Unmanned surface vehicles(USVs) are important autonomous marine robots that have been studied and gradually applied into practice. However, the autonomous navigation of USVs, especially the issue of obstacle avoidance...Unmanned surface vehicles(USVs) are important autonomous marine robots that have been studied and gradually applied into practice. However, the autonomous navigation of USVs, especially the issue of obstacle avoidance in complicated marine environment, is still a fundamental problem. After studying the characteristics of the complicated marine environment, we propose a novel adaptive obstacle avoidance algorithm for USVs,based on the Sarsa on-policy reinforcement learning algorithm.The proposed algorithm is composed of local avoidance module and adaptive learning module, which are organized by the “divide and conquer” strategy-based architecture. The course angle compensation strategy is proposed to offset the disturbances from sea wind and currents. In the design of payoff value function of the learning strategy, the course deviation angle and its tendency are introduced into action rewards and penalty policies. The validity of the proposed algorithm is verified by comparative experiments of simulations and sea trials in three sea-state marine environments. The results show that the algorithm can enhance the autonomous navigation capacity of USVs in complicated marine environments.展开更多
In the last decade,IoT has been widely used in smart cities,autonomous driving and Industry 4.0,which lead to improve efficiency,reliability,security and economic benefits.However,with the rapid development of new tec...In the last decade,IoT has been widely used in smart cities,autonomous driving and Industry 4.0,which lead to improve efficiency,reliability,security and economic benefits.However,with the rapid development of new technologies,such as cognitive communication,cloud computing,quantum computing and big data,the IoT security is being confronted with a series of new threats and challenges.IoT device identification via Radio Frequency Fingerprinting(RFF)extracting from radio signals is a physical-layer method for IoT security.In physical-layer,RFF is a unique characteristic of IoT device themselves,which can difficultly be tampered.Just as people’s unique fingerprinting,different IoT devices exhibit different RFF which can be used for identification and authentication.In this paper,the structure of IoT device identification is proposed,the key technologies such as signal detection,RFF extraction,and classification model is discussed.Especially,based on the random forest and Dempster-Shafer evidence algorithm,a novel ensemble learning algorithm is proposed.Through theoretical modeling and experimental verification,the reliability and differentiability of RFF are extracted and verified,the classification result is shown under the real IoT device environments.展开更多
文摘Unmanned surface vehicles(USVs) are important autonomous marine robots that have been studied and gradually applied into practice. However, the autonomous navigation of USVs, especially the issue of obstacle avoidance in complicated marine environment, is still a fundamental problem. After studying the characteristics of the complicated marine environment, we propose a novel adaptive obstacle avoidance algorithm for USVs,based on the Sarsa on-policy reinforcement learning algorithm.The proposed algorithm is composed of local avoidance module and adaptive learning module, which are organized by the “divide and conquer” strategy-based architecture. The course angle compensation strategy is proposed to offset the disturbances from sea wind and currents. In the design of payoff value function of the learning strategy, the course deviation angle and its tendency are introduced into action rewards and penalty policies. The validity of the proposed algorithm is verified by comparative experiments of simulations and sea trials in three sea-state marine environments. The results show that the algorithm can enhance the autonomous navigation capacity of USVs in complicated marine environments.
文摘In the last decade,IoT has been widely used in smart cities,autonomous driving and Industry 4.0,which lead to improve efficiency,reliability,security and economic benefits.However,with the rapid development of new technologies,such as cognitive communication,cloud computing,quantum computing and big data,the IoT security is being confronted with a series of new threats and challenges.IoT device identification via Radio Frequency Fingerprinting(RFF)extracting from radio signals is a physical-layer method for IoT security.In physical-layer,RFF is a unique characteristic of IoT device themselves,which can difficultly be tampered.Just as people’s unique fingerprinting,different IoT devices exhibit different RFF which can be used for identification and authentication.In this paper,the structure of IoT device identification is proposed,the key technologies such as signal detection,RFF extraction,and classification model is discussed.Especially,based on the random forest and Dempster-Shafer evidence algorithm,a novel ensemble learning algorithm is proposed.Through theoretical modeling and experimental verification,the reliability and differentiability of RFF are extracted and verified,the classification result is shown under the real IoT device environments.
