Implementation of robotic writing ability is recognized as a di±cult task, which involves complicated image processing and robotic control algorithms. This paper introduces a novel approach to robotic writing by using human-robot interactions. The method applies a motion sensing input device to capture a human demonstrator's arm trajectories, uses a gesture determination algorithm to extract a Chinese character's strokes from these trajectories, and employs noise¯ltering and curve¯tting methods to optimize the strokes. The approach displays real-time captured trajectories to the human demonstrator; therefore, the human demonstrator is able to adjust his/her gesture to achieve a better character writing e®ect. Then, our robot writes the human-gestured character by using the robotic arm's joint values. The inverse kinematics algorithm generates the joint values from the stroke trajectories. Experimental analysis shows that the proposed approach can allow a human to naturally and conveniently control the robot in order to write many Chinese characters. Additionally, this approach allows the robot to achieve a satisfactory writing quality for characters with a simple structure, with the potential to write more complex characters.
Interferometric synthetic aperture radar (InSAR) technology can obtain one-dimensional surface displacements in the radar line of sight (LOS). In the field of mining subsidence, large 3D movements often occur at the same time, and hence InSAR derived one-dimensional LOS displacements can hardly reflect the actual surface motion in mining areas. To realize the monitoring of three-dimensional large surface displacements in mining areas, a method for monitoring three-dimensional large surface displacements in mining areas that combines SAR pixel offset tracking (OT) and an improved mining subsidence model is proposed in this article. First, a new functional relationship between surface subsidence and horizontal movement combined with the characteristics of the overburden rock stress and the deformation characteristics of the fractured rock mass in coal mining areas is established. Then, a three-dimensional surface deformation model is established based on the proposed relationship between surface subsidence and horizontal movement and the radar projection equation, and finally, the optimal parameters of the deformation model are inverted iteratively using LOS deformation results obtained by OT method to retrieve the three-dimensional large displacements of the surface. The significant advantage of the method proposed in this article is that it can accurately acquire the 3D large surface displacements using only two SAR amplitude images with the same imaging geometry. To verify the accuracy and reliability of the proposed algorithm, two scenes of high-resolution spotlight TerraSAR-X images are used in this paper to conduct a three-dimensional surface displacement monitoring experiment on a working panel in the Daliuta mining area in Shaanxi Province, China, based on the proposed method. Experimental monitoring results show that the maximum surface subsidence is approximately 4.5 m, and the maximum horizontal movements in the strike and dip directions are approximately 1.4 m and 1.2 m, respectively. Using GPS measurements to verify the monitoring results, the root mean square error (RMSE) of vertical subsidence is 6.8 cm, and the RMSE of horizontal movement is 7.1 cm. Compared with those in the original mining subsidence model, the accuracies of vertical subsidence and horizontal movement in the proposed model are increased by 28.2% and 37.5%, respectively, which proves the reliability and accuracy of the proposed method.
The paper presents an approach to applying a classifier ensemble to identify human body gestures, so as to control a robot to write Chinese characters. Robotic handwriting ability requires complicated robotic control algorithms. In particular, the Chinese handwriting needs to consider the relative positions of a character's strokes. This approach derives the font information from human gestures by using a motion sensing input device. Five elementary strokes are used to form Chinese characters, and each elementary stroke is assigned to a type of human gestures. Then, a classifier ensemble is applied to identify each gesture so as to recognize the characters that gestured by the human demonstrator. The classier ensemble's size is reduced by feature selection techniques and harmony search algorithm, thereby achieving higher accuracy and smaller ensemble size. The inverse kinematics algorithm converts each stroke's trajectory to the robot's motor values that are executed by a robotic arm to draw the entire character. Experimental analysis shows that the proposed approach can allow a human to naturally and conveniently control the robot in order to write many Chinese characters.
Monitoring ground displacement produced by underground mining is essential to ensure the safety of infrastructure over mining areas. Differential synthetic aperture radar (DInSAR) can only obtain the 1-D [i.e., along the line-of-sight (LOS) direction] displacement component. In this study, we present an improved algorithm for retrieving and predicting 3-D displacement fields induced by underground mining based on the LOS displacement derived from DInSAR and the probability integral method (PIM). Whole parameters included in the standard PIM model are involved in the improved algorithm. In addition, the interaction between multiple working panels is considered and incorporated into the model. Next, a stochastic optimization technique hybridizing the cultural algorithm and random particle swarm optimization has been designed to retrieve model parameters, which can be used to retrieve and predict the 3-D displacement field. Simulated experiments show that the root mean square errors (RMSEs) are 10, 12, and 17 mm in the vertical, east-west, and north-south directions, respectively, by comparing the simulated and retrieved 3-D displacement. Furthermore, the capability of the proposed method is investigated and validated in the Xuehu mining area of China using three ALOS PALSAR acquisitions. Our results agree well with leveling measurements in the vertical direction with an RMSE of 38 mm. Although the retrieved horizontal displacement cannot be validated due to a lack of field surveys, these displacement fields coincide spatially with the evolution of mining excavation. Index Terms-Cultural algorithm and random particle swarm optimization (CA-rPSO), mining displacement, probability integral method (PIM), three-dimensional (3-D) displacement.
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