A gait control method for a biped robot based on the deep Q-network (DQN) algorithm is proposed to enhance the stability of walking on uneven ground. This control strategy is an intelligent learning method of posture ...A gait control method for a biped robot based on the deep Q-network (DQN) algorithm is proposed to enhance the stability of walking on uneven ground. This control strategy is an intelligent learning method of posture adjustment. A robot is taken as an agent and trained to walk steadily on an uneven surface with obstacles, using a simple reward function based on forward progress. The reward-punishment (RP) mechanism of the DQN algorithm is established after obtaining the offline gait which was generated in advance foot trajectory planning. Instead of implementing a complex dynamic model, the proposed method enables the biped robot to learn to adjust its posture on the uneven ground and ensures walking stability. The performance and effectiveness of the proposed algorithm was validated in the V-REP simulation environment. The results demonstrate that the biped robot's lateral tile angle is less than 3° after implementing the proposed method and the walking stability is obviously improved.展开更多
针对常规地锁开关不方便等问题,文章研究深度学习和物联网的应用,设计基于图像深度学习的物联网智能地锁系统,以此提升地锁系统的实用性。该系统由摄像头、网络通信、电磁锁控制等模块组成。通过地锁终端采集车牌图像数据,利用5G网络将...针对常规地锁开关不方便等问题,文章研究深度学习和物联网的应用,设计基于图像深度学习的物联网智能地锁系统,以此提升地锁系统的实用性。该系统由摄像头、网络通信、电磁锁控制等模块组成。通过地锁终端采集车牌图像数据,利用5G网络将图像数据传输给服务器。由服务器利用基于yolov4-tiny的车牌检测算法进行车牌检测,利用基于轻量级卷积神经网络(License Plate Recognition via Deep Neural Networks,LPRNet)的车牌识别算法进行车牌识别,并根据识别结果控制地锁开关。展开更多
基金Supported by the National Ministries and Research Funds(3020020221111)
文摘A gait control method for a biped robot based on the deep Q-network (DQN) algorithm is proposed to enhance the stability of walking on uneven ground. This control strategy is an intelligent learning method of posture adjustment. A robot is taken as an agent and trained to walk steadily on an uneven surface with obstacles, using a simple reward function based on forward progress. The reward-punishment (RP) mechanism of the DQN algorithm is established after obtaining the offline gait which was generated in advance foot trajectory planning. Instead of implementing a complex dynamic model, the proposed method enables the biped robot to learn to adjust its posture on the uneven ground and ensures walking stability. The performance and effectiveness of the proposed algorithm was validated in the V-REP simulation environment. The results demonstrate that the biped robot's lateral tile angle is less than 3° after implementing the proposed method and the walking stability is obviously improved.
文摘针对常规地锁开关不方便等问题,文章研究深度学习和物联网的应用,设计基于图像深度学习的物联网智能地锁系统,以此提升地锁系统的实用性。该系统由摄像头、网络通信、电磁锁控制等模块组成。通过地锁终端采集车牌图像数据,利用5G网络将图像数据传输给服务器。由服务器利用基于yolov4-tiny的车牌检测算法进行车牌检测,利用基于轻量级卷积神经网络(License Plate Recognition via Deep Neural Networks,LPRNet)的车牌识别算法进行车牌识别,并根据识别结果控制地锁开关。
