In a cable-driven parallel robot (CDPR), force sensors are utilized at each winch motor to measure the cable tension in order to obtain the force distribution at the robot end-effector. However, because of the effects of friction in the pulleys and the unmodeled cable properties of the robot, the measured cable tensions are often inaccurate, which causes force-control difficulties. To overcome this issue, this paper presents an artificial neural network (ANN)-based indirect end-effector force-estimation method, and its application to CDPR force control. The pulley friction and other unmodeled effects are considered as black-box uncertainties, and the tension at the end-effector is estimated by compensating for these uncertainties using an ANN that is developed using the training datasets from CDPR experiments. The estimated cable tensions at the end-effector are used to design a P-controller to track the desired force. The performance of the proposed ANN model is verified through comparisons with the forces measured directly at the end-effector. Furthermore, cable force control is implemented based on the compensated tensions to evaluate the performance of the CDPR in wrench space. The experimental results show that the proposed friction-compensation method is suitable for application in CDPRs to control the cable force.

In this paper, we present one approach to achieve safe navigation in indoor dynamic environment. So far, there have been various useful collision avoidance algorithms and path planning schemes. However, those algorithms have a fundamental limitation that the robot can avoid only "visible" obstacles. In real environment, it is not possible to detect all the dynamic obstacles around the robot. There exist a lot of "invisible" regions due to the limitation of field of view. In order to avoid possible collisions, it is desirable to consider visibility information. Then a robot can reduce the speed or modify a path. This paper proposes a safe navigation scheme to reduce the risk of collision due to unexpected dynamic obstacles. The robot's motion is controlled according to a hybrid control scheme. The possibility of collision is dually reflected to a path planning and a speed control. The proposed scheme clearly indicates the structural procedure how to model and to exploit the risk of navigation. The proposed scheme is experimentally tested in a real office building. The presented result shows that the robot moves along the safe path to obtain sufficient field of view, while appropriate speed control is carried out.

In this paper, a Collision Grid Map (CGM) is proposed by using 3d point cloud data to predict the collision between the cattle and the end effector of the manipulator in the barn environment. The Generated Collision Grid Map using x-y plane and depth z data in 3D point cloud data is applied to a Convolutional Neural Network to predict a collision situation. There is an invariant of the permutation problem, which is not efficiently learned in occurring matter of different orders when 3d point cloud data is applied to Convolutional Neural Network. The Collision Grid Map is generated by point cloud data based on the probability method. The Collision Grid Map scheme is composed of a 2-channel. The first channel is constructed by location data in the x-y plane. The second channel is composed of depth data in the z-direction. 3D point cloud is measured in a barn environment and created a Collision Grid Map. Then the generated Collision Grid Map is applied to the Convolutional Neural Network to predict the collision with cattle. The experimental results show that the proposed scheme is reliable and robust in a barn environment.

Mobile robots often follow humans in warehouse environments or indoor office spaces. A mobile robot requires a control system that stably follows humans without colliding with them. A human-following robot is composed of target detection, target tracking, and control. Conventional 2D laser-based human following systems exploit information from 2D planar laser data to detect human legs through machine learning methods, such as support vector data description and random forest. However, in crowded or cluttered environments, 2D LiDAR data is limited by the lack of features that distinguish human legs from obstacles. Due to the lack of features, false-positive detection is a problem in crowded or cluttered environments. Recent studies using 3D LiDAR have used sensors mounted at an elevated height to measure the overall shape of a person to extract features. Typical mobile robots are mounted in the bottom due to the vibration of the sensors, so there is a cost problem that additional sensors are required. We propose a framework for human following using 3D LiDAR. Our method is able to detect human legs using a LiDAR sensor attached at a low position without additional sensors. This study proposes a 3D laser-based human leg detection and tracking framework to improve the robustness of human-following for autonomous mobile robots. With a deep learning-based human leg detector, using the Point-Voxel-RCNN model, the proposed 3D human leg tracking system can help robots robustly follow humans in cluttered and crowded environments. Additionally, we demonstrate the robustness of our method in a practical cluttered environment by comparing the performance of a conventional human leg detection and following system.