidence: 99%

Learning based multi-obstacle avoidance of unmanned aerial vehicles with a novel reward

Gao,

Kong,

et al. 2023

Complex Engineering Systems

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In this paper, a novel reward-based learning method is proposed for unmanned aerial vehicles to achieve multi-obstacle avoidance. The Markov jump model was first formulated for the unmanned aerial vehicle obstacle avoidance problem. A distinctive reward shaping function is proposed to adaptively avoid obstacles and finally reach the target position via an optimal approach such that an adaptive Q-learning algorithm called the improved prioritized experience replay is developed. Simulation results show that the proposed algorithm can achieve autonomous obstacle avoidance in complex environments with improved performance.

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Section: Introductionmentioning

confidence: 99%