Optimization of shotcrete manipulator motion trajectory based on AIWCPSO algorithm

To address issues in traditional shotcrete manipulator trajectory planning algorithms, such as abrupt transitions between multiple path segments and low shotcrete efficiency and uniformity caused by frequent starts and stops, an optimized motion trajectory method based on the adaptive inertia weight and acceleration coefficient particle swarm optimization (AIWCPSO) algorithm was proposed. An improved multi-segment trajectory planning algorithm was developed, which incorporated a transition strategy combining linear and arc trajectories. Vertical linear motion was replaced with arc motion. Additionally, a sinusoidal acceleration and deceleration start-stop algorithm was used to plan the trajectory of the end effector of the manipulator at start and stop points to prevent abrupt changes in acceleration. The middle segment of linear and arc trajectories was planned for uniform motion, ensuring smooth and uniform movement at the end effector of the manipulator. Using the AIWCPSO algorithm, motion parameters were optimized under kinematic constraints to achieve the optimal shotcrete time and speed, thereby improving the efficiency and uniformity of the shotcrete manipulator. Experimental results showed that, compared with traditional trajectory planning algorithms, the improved multi-segment trajectory planning algorithm increased average shotcrete efficiency by 25.42% and significantly improved the uniformity of shotcrete trajectory. After optimization with the AIWCPSO algorithm, shotcrete efficiency increased by 1.330 8%.