Mathematical Problems in Engineering

Volume 2018, Article ID 4751245, 9 pages

https://doi.org/10.1155/2018/4751245

## Research of Pose Control Algorithm of Coal Mine Rescue Snake Robot

^{1}Engineering Training Center, Xi’an University of Science & Technology, Xi’an 710054, China^{2}College of Electrical and Control Engineering, Xi’an University of Science & Technology, Xi’an 710054, China

Correspondence should be addressed to Yun Bai; moc.qq@269163494

Received 10 November 2017; Revised 17 January 2018; Accepted 24 January 2018; Published 22 February 2018

Academic Editor: Luis Gracia

Copyright © 2018 Yun Bai and YuanBin Hou. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

Aiming at how to achieve optimal control of joint pitch angles in the process of the robot surmounting obstacle, taking the developed coal mine rescue snake robot as an experimental platform, a pose control algorithm based on particle swarm optimization weight coefficient of extreme learning machine (PSOELM) is proposed. In order to obtain the optimized hidden layer matrix of the extreme learning machine (ELM), particle swarm optimization (PSO) is applied to optimize the weight coefficient of hidden layer matrix. The simulation and experiment results prove that, compared with the ELM algorithm, the smaller mean square error (MSE) between the joint pitch angles of robot and the expected values is acquired by the PSOELM, which overcomes the shortcoming that traditional extreme learning machine cannot reach the best performance because of the random selection of the parameters of the hidden layer nodes. PSOELM is superior to ELM algorithm in control accuracy, fast searching for the optimal and stability. Optimal control of robot’s joint pitch angles is achieved. The algorithm is applied to the surmounting obstacle control of the developed snake robot, and it lays the foundation for further implement of the coal mine rescue.

#### 1. Introduction

The snake robot has the characteristics of good stability, multiple degrees of freedom, multimovement gaits, small cross-section, and so forth. The snake robot can walk on the rugged grounds, can move through caves, and has strong obstacle surmounting abilities. Therefore, compared with the traditional mobile robot driven by wheels or caterpillar tracks, faced with the complex environment after a coal mine disaster, the snake robot can quickly and reliably respond to rescue work. The robot can replace the rescue personnel to enter the site as a first responder. The site information can be collected, and scientific basis for a rapid rescue is provided. Hence developing a coal mine rescue snake robot has a very important practical significance. At the same time, multiple degrees of freedom and flexibility in the movement of the snake robot has brought opportunities and challenges for the research of its pose control. To allow the snake robot to move as flexibly as biological snakes, there are a variety of methods for pose control such as the discrete curve method, dynamics and kinematics model generation method, neural network model generation method (e.g., CPG: central pattern generator) [1], and so on.

*Discrete Curve Method. *This method refers to that a curve which is defined according to biological snake gait is fitted by the body of snake robot; thus each joint rotating angle of the robot is obtained, also known as the “inverse kinematics method.” According to the serpenoid curve of serpentine locomotion proposed by Hirose [2], Wang et al. [3] used the specific parameters including the wave propagation rate and the number to disperse the serpenoid curve, and the governing equations of serpentine locomotion were obtained. Ye et al. [4] proposed a simple snake curve for rotational and lateral motion and thus made the motion control equation of snake robot simpler. Hatton and Choset [5] used a back curve (backbone curve) to extract the waveform of the snake robot and to fit the waveform by using the simulated annealing algorithm; hence multiple gaits for the snake robot were acquired. Xie et al. [6] developed a prototypical underwater snake robot. The snake’s body is composed of 16 light waterproof small servomotors, and the serpentine locomotion control of robot was realized by using the snake curve. The discrete curve method has the advantages of simple implementation and easy control. The disadvantage is that the jumping of joint angular velocity and torque can lead to the servomotors locking phenomenon and the damage of servomotors.

*Dynamic and Kinematic Model Generation Method. *Aiming at the snake robot with linkage type equipped with three driven wheels, Ostrowski and Burdick [7] established a dynamic model of serpentine locomotion by using the Lagrange method and analyzed the controllability of the snake robot. Liljeback et al. [8] established a dynamic model of the snake robot by making use of the relationship between contact force and relative angle, thereby achieving control of obstacle-aided snake robot locomotion. Chen et al. [9] established a space kinematic model (spatial linkage model), and two kinds of three-dimensional motion of snake robot, side winding and lateral rolling, were implemented. Wei and Sun [10] established a kinematic model of snake robot based on orthogonal joints, achieving control of the bridge cable climbing gait. Guo et al. [11] put forward a velocity tracking control algorithm avoiding the singular posture based on the dynamic and control unified model. The algorithm was applied to motion control of a snake robot with passive wheels. Cheng et al. [12] presented the method of surmounting obstacle for snake robot with 16 P-R-T unit modules based on the kinematic model. The advantage of the dynamic and the kinematic model generation method is that complex and accurate gait control can be realized. The disadvantage is that the modeling process is difficult, and the controller must perform a large amount of calculation, and the adaptability to unknown environments is poor.

*Neural Network Model (CPG) Generation Method. *The CPG method has been widely used in the control of snake robots. Crespi and Ijspeert [13] made use of the CPG model and accomplished optimal control of the swimming and crawling of a snake robot. Lu et al. [14] proposed a cyclic inhibitory CPG controller and serpentine locomotion of a snake robot was successfully implemented. Aiming to resolve the problem of low efficiency and instability of parameter tuning in the cyclic inhibitory CPG control model, Lian et al. [15] presented a parameter optimization method of CPG model based on genetic algorithms, and the method was effectively applied to the gait control of a snake robot. Gao et al. [16] established a CPG motion control network by using Hopf oscillators and applied the network to the serpentine locomotion of a snake robot, which improved the environmental adaptability of the robot. CPG control has the advantage of easy integration of environmental information, so that the snake robot has the ability to adapt to the environment. The disadvantage is that the control parameters in CPG model need to be further optimized.

Based on the present methods for pose control of the snake robot, combined with the advantages of particle swarm optimization (PSO) algorithm and extreme learning machine (ELM), a pose control algorithm based on particle swarm optimization weight coefficient of extreme learning machine (PSOELM) is put forward. Taking the developed coal mine rescue snake robot as an experimental platform, and, in order to achieve optimal control of robot’s joint pitch angles, the PSOELM algorithm is applied to the robot’s surmounting obstacle behavior.

#### 2. Design of the Coal Mine Rescue Snake Robot

##### 2.1. Design of Mechanical Structure

The mechanical body of the coal mine rescue snake robot adopts orthogonal joint connection, which has four orthogonal joints, as is shown in Figure 1. The total length of the snake robot is 1 m, and it is composed of the head, the body, and the tail. Considering the rugged tunnel environment after the coal mine disaster, the robot is driven by the self-made blades wheels. Compared with snake robot driven by wheels or caterpillar tracks, this robot has better obstacle surmounting capabilities. The mechanical structure of the robot is shown in Figure 2.