Method for planning space manipulator tail end effector avoidance path based on ant colony algorithm

Abstract

The invention designs a method for planning a space manipulator tail end effector avoidance path based on an ant colony algorithm. The method comprises the following steps: modeling for a space manipulator and an environment; defining a fitness function specific to the aim of keeping a short and safe avoidance path between an initial point and a target point; improving a search strategy, a transfer rule and an information element update method in the ant colony algorithm; searching for an optimal avoidance path by using the improved ant colony algorithm. Through adoption of the method, the avoidance of the space manipulator tail end effector is realized by using the improved ant colony algorithm.

Description

technical field [0001] The invention relates to a method for planning obstacle-avoiding paths of space manipulators, in particular to a method for planning obstacle-avoiding paths of end devices of space manipulators based on ant colony algorithm, which belongs to the field of aerospace control. Background technique [0002] A space robot is composed of a robot body (satellite) and a robotic arm carried by it. Because the robot body carries a gas propeller, it can fly or float freely in the microgravity environment of space, thereby expanding the working space of the robot, so it will replace astronauts in various extravehicular operations and become the main research of space robots in the future. one of the directions. Debris in the space environment, test equipment outside the space cabin, etc. may become obstacles during the on-orbit operation of the space manipulator, and the collision between the manipulator and obstacles will not only interfere with the completion of...

AI Technical Summary

Problems solved by technology

For the path planning problem, the methods that have been adopted include C space method, A * Search method, improved artificial potential field method, genetic algorithm, ant colony algorithm, etc., they all have certain limitations

Since the calculation time of the C space method is longer than the response time of the manipulator, its application in actual obstacle avoidance is limited; A * The calculation amount of the algorithm will increase sharply with the increase of the dimension, and its space-time requirements are difficult to be satisfied; the improved artificial potential field method is very suitable for dealing with dynamic obstacle avoidance, but it is easy to fall into the local minimum point; in complex environments , it is difficult for the genetic algorithm to find a feasible path that satisfies the constraints; the ant colony algorithm has strong robustness, can perform distributed calculations, and is easy to combine with other methods, but it also has some defects, such as slow convergence, Easy to get stuck in local optima

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