Research of Pose Control Algorithm of Coal Mine Rescue Snake Robot

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.