Abstract: In order to realise laser cladding repair of mining scrapers,for the problem of maintaining high efficiency and stability of laser cladding robots during operation,the trajectory planning method in the joint space was studied according to the kinematic characteristics of the robots,and a hybrid genetic particle swarm optimization algorithm was proposed. Based on the particle swarm optimization algorithm,the method introduced the crossover and mutation behaviours in the genetic algorithm by constructing adaptive inertia weights and dynamic learning factors,and fitted the trajectory into the joint space of the robot using 3-5-3 polynomial interpolation. The hybrid genetic particle swarm optimization algorithm,chaotic particle swarm optimization algorithm and standard particle swarm optimization algorithm were compared,and after obtaining the optimal interpolation time,simulation was carried out in MATLAB software,and the change process of position,velocity and acceleration of each joint over time was kept in the ideal continuity interval,which realised the time-optimal motion planning in the joint space,and the optimal time was reduced from 5.058 0 s of the standard particle swarm optimization algorithm to 4.633 0 s,and the robotic arm trajectory planning time was shortened by 8.4 %,which verified the feasibility of the proposed algorithm in the trajectory planning of the laser cladding robot for repairing the mining scraper.

Key words: mining scrape, time-optimal, trajectory planning, particle swarm optimization algorithm, genetic algorithm, robotic arm