Recently, the IP connectivity during the Mobile Node (MN) movement between Base Stations (BSs) belonging to different Internet Service Providers (ISPs) is still a key issue to be tackled. In this paper, therefore, we ...Recently, the IP connectivity during the Mobile Node (MN) movement between Base Stations (BSs) belonging to different Internet Service Providers (ISPs) is still a key issue to be tackled. In this paper, therefore, we develop a new scheme to improve the performance of inter-domain fast handover over mobile WiMAX networks. The framework basically relies on the Fast Handover for Mobile IPv6 protocol (FMIPv6) when the Media Independent Information Services (MIIS) as defined in IEEE802.21 standard is applied to enable the Mobile Node in storing the information of the neighboring networks. A Fully Qualified Domain Name (FQDN) is also used to identify the IP address of the previous network operator and the MN during its movements. Since both MIIS and FQDN can support the node mobility between multiple domains, our proposed scheme can also be called P-FMIPv6. The numerical results show that the latency of IP connectivity of this proposed handover can be significantly reduced in addition to less service disruption time during handovers as compared to the existing FMIPv6 when IEEE802.16e network is considered.展开更多
提出了一种采用逻辑接口的基于TPMIPv6(transient binding for proxy mobile IPv6)的域间切换管理方案.逻辑接口从2个物理接口抽象出来,上层需知道这个逻辑接口;2个物理接口对上层透明,在切换时分别与2个MAG(mo-bile access gateway)相...提出了一种采用逻辑接口的基于TPMIPv6(transient binding for proxy mobile IPv6)的域间切换管理方案.逻辑接口从2个物理接口抽象出来,上层需知道这个逻辑接口;2个物理接口对上层透明,在切换时分别与2个MAG(mo-bile access gateway)相连;后一接口在进行预注册时,前一接口仍保持原来的通信.流移动管理由LMA(local mobileanchor)控制,以避免切换过程中物理接口的中断.分析结果表明,与已有的域间切换方案相比,建议方案的切换时延更小.展开更多
Background Robot grasping encompasses a wide range of research areas;however, most studies have been focused on the grasping of only stationary objects in a scene;only a few studies on how to grasp objects from a user...Background Robot grasping encompasses a wide range of research areas;however, most studies have been focused on the grasping of only stationary objects in a scene;only a few studies on how to grasp objects from a user's hand have been conducted. In this paper, a robot grasping algorithm based on deep reinforcement learning (RGRL) is proposed. Methods The RGRL takes the relative positions of the robot and the object in a user's hand as input and outputs the best action of the robot in the current state. Thus, the proposed algorithm realizes the functions of autonomous path planning and grasping objects safely from the hands of users. A new method for improving the safety of human-robot cooperation is explored. To solve the problems of a low utilization rate and slow convergence of reinforcement learning algorithms, the RGRL is first trained in a simulation scene, and then, the model para-meters are applied to a real scene. To reduce the difference between the simulated and real scenes, domain randomization is applied to randomly change the positions and angles of objects in the simulated scenes at regular intervals, thereby improving the diversity of the training samples and robustness of the algorithm. Results The RGRL's effectiveness and accuracy are verified by evaluating it on both simulated and real scenes, and the results show that the RGRL can achieve an accuracy of more than 80% in both cases. Conclusions RGRL is a robot grasping algorithm that employs domain randomization and deep reinforcement learning for effective grasping in simulated and real scenes. However, it lacks flexibility in adapting to different grasping poses, prompting future research in achieving safe grasping for diverse user postures.展开更多
文摘Recently, the IP connectivity during the Mobile Node (MN) movement between Base Stations (BSs) belonging to different Internet Service Providers (ISPs) is still a key issue to be tackled. In this paper, therefore, we develop a new scheme to improve the performance of inter-domain fast handover over mobile WiMAX networks. The framework basically relies on the Fast Handover for Mobile IPv6 protocol (FMIPv6) when the Media Independent Information Services (MIIS) as defined in IEEE802.21 standard is applied to enable the Mobile Node in storing the information of the neighboring networks. A Fully Qualified Domain Name (FQDN) is also used to identify the IP address of the previous network operator and the MN during its movements. Since both MIIS and FQDN can support the node mobility between multiple domains, our proposed scheme can also be called P-FMIPv6. The numerical results show that the latency of IP connectivity of this proposed handover can be significantly reduced in addition to less service disruption time during handovers as compared to the existing FMIPv6 when IEEE802.16e network is considered.
文摘提出了一种采用逻辑接口的基于TPMIPv6(transient binding for proxy mobile IPv6)的域间切换管理方案.逻辑接口从2个物理接口抽象出来,上层需知道这个逻辑接口;2个物理接口对上层透明,在切换时分别与2个MAG(mo-bile access gateway)相连;后一接口在进行预注册时,前一接口仍保持原来的通信.流移动管理由LMA(local mobileanchor)控制,以避免切换过程中物理接口的中断.分析结果表明,与已有的域间切换方案相比,建议方案的切换时延更小.
文摘Background Robot grasping encompasses a wide range of research areas;however, most studies have been focused on the grasping of only stationary objects in a scene;only a few studies on how to grasp objects from a user's hand have been conducted. In this paper, a robot grasping algorithm based on deep reinforcement learning (RGRL) is proposed. Methods The RGRL takes the relative positions of the robot and the object in a user's hand as input and outputs the best action of the robot in the current state. Thus, the proposed algorithm realizes the functions of autonomous path planning and grasping objects safely from the hands of users. A new method for improving the safety of human-robot cooperation is explored. To solve the problems of a low utilization rate and slow convergence of reinforcement learning algorithms, the RGRL is first trained in a simulation scene, and then, the model para-meters are applied to a real scene. To reduce the difference between the simulated and real scenes, domain randomization is applied to randomly change the positions and angles of objects in the simulated scenes at regular intervals, thereby improving the diversity of the training samples and robustness of the algorithm. Results The RGRL's effectiveness and accuracy are verified by evaluating it on both simulated and real scenes, and the results show that the RGRL can achieve an accuracy of more than 80% in both cases. Conclusions RGRL is a robot grasping algorithm that employs domain randomization and deep reinforcement learning for effective grasping in simulated and real scenes. However, it lacks flexibility in adapting to different grasping poses, prompting future research in achieving safe grasping for diverse user postures.
