Inverse reinforcement learning-based time-dependent A* planner for human-aware robot navigation with local vision

In an environment where robots coexist with humans, mobile robots should be human-aware and comply with humans' behavioural norms so as to not disturb humans' personal space and activities. In this work, we propose an inverse reinforcement learning-based time-dependent A* planner for human-aware robot navigation with local vision. In this method, the planning process of time-dependent A* is regarded as a Markov decision process and the cost function of the time-dependent A* is learned using the inverse reinforcement learning via capturing humans' demonstration trajectories. With this method, a robot can plan a path that complies with humans' behaviour patterns and the robot's kinematics. When constructing feature vectors of the cost function, considering the local vision characteristics, we propose a visual coverage feature for enabling robots to learn from how humans move in a limited visual field. The effectiveness of the proposed method has been validated by experiments in real-world scenarios: using this approach robots can effectively mimic human motion patterns when avoiding pedestrians; furthermore, in a limited visual field, robots can learn to choose a path that enables them to have the larger visual coverage which shows a better navigation performance.     

User authentication in a tactile internet based remote surgery environment: Security issues,challenges, and future research directions

Recently, the Tactile Internet (TI) becomes a new era of the Internet. The TI provides ultra-reliable, ultra-responsive and intelligent network connectivity for delivering real-time control and physical haptic experiences from a remote location. The TI provides a different feeling to human–machine interaction by implementing the real-time interactive systems. In this review article, we discuss a generalized authentication model which can be used to perform authentication procedure among different communicating parties in order to secure remote surgery in the TI environment. By using the proposed authentication model, an authentication protocol can be designed so that an authenticated surgeon can use the robot/robotic arms to perform the surgery securely as well as remotely. Since the application is very critical, the important instructions provided by the surgeon to the robot/robotic arms must not be leaked in between the communication during the surgical procedure. To deal with this emerging research area, a secure mutual user authentication mechanism should be provided between a remote surgeon and the robot/robotic arms so that they can communicate securely using the established session key among them. Further, several security issues and challenges for such kind of communication are also discussed in this article. Finally, we discuss few points that need to be considered as future research works that are related to authentication for securing remote surgery in the TI environment.     

基于改进蚁群算法的三维路径规划

针对移动机器人在海水环境中的三维路径规划问题容易陷入局部最优和收敛慢等瑕疵，根据三维环境全局信息来改进蚁群算法以提高实时性和收敛速度。改进蚁群算法的启发函数，采用局部信息和全局信息结合动态地改进信息素更新方式，以及根据三维空间中路径的平坦程度和光滑度二阶微分分别增加了一阶微分和二阶微分来再次修改信息素更新规则。仿真对比实验结果显示改进后的蚁群算法克服了收敛速度慢、容易陷入局部最优的缺点。     

Hierarchical sampling optimization of particle filter for global robot localization in pervasive network environment

This paper presents a hierarchical framework for managing the sampling distribution of a particle filter (PF) that estimates the global positions of mobile robots in a large‐scale area. The key concept is to gradually improve the accuracy of the global localization by fusing sensor information with different characteristics. The sensor observations are the received signal strength indications (RSSIs) of Wi‐Fi devices as network facilities and the range of a laser scanner. First, the RSSI data used for determining certain global areas within which the robot is located are represented as RSSI bins. In addition, the results of the RSSI bins contain the uncertainty of localization, which is utilized for calculating the optimal sampling size of the PF to cover the regions of the RSSI bins. The range data are then used to estimate the precise position of the robot in the regions of the RSSI bins using the core process of the PF. The experimental results demonstrate superior performance compared with other approaches in terms of the success rate of the global localization and the amount of computation for managing the optimal sampling size.     

考虑关节角加速度约束的仿人机器人偏摆力矩控制方法

针对仿人机器人步行过程中存在的机器人关节角加速度约束影响控制性能的问题，提出一种考虑关节角加速度约束的仿人机器人偏摆力矩控制方法.该方法充分考虑了双臂在摆动过程中对偏摆力矩的影响，根据力矩平衡条件得到需要抵消的偏摆力矩的大小与方向，将偏摆力矩的控制问题转化为带约束条件的二次规划问题，并设计了一种在线变步长迭代算法计算得到优化后的双臂摆动轨迹.实验表明，该方法能有效抵消机器人步行中产生的偏摆力矩，避免控制过程中的"削峰"现象，有效提高机器人的步行稳定性.     

基于虚拟导航线的农业机器人精确视觉导航方法

针对农田、野外环境中无人工标记情况下的导航问题，提出了一种基于虚拟导航线的农业机器人精确视觉导航方法。该方法不需要铺设导航线或者路标即可引导机器人行走直线。首先，根据需求确定需要跟踪的目标区域，之后控制机器人调整方向直到目标移至视野中央；其次，根据机器人和目标的位置确定参照目标，并依据两个目标的位置确定虚拟导航线；然后，动态更新导航线，并结合虚拟定标线和虚拟导航线确定偏移角度和偏移距离；最后，利用偏移参数构建模糊控制表，并以此实现对机器人旋转角度和行走速度的调整。实验结果表明，该算法能较为精确地实现对导航路线的识别，进而利用模糊控制策略使机器人沿直线向目标行走，且导航精度在10 cm以内。     

A novel robot co-worker system for paint factories without the need of existing robotic infrastructure

This paper presents a human–robot co-working system to be applied to industrial tasks such as the production line of a paint factory. The aim is to optimize the picking task with respect to manual operation in a paint factory. The use of an agile autonomous robot co-worker reduces the time in the picking process of materials, and the reduction of the exposure time to raw materials of the worker improves the human safety. Moreover, the process supervision is also improved thanks to a better traceability of the whole process. The whole system consists of a manufacturing process management system, an autonomous navigation system, and a people detection and tracking system. The localization module does not require the installation of reflectors or visual markers for robot operation, significantly simplifying the system deployment in a factory. The robot is able to respond to changing environmental conditions such as people, moving forklifts or unmapped static obstacles like pallets or boxes. The system is not tied to specific manufacturing orders. It is fully integrated with the manufacturing process management system and it can process all possible orders as long as their components are placed into the warehouse. Real experiments to validate the system have been performed in a paint factory by a real holonomic platform and a worker. The results are promising from the evaluation of performance indicators such as exposure time of the worker to raw materials, automation of the process, robust and safe navigation, and the assessment of the end-user.     

Effect of practice on similar and dissimilar skills in patient transfer through training with a robot patient

With the number of hospital stays increasing, nurses require more training to handle a variety of patients. However, time for training in nursing schools is limited, and students lack the opportunity to practice on a diverse variety of patients. Using a robot to simulate actual patients, this study observes the learning transfer effect of practice on practice-similar and practice-dissimilar skills from one patient to another, and investigates which types of practice suit which kinds of training. An experiment was conducted by administering a pre-test, practice, a post-test, and a transfer test to two groups (N?=?8), each with different practice-related skills. The evaluation used a checklist covering required skills that were either similar or dissimilar across groups, depending on their practice. The effect of practice can be observed through a comparison of skills similar to one group but dissimilar to the other. The results show that practice facilitates learning transfer on similar skills but not, or to a lesser degree, on dissimilar skills. Furthermore, if skills needed to handle given symptoms are unfamiliar or inaccessible to students, practice related to those symptoms should be emphasized through simulated training with robots.