Research On Pose Control Method Of Coal Mine Snake Detecting Robot

After the coal mine accident,rescue robot can replace rescuers in the first time to enter the disaster scene to carry out search and rescue work,quickly identify the scene environment,timely find trapped miners,provide scientific basis for rapid rescue.However,the mine environment after the disaster is very complex and unknown.Therefore,rescue robot is required to have the ability of environment identification,planning and decision-making,motion control and so on,in order to ensure the successful completion of rescue tasks in the coal mine.The snake detection robot system in the coal mine is proposed and constructed,the key technologies involved in the pose control of the robot are studied,which including the relative localization of the robot,the control of the motion attitude,the optimal control of the autonomous obstacle surmounting,the effective identification of the environment and the autonomous obstacle avoidance,and a snake robot for environmental detection in underground coal mine after disaster is developed,aiming at improving the intelligent control and local autonomy of the coal mine snake detecting robot.Based on the analysis of the underground environment after the disaster,aiming at the functional requirements of the detection robot,a multi-joint snake robot is designed and developed.The mechanical body of the robot is connected by orthogonal joints,and the mobile mechanism is driven by self-made blades wheels.The control system is composed of hardware platform and software system.The hardware platform is a three-layer distributed architecture,which consists of environment perception layer,planning decision layer and action execution layer.The software system is a functional modularization architecture,which consists of six functional modules:environment detection system,localization system,attitude control system,obstacle surmounting and obstacle avoidance module,communication system and host computer system.The snake robot can lay the foundation for the theoretical and methodological verification of the thesis.Aiming at the problem of snake robot localization in closed complex environment of underground coal mine,the method for estimating the curvature and path angle of robot trajectory is proposed,which replaces the traditional idea of estimating complex ground parameters in coal mine tunnel,and a simple localization model of snake detecting robot based on turning is established.On this basis,a method of location method of dead reckoning for coal mine snake detecting robot based on Kalman filter algorithm and Deep Learning Algorithms is put forward,which adopts Kalman filter algorithm to eliminate the white Gaussian noise in the path angle signal of the robot.Aiming at various drifts from low-frequency stage in path angle signal,a prediction model of gyroscope output value based on LSTM(Long Short-Term Memory)deep neural network is established,which can predict the gyroscope output value in the future period of time,and the relative location of dead reckoning can be realized.Experiments show that the method can realize the dead reckoning of the snake detection robot,the minimum relative error of the robot’s location is 3.299 ×10-12cm and the minimum error of the path angle is 2.173 × 10-5rad.Aiming at the robot attitude control problem of uneven environmental ground in coal mine,the kinematics mechanism of orthogonal snake robot is analyzed by using D-H(Denavit-Hartenberg)analysis method,and the model of three-joint mechanism with two connecting rods is constructed,which avoids the complexity of establishing the kinematics model of snake robot.And then,an improved motion control function method based on simplified Serpenoid curve is proposed,curvature error is introduced.The mathematical models of snake robot with different motion attitudes of serpentine,concertina and head raising are established,the control functions of the deflection angle,pitch angle and relative rotation angle are derived with different motion attitudes of the snake robot.The optimal parameters obtained by simulations are applied to the robot’s motion control,and the control of snake robot’s motion attitude adapted to complex ground is realized,it lays a theoretical foundation for the realization of the robot’s autonomous surmounting obstacle and obstacle avoidance function.Aiming at the problem of how to determine the joint pitch angles of robot in the process of the robot autonomous surmounting obstacle,a pose control algorithm is proposed based on improved Particle Swarm Optimization weight coefficient of Extreme Learning Machine(PSOELM),the dynamic inertia coefficient is derived.In order to obtain the optimized hidden layer matrix of the Extreme Learning Machine(ELM),improved Particle Swarm Optimization(PSO)is applied to optimize the weight coefficient of hidden layer matrix.PSOELM overcomes the shortcoming that traditional ELM cannot reach the best performance because of the random selection of the parameters of the hidden layer nodes.The simulation and experiment results prove that compared with the ELM algorithm,PSOELM algorithm not only continues the characteristics of fast learning of ELM,but also has better control accuracy,fast optimization and stability than ELM,and optimal control of robot’s joint pitch angles is achieved.Aiming at the problem of robot environment identification and modeling in unknown environment,a multi sensor data fusion algorithm based on Genetic Algorithm optimization of the Variable Structure Fuzzy Neural Network(GAVSFNN)is proposed.The neural network is introduced into the inference of fuzzy rule,and the improved fuzzy control rules based on probability theory is established.The fuzzy rule base is adjusted by simplifying the structure of the neural network,the self-learning of fuzzy membership functions and the preferential extraction of fuzzy rules are realized.The genetic algorithm is used to optimize the learning parameters of the variable structure fuzzy neural network,which overcomes the shortcoming that the conventional BP algorithm is easy to fall into local optimum,and the fast and global optimization of the parameters is realized.The experimental results show that compared with VSFNN algorithm,GAVSFNN can obtain higher identifying accuracy for environmental model.The average error is 2.725×10-3.On this basis,the soft model of robot’s autonomous obstacle avoidance system based on the knowledge base is established,which generates obstacle avoidance behavior commands to control the deflection angles of the robot,so that the robot can safely avoid obstacles and the function of autonomous obstacle avoidance is realized.Taking the developed multi-joint snake robot as a platform and combining with algorithms proposed in this paper,an experimental system of coal mine snake detecting robot is constructed.Field experiments are carried out in the simulated coal mine tunnel of Xi’an University of Science and Technology Practical Training Base.Experiments show that the theories and methods proposed in this thesis can provide theoretical support for the pose control of coal mine snake detecting robot in the underground unstructured complex environment after the disaster,and also lay a foundation for the research of snake robot’s pose control in other disaster environments.