A Head Control Strategy of the Snake Robot Based on Segmented Kinematics

Head control is important for snake robots to work in an unknown environment. However, the existing methods of head control have certain application limitations for snake robots with different configurations. Thus, a strategy for head control based on segmented kinematics is proposed. Compared with the existing head control strategies, our strategy can adapt to different structures of snake robots, whether wheeled or non-wheeled. In addition, our strategy can realize the accurate manipulation of the snake robot head. The robot body is divided into the base part, neck part and head part. First, parameters of backbone curve are optimized for enlarging the area of the support polygon. Then the desired pose for the head link and the dexterous workspace of the head part can in turn derive the desired position and direction of the end frame for the neck part. An optimization algorithm is proposed to help the end frame of the neck part approach a desired one and obtains the joint angles of the neck part. When the actual frames of the neck part are determined, the dexterous workspace of the head part will cover the desired pose of the head link. Then the TRAC-IK inverse kinematics algorithm is adopted to solve the joint angles of the head part. To avoid the collision between the body and the ground, a trajectory planning method of the overall body in Cartesian space is proposed. Finally, simulations validate the effectiveness of the control strategy

A Method for Measurement of Absolute Angular Position and Application in a Novel Electromagnetic Encoder System

For the encoders, especially the sine-cosine magnetic ones, a new method to measure absolute angular position is proposed in the paper. In the method, the code disc of the encoder has only two circle tracks and each one was divided into N and (N-1) equal code cells. The cell angles, changing from 0° to 360° between any two neighboring code cells, are defined to represent any position on the code disc. The position value of the same point can be represented by different cell angle values of different tracks and the absolute angular position of the point can be obtained by the difference value between the cell angle value of the outer track and the inner one. To validate the correctness of the method theoretically, the derivation process of the method was provided. An electromagnetic encoder system was designed and the experimental platform was established to test the method. The experimental results indicate that the electromagnetic encoder can measure the absolute angular position. Besides, it shows that the method is easy to be realized in algorithm and can reduce computational complexity and decrease dimension of the encoder

A GAN-BPNN-Based Surface Roughness Measurement Method for Robotic Grinding

Existing machine vision-based roughness measurement methods cannot accurately measure the roughness of free-form surfaces (with large curvature variations). To overcome this problem, this paper proposes a roughness measurement method based on a generative adversarial network (GAN) and a BP neural network. Firstly, this method takes images and curvature of free-form surfaces as training samples. Then, GAN is trained for roughness measurement through each game between generator and discriminant network by using real samples and pseudosamples (from generator). Finally, the BP neural network maps the image discriminant value of GAN and radius of curvature into roughness value (Ra). Our proposed method automatically learns the features in the image by GAN, omitting the independent feature extraction step, and improves the measurement accuracy by BP neural network. The experiments show that the accuracy of the proposed roughness measurement method can measure free-form surfaces with a minimum roughness of 0.2 μm, and measurement results have a margin of 10%

A Hybrid Mapping Method with Position and Stiffness for Manipulator Teleoperation

Transferring versatile skills of human behavior to teleoperate manipulators to execute tasks with large uncertainties is challenging in robotics. This paper proposes a hybrid mapping method with position and stiffness for manipulator teleoperation through the exoskeleton device combining with the surface electromyography (sEMG) sensors. Firstly, according to the redefinition of robot workspace, the fixed scale mapping in free space and virtual impedance mapping in fine space are presented for position teleoperation. Secondly, the stiffness at the human arm endpoint is predicted and classified into three levels based on the K nearest neighbor (KNN) and XGBoost, and the stiffness mapping method is utilized to regulate the stiffness behavior of manipulator. Finally, the proposed method is demonstrated in three complementary experiments, namely the trajectory tracking in free space, the obstacle avoidance in fine space and the human robot interaction in contact space, which illustrate the effectiveness of the method