An integrated approach for inverse kinematics of unconventional manipulators using Monte Carlo random sampling method and Newton iterative method

Accurate determination of the inverse kinematics for the drilling arm of a drilling and anchoring robot is crucial in achieving automatic support in coal mine roadways. Addressing issues such as poor accuracy, limited real-time performance, and susceptibility to local optima, this study presents a novel approach for solving the inverse kinematics problem of an unconventional mechanical arm by combining Monte Carlo random sampling method and Newton iteration method. Initially, a kinematics model is established based on the structure of the drilling arm, and individual coordinate systems are defined for each joint to enable forward kinematics solutions. The Monte Carlo method is employed to determine the motion space of the drill boom. By subdividing the motion space into smaller cubes with a defined step size, a mapping relationship is established between the spatial coordinates of these cubes and the corresponding joint variable values. Using the transformation matrix associated with a given target space poin