Research On Autonomous Control Method Of Drilling Robot

Rock drilling robot is the key equipment for drilling and blasting mining and engineering construction.The intelligent development level of rock drilling robots is directly related to the construction level of mine engineering and affects the mining efficiency and economy.The underground mining roadway is narrow,with many obstacles and unstructured,which brings challenges to the safe movement of drilling robots and drilling arms.Therefore,aiming at the intelligent autonomous control method of the drilling robot,this paper carries out research on the path planning of the drilling robot,obstacle avoidance method of drilling arm and trajectory planning,etc.The main research contents are as follows:(1)Research on path planning method of improved artificial potential field method of rock drilling robot fusion with DWA.Given the narrow,many obstacles and unstructured characteristics of the underground mining roadway,an improved artificial potential field method(D-APF)based on the dynamic window method was proposed for path planning of the drilling robot.Based on the improved repulsive potential field function,the adaptive repulsive potential field function is constructed by switching strategy to overcome the unreachable problem of the traditional artificial potential field method.At the same time,APF is combined with the dynamic window method to provide escape force and overcome the problem that the traditional artificial potential field method is easy to fall into local extreme value region.It is verified by simulation.(2)Motion planning algorithm for collision detection of double drilling arm based on sphere and capsule body bounding box.The improved D-H method is used to model the robot drill arm of rock drilling.Combined with the structural characteristics of the drill arm,a collision detection strategy based on the sphere and capsule box is adopted to establish a simplified model of the drill arm.The collision detection problem is transformed into the problem of solving the shortest distance between space points and lines and between lines,and several common situations in the process of drill arm movement are analyzed.The improved RRT algorithm can overcome the problems of poor orientation and low efficiency of the original algorithm.It is verified by a simulation experiment.(3)Trajectory planning method of optimized time with improved particle swarm optimization algorithm for drill arm.To improve the operational stability and efficiency of the drill arm,a trajectory planning algorithm based on improved particle swarm optimization time was proposed.Piecewise polynomial interpolation is used for trajectory fitting in joint space to ensure the stable operation of the drill arm.Aiming at the constraints of velocity and acceleration,an improved particle swarm optimization algorithm combining adaptive inertia weight and dynamic adjustment learning factor was proposed to solve the problem that standard particle swarm optimization algorithm is prone to fall into local extreme value and slow convergence rate and to improve the stability and operation efficiency of drill arm movement.Simulation experiments were carried out.