This paper tackles the intricate challenge of autonomous navigation for Unmanned Aerial Vehicles (UAVs) through dynamically changing environments. We focus on a sophisticated Deep Reinforcement Learning (DRL) approach using the Soft Actor-Critic (SAC) algorithm, optimized for UAV path planning within a continuous action space. This methodology leverages environmental image data to enhance the precision of flight maneuvers and effective obstacle avoidance. Our approach, validated through extensive simulations in Gazebo and field tests, demonstrates the algorithm’s efficacy in enabling UAVs to adeptly navigate obstacles using depth maps. The study further explores the robustness of the SAC algorithm by comparing it with traditional DRL methods, emphasizing its superior performance in real-world applications. This research contributes significantly to advancing UAV technology, particularly in autonomous motion planning, by integrating cutting-edge machine learning techniques. The video link is: https://www.youtube.com/watch?v=Nd_aMzejNXY.

Extracting the unknown parameters of proton exchange membrane fuel cell (PEMFC) models accurately is vital to design, control, and simulate the actual PEMFC. In order to extract the unknown parameters of PEMFC models precisely, this work presents an improved version of neural network algorithm (NNA), namely the multiple learning neural network algorithm (MLNNA). In MLNNA, six learning strategies are designed based on the created local elite archive and global elite archive to balance exploration and exploitation of MLNNA. To evaluate the performance of MLNNA, MLNNA is first employed to solve the well-known CEC 2015 test suite. Experimental results demonstrate that MLNNA outperforms NNA on most test functions. Then, MLNNA is used to extract the parameters of two PEMFC models including the BCS 500 W PEMFC model and the NedStack SP6 PEMFC model. Experimental results support the superiority of MLNNA in the parameter estimation of PEMFC models by comparing it with 10 powerful optimization algorithms.