本发明涉及路径规划技术领域,涉及一种融合堆叠LSTM与SAC算法的路径规划方法及系统,方法包括:一,收集常规场景图像及深度场景图像,并设计卷积神经网络提取图像特征;二,收集目标点,障碍物的位置信息,计算移动机器人与目标点,障碍物的距离;三,构建堆叠LSTM网络,将提取到的卷积特征,目标点的位置,上一时刻移动机器人的线...
SAC-LSTM This is a MindSpore implementation of SAC-LSTM, a recurrent model for radar echo extrapolation (precipitation nowcasting). Our implementation code in Pytorch is available at https://github.com/LeiShe1/SAC-LSTM. Setup Required python libraries: MindSpore==1.8.0 cann==5.1.2 python==3.7...
Pyramidal Recurrent Units (PRUs): A New LSTM Unit sacmehta.github.io/PRU/ Topics natural-language-processing deep-learning language-modeling recurrent-neural-networks lstm-neural-networks Resources Readme License MIT license Activity Stars 10 stars Watchers 2 watching Forks 3 forks Report...
In this framework, the SAC-LSTM algorithm combines historical and current states to select an action and controls the mobile robot to execute the action in the environment. After interacting with the environment, the mobile robot receives a reward value and a state value. During the training ...
In this framework, the SAC-LSTM algorithm combines historical and current states to select an action and controls the mobile robot to execute the action in the environment. After interacting with the environment, the mobile robot receives a reward value and a state value. During the training ...
In addition, in order to solve the problem of non-stability and uncertainty of the input state in the dynamic environment, which leads to the inability to fully express the state information, we propose an attention network fused with Long Short-Term Memory (LSTM) to improve the SAC algorithm...
In addition, in order to solve the problem of non-stability and uncertainty of the input state in the dynamic environment, which leads to the inability to fully express the state information, we propose an attention network fused with Long Short-Term Memory (LSTM) to improve the SAC algorithm...
In addition, in order to solve the problem of non-stability and uncertainty of the input state in the dynamic environment, which leads to the inability to fully express the state information, we propose an attention network fused with Long Short-Term Memory (LSTM) to improve the SAC algorithm...
In addition, in order to solve the problem of non-stability and uncertainty of the input state in the dynamic environment, which leads to the inability to fully express the state information, we propose an attention network fused with Long Short-Term Memory (LSTM) to improve the SAC algorithm...
The motion planning task of the manipulator in a dynamic environment is relatively complex. This paper uses the improved Soft Actor Critic Algorithm (SAC) with the maximum entropy advantage as the benchmark algorithm to implement the motion planning of t