Smooth and efficient motion planning of large-scale and cooperative multi-arm tunnel drilling robot

Abstract

To address the motion planning challenges in multi-arm cooperative operations of tunnel rock drilling robots, we establish forward/inverse kinematics models for drilling arms using an enhanced Denavit-Hartenberg method combined with radial basis function neural networks. An improved genetic algorithm (IGA) is developed, integrating heuristic crossover operators, adaptive mutation operations, and local neighborhood search mechanisms to optimize multi-arm trajectories with the objective of minimizing end-effector travel distance. A joint-space collision avoidance strategy is proposed using an enhanced artificial potential field (IAPF) method that incorporates both attractive potential fields and repulsive potential functions. Simultaneously, quintic B-spline-based trajectory planning ensures smooth motion continuity during collaborative drilling operations. Experimental validation demonstrates that the IGA-IAPF integration achieves 37.2% reduction in collision probability compared to conventional methods, while maintaining joint angular accelerations below 0.25 rad/s2 for all manipulators.