Path planning method for power inspection robot based on simulated annealing ant colony algorithm

Abstract

The invention discloses a path planning method for a power inspection robot based on a simulated annealing ant colony algorithm. The method is as follows: construct a map according to the outdoor substation environment, plan the running path and stop points of the power inspection robot, and construct a topological map according to the coordinate information of the stop points; according to the inspection task, use the classic Dijkstra algorithm to obtain the distance between any two inspection points Local shortest path, while constructing an undirected graph; according to the path length and the number of stops passed, the global optimal path planning problem is transformed into a multi-objective traveling salesman problem, and the simulated annealing ant colony algorithm is used to plan on the basis of the undirected graph The global optimal path; replace the local path in the global optimal path with the local shortest path to get the complete final path. The invention has the advantages of fast convergence speed, high global search ability, low complexity, high feasibility and fast speed.

Description

technical field [0001] The invention relates to the technical field of path planning of an inspection robot, in particular to a method for path planning of an electric inspection robot based on a simulated annealing ant colony algorithm. Background technique [0002] With the increase of social demand, the number of substations is increasing year by year, and the scale is getting bigger and bigger. The workload of substation inspection is more heavy. The traditional manual inspection method is labor-intensive and low in efficiency. It is not suitable for working in harsh environments. Unable to continue to meet the ever-increasing industry requirements. In the environment of large outdoor substations, power inspection robots can replace manual inspections, conduct autonomous inspections of power equipment through autonomous navigation and positioning, and can intelligently analyze data information through image processing technology, so the application prospects are becoming...

AI Technical Summary

Problems solved by technology

For example, the simulated annealing algorithm (SA) in the traditional algorithm can be applied to large-scale optimization problems. The algorithm is simple and the operation efficiency is high, but the convergence speed is slow and the randomness is poor.

For example, the ant colony algorithm (ACA) in the intelligent bionics algorithm has positive feedback, parallelism and strong robustness, and has a good global optimization ability, but it is easy to fall into a local optimal solution.

At present, any single path planning algorithm cannot perfectly solve all practical path planning problems

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