Remotely operated robotic systems are vital for maintaining the ITER plant—the world largest fusion device. To operate the ITER Blanket Remote Handling System (BRHS), its control system must have functionalities that are not familiar in conventional robot control systems; e.g., completely remote operation, multi-device control with reconfiguration during operations, and large-scale operation programming. We designed the control system to solve those problems: split the control system into the High-Level Control System, which has HMI functions, and Low-Level Control System, which has interfaces with robots, and enabled the remote operation from a different building; implemented abstract commands from the High-Level to the Low-Level Control system so that the robot devices can be reconfigured during an operation; developed hierarchical operation program that manages 2-year operations. We also designed specific functions of both levels of the control system and GUI of main components of the High-Level Control System.

This paper proposes a method of formulating the robot kinematics. The proposed method can be applied to mobile manipulators, parallel link robots, and legged robots by the same procedure. The proposed forward kinematics formulation is based on tree-structured rigid-body system and is compared with the Denavit–Hartenberg method and URDF. The proposed differential kinematics formulation is represented by a block lower triangular matrix, which is a generalized form of the basic Jacobian matrix. In addition, an application example of the proposed formulation to the numerical inverse kinematics method is also shown. The usage of the proposed method is demonstrated by simulation experiments using a dual-arm mobile manipulator and a parallel link robot.

This paper presents a vision-based control to pick up an object by a robotic hand. In-hand manipulation, i.e., changing the position and orientation of the grasped object without dropping it, is a challenging task. It is important and necessary for tasks like achieving a stable grasp by translating or rotating the grasped object even though only a part of the object is grasped. However, it is difficult for soft objects like food, because the grasping force needs to be just enough to hold and manipulate the object without crushing it. To tackle this problem, we propose a system which integrates the control of the hand and object detection via a camera. The target robotic hand is configured by two parallel grippers with conveyor belts for manipulation, and is equipped only with a stereo camera as a sensor. From the camera image frames, the three-dimensional position, orientation, and size of the object are calculated. While controlling the grippers and belts, the slippage between belts and the object is estimated based on the difference between their displacements. The validity of the proposed system is verified through the experiments wherein the hand is controlled to pick up objects of varying size and softness. By experimentally evaluating whether the objects have slipped, been crushed, or been manipulated to the target position, it was confirmed that all objects could be picked up with the appropriate force without dropping or crushing them.

In this paper, we propose a highly accurate specific person tracking system for home service robots. This study focuses on Selected Online Ada-Boosting (SOAB) and Kernelized Correlation Filters (KCF). It proposes the Selected KCF (SKCF), which combines features of fast adaptation to a specific person and robustness for occlusion. We also propose a system that combines You Only Look Once (YOLO), a real-time object detection algorithm, with SKCF to initialize and recover the tracking system. The proposed system runs at 17 [fps] in an experimental environment with an Intel Core i7-8700K CPU and an NVIDIA GTX 1080 GPU. We also show that the tracking accuracy of the proposed system on public datasets for tracking specific persons is better than that of conventional methods.

In recent years, a jig-less robotic assembly system is desired as a lean production system in the manufacturing industry. In jig-less assembly, it is assumed that parts are supplied on a parts tray. However, if the parts are placed in a corner of the tray or along the partitions of the tray, it is difficult to grasp the parts with a versatile robotic hand due to the interference between the fingers of the hand and the tray. In this paper, we address this issue for a cylindrical part, which is frequently handled in actual assembly tasks, and propose a method to plan a robust grasping strategy of the part against the uncertainty in its position, utilizing pushing operation with a versatile robotic hand with parallel stick fingers.

The autonomous lawn mower “HerbF” to improve the golf course management efficiently was developed successfully. The “HerbF” project was initiated in 2018 with the aim of autonomously driving existing lawn mowers already owned by golf courses and transferring the skills of experienced keepers to the autonomous lawn mower. Our proposed autonomous driving system can be easily retrofit to existing lawn mowers. The experimental results in the golf course showed the practical feasibility of the autonomous lawn mower with the retrofittable autonomous driving control system.