Electric power inspection robot path planning method based on simulated annealing ant colony algorithm

Abstract

The invention discloses an electric power inspection robot path planning method based on a simulated annealing ant colony algorithm. The method includes the steps: building a map according to an outdoor transformer substation environment, planning the running path and stop points of an electric power inspection robot and building a topological map according to coordinate information of the stop points; acquiring the local shortest path of two optional inspection points by the aid of a classical Dijkstra algorithm according to inspection tasks, and building an undirected graph; transforming a global optimal path planning problem into a multi-target traveling salesman problem according to path lengths and the number of passing stop points, and planning a global optimal path on the basis of the undirected graph by the simulated annealing ant colony algorithm; replacing a local path in the global optimal path by the local shortest path to obtain a complete final path. The method is high inconvergence rate and global searching ability and has the advantages of low complexity, high feasibility and high 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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