Energy-saving path planning method for rotor unmanned aerial vehicle based on combination of corner and distance

Abstract

The invention discloses an energy-saving path planning method for a rotor unmanned aerial vehicle based on combination of a corner and a distance. According to the method, energy consumption of a rotor unmanned aerial vehicle flying over a winding path is estimated based on the corner and distance features of the winding path, so that an incomprehensive problem caused by only using the distance to estimate path energy consumption in energy-saving path planning of the rotor unmanned aerial vehicle is solved. A three-dimensional weight matrix is constructed and an energy-saving path is planned by combining a greedy strategy. Therefore, the endurance of the rotor unmanned aerial vehicle is enhanced; and the energy limitation in the application of the rotor unmanned aerial vehicle is improved. According to the energy-saving path planning method disclosed by the invention, with simultaneous consideration of the path corner and distance, the energy consumption of the rotor unmanned aerial vehicle flying over the path is evaluated. The method can be applied to UAV-assisted task point traversal, so that the flight energy consumption is reduced and the amount of task completion for single charging is increased.

Description

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AI Technical Summary

Benefits of technology

This patented technology improves how we plan flying objects by taking into account their own position within them's environment (their corners). By doing this, it helps us calculate where they should go next without wasting too much fuel or having other things taken up time waiting for another object) while still optimizing its use of space resources. It reduces the amount of electricity needed during flight compared to previous designs, resulting in longer lifespan and better overall functioning of the aircraft.

Problems solved by technology

Technological Problem: Current Energy Saving Planing methods are limited because they use fixed distances or time intervals instead of optimizing their performance. Additionally, current approaches require multiple paths with different lengths but cannot accurately determine if each one works well individually due to factors like weather conditions and other environmental variables affecting how fast it travels through airspace.

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