Multi-node Cooperative Landing Position Planning Method Based on Genetic Particle Swarm Optimization

Abstract

The invention discloses a multi-node coordinated landing position planning method for a detector based on a genetic particle swarm algorithm, which belongs to the technical field of aerospace deep space exploration and artificial intelligence technology. According to the method of the invention, aiming at the landing position planning problem of the multi-landing node detector system, the grid map is used to process the landing area map data and obstacle information, the particle swarm algorithm is discretized, the crossover mutation operation in the genetic mechanism is integrated, and the The adaptive genetic factor controls the occurrence probability of mutation operation and enhances the global search ability of the algorithm. The method can effectively deal with the anti-interference constraints and connection constraints of the multi-landing node detector system, generate the detector landing points that satisfy the distance constraints, prevent the collision between the detector nodes and the ground obstacles, and maintain the existence of flexible physical connections. The nodes are evenly distributed to increase the stability and safety of the probe landing.

Description

technical field

[0001] The invention relates to a multi-node cooperative landing position planning method for a detector, in particular to a multi-node cooperative landing position planning method for a detector based on a genetic particle swarm algorithm, which belongs to the technical field of aerospace deep space detection and artificial intelligence technology. Background technique

[0002] The aerospace field is one of the main fields for the development of autonomous intelligent technology in the 21st century. For deep space exploration missions, the target is generally far away from the earth, the communication delay is large, and the environment is uncertain, which leads to huge challenges in the operation and control of the probe.

[0003] Using artificial intelligence technology to improve the on-board autonomy of the probe is a mainstream direction to solve the above-mentioned problems of deep space probes. The landing of the probe is a key stage in the deep spac...

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