In human-robot collaboration, there has been a trade-off relationship between the speed of collaborative robots and the safety of human workers. In our previous paper, we introduced a time-optimal path tracking algorithm designed to maximize speed while ensuring safety for human workers [1]. This algorithm runs in real-time and provides the safe and fastest control input for every cycle with respect to ISO standards [2]. However, true optimality has not been achieved due to inaccurate distance computation resulting from conservative model simplification. To attain true optimality, we require a method that can compute distances 1. at many robot configurations to examine along a trajectory 2. in realtime for online robot control 3. as precisely as possible for optimal control. In this paper, we propose a batched, fast and precise distance checking method based on precomputed link-local SDFs. Our method can check distances for 500 waypoints along a trajectory within less than 1 millisecond using a GPU at runtime, making it suited for time-critical robotic control. Additionally, a neural approximation has been proposed to accelerate preprocessing by a factor of 2. Finally, we experimentally demonstrate that our method can navigate a 6-DoF robot earlier than a geometric-primitives-based distance checker in a dynamic and collaborative environment.

One way of ensuring operator's safety during human-robot collaboration is through Speed and Separation Monitoring (SSM), as defined in ISO standard ISO/TS 15066. In general, it is impossible to avoid all human-robot collisions: consider for instance the case when the robot does not move at all, a human operator can still collide with it by hitting it of her own voluntary motion. In the SSM framework, it is possible however to minimize harm by requiring this: if a collision ever occurs, then the robot must be in a stationary state (all links have zero velocity) at the time instant of the collision. In this paper, we propose a time-optimal control policy based on Time-Optimal Path Parameterization (TOPP) to guarantee such a behavior. Specifically, we show that: for any robot motion that is strictly faster than the motion recommended by our policy, there exists a human motion that results in a collision with the robot in a non-stationary state. Correlatively, we show, in simulation, that our policy is strictly less conservative than state-of-the-art safe robot control methods. Additionally, we propose a parallelization method to reduce the computation time of our pre-computation phase (down to about 0.5 sec, practically), which enables the whole pipeline (including the pre-computation) to be executed at runtime, nearly in real-time. Finally, we demonstrate the application of our method in a scenario: time-optimal, safe control of a 6-dof industrial robot.

In order to safely and efficiently collaborate with humans, industrial robots need the ability to alter their motions quickly to react to sudden changes in the environment, such as an obstacle appearing across a planned trajectory. In Realtime Motion Planning, obstacles are detected in real time through a vision system, and new trajectories are planned with respect to the current positions of the obstacles, and immediately executed on the robot. Existing realtime motion planners, however, lack the smoothing post-processing step - which are crucial in sampling-based motion planning - resulting in the planned trajectories being jerky, and therefore inefficient and less human-friendly. Here we propose a Realtime Trajectory Smoother based on the shortcutting technique to address this issue. Leveraging fast clearance inference by a novel neural network, the proposed method is able to consistently smooth the trajectories of a 6-DOF industrial robot arm within 200 ms on a commercial GPU. We integrate the proposed smoother into a full Vision-Motion Planning-Execution loop and demonstrate a realtime, smooth, performance of an industrial robot subject to dynamic obstacles.

In this paper, we discuss the design and implementation of a novel telecommunication system, the avatar-mediated communication (AMC) system, for constructing interpersonal relationships. In the AMC system, human users develop conversation experiences through software agents without directly interacting with their conversation partners. We develop an experimental platform for human users to control software agents to interact with their conversation partners and examine how their actions of controlling the software agents and consequences led by the software agents contribute to the development of their conversation experiences and interpersonal relationships. Experimental results demonstrate that actions are not important; however, consequences are important. The result sets a basis for the design and implementation of future AMC systems.

In the physical wireless parameter conversion sensor network (PhyC-SN), the sensor controls the frequency of carrier according to the sensing result and all the nodes inform the sensing results to the fusion center, simultaneously. The dynamic range of sensing result which node sends to FC depends on the available channels of frequency spectrum. In spectrum sharing to the other system (primary system), PhyC-SN (secondary system) considers the assignment between the quantization level of digital sensing result and the channel of frequency spectrum. In this paper, for constructing this assignment, the fusion center collects two types of tendencies. First one is the sensing results. The mean and variance value of sensing results can be estimated by data tracking technique. Second one is a channel occupancy rate (COR). From COR, the fusion center predicts the probability of finding non-occupied channel. The fusion center can construct the assignment map based on minimum mean square error between the predicted sensing result and the exact one. As a result, the constructed assignment achieves the high spectrum usage efficiency.