Obstacle avoidance planning of space manipulator end-effector based on improved ant colony algorithm

Obstacle Avoidance Path Planning of Space Robot Based on Improved Particle Swarm Optimization

In order to meet security requirements of space on orbit service, an obstacle avoidance trajectory planning method using improved particle swarm optimization had been presented in this paper. On the basis of the actual overall structure of 7 degrees of freedom redundant space manipulator and the characteristics of obstacles, the envelope method was used to model the arm and obstacles, respectively. The limit conditions to avoid the collision between them were analyzed. Then, the fitness function under the symmetrical conditions of avoiding the collision and searching for the shortest trajectory was constructed. In addition, the obstacle avoidance trajectory planning was solved based on improved particle swarm optimization (IPSO). Finally, simulation experiments were carried out to prove its effectiveness and rationality, where there were symmetrical advantages in two aspects. It can be concluded that the presented method based on IPSO has strong robustness.

Motion planning optimization of trajectory path of space manipulators

With the development of the aerospace industry, the work carried out inside and outside the weightless space station is becoming more and more complicated. In order to ensure the safety of astronauts, space manipulators are used for operation, but it will disturb the space station that is a base during work. In order to solve the above problems, in this paper, the planning method of the motion trajectory of manipulators, the motion model of manipulators and the particle swarm optimization (PSO) algorithm used for optimizing the trajectory are briefly introduced, the multi-population co-evolution method is used to improve the PSO algorithm, and the above two algorithms are used to optimize the motion trajectory of the floating pedestal space manipulator with three free degrees of rotation in the same plane by the matrix laboratory (MATLAB) software. It is compared with genetic algorithm. The results show that the improved PSO algorithm can converge to a better global optimal fitness with fewer iterations compared with the traditional PSO algorithm and genetic algorithm. The obtained motion trajectory optimized by the improved PSO algorithm has less disturbances to the pedestal posture, and less time is required to achieve the target motion; moreover the changes of mechanical arm joint are more stable during the motion.