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Unmanned aerial vehicle route planning method based on improved bat algorithm

A bat algorithm and route planning technology, applied in three-dimensional position/channel control, vehicle position/route/altitude control, non-electric variable control and other directions, can solve the problem that the setting of the two-dimensional experimental environment is too simple, the algorithm execution stability is not high enough, There are no problems such as 3D simulation experiments

Active Publication Date: 2019-01-04
SHENYANG AEROSPACE UNIVERSITY
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  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

The technical effect that this technology allows each person's bat or animal to locate itself by means of sound waves without being affected from visual cues like their eyesight. This makes it possible to detect objects within its environment more accurately than traditional methods.

Problems solved by technology

This patented technical problem addressed in this patents relates to improving the quality (accuracy) and efficiency (lifeline flying distance) of autonomous drones' routing systems through their own optimized solutions called swarman networks or metaheuristics. These techniques aim to improve the overall outcome of navigation tasks such as navigating between multiple locations while maintaining consistence across all possible paths within reasonable limits.

Method used

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  • Unmanned aerial vehicle route planning method based on improved bat algorithm
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  • Unmanned aerial vehicle route planning method based on improved bat algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0105] In the two-dimensional environment simulation experiment, the overall picture of the two-dimensional terrain environment can be reflected from the route planning effect map, and the topographic map is no longer presented separately. The two-dimensional Cartesian coordinates of the starting point in the two-dimensional terrain environment are (0, 0), the coordinates of the target point are (110, 100), and the settings of various obstacles are shown in Table 1:

[0106] Table 1 Threat source settings of 2D environment simulation experiment

[0107]

[0108] The comparison of the optimal route length, route cost fitness function value and algorithm execution time of BA, DEBA and CPFIBA in the two-dimensional environment (30 iterations of a single experiment, taking the average value of ten independent experiments) is shown in Table 2:

[0109] Table 2 Comparison of various performance indicators of the improved algorithm in two-dimensional environment

[0110]

[01...

Embodiment 2

[0114] In the three-dimensional environment simulation experiment, the coordinates of the starting point are (0, 0, 100), and the coordinates of the target point are (100, 100, 100). Introduce the concept of flight safety circle, that is, the flying height of the UAV cannot be higher than the horizontal height of the flight safety circle. Here, the safety circle is set to be parallel to the surface and z=600m. Using the digital elevation map to set the panoramic and side views of the terrain model of the 3D UAV flight simulation environment, such as Figure 10 shown.

[0115] The parameter settings of BA, DEBA and CPFIBA are consistent with the two-dimensional experiment. Three algorithms are used to plan the route in the three-dimensional terrain space, and the route planning simulation results are as follows: Figure 11 As shown, the route cost fitness function convergence curve is as follows Figure 12 shown.

[0116] The comparison of the optimal path length, route cos...

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Abstract

The invention provides an unmanned aerial vehicle route planning method based on an improved bat algorithm. According to the method, the optimization success rate is introduced to change the speed updating mode of the individual bats based on the conventional bat algorithm; meanwhile, the chaotic method is applied to initialize the distribution of the individual bats in the search space and the concept of the artificial potential field is utilized to simulate the gravitational field of the ending point and the repulsive field of the starting point and the obstacle so as to accelerate the speedof the individual bats to the optimal solution; and finally the improved bat algorithm based on the chaotic artificial potential field is proposed. Compared with the conventional bat algorithm, the track length is shortened for 36.56%, the planning time is shortened for 56.05% and the obstacle avoidance fitness value is reduced for 49.53% by the method; and compared with the differential evolutionary bat algorithm, the track length is shortened for 27.16%, the planning time is shortened for 27.30% and the obstacle avoidance fitness value is reduced for 42.46% by the method in the unmanned aerial vehicle route planning task so that the method is a route planning algorithm with practical significance.

Description

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Claims

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Application Information

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Owner SHENYANG AEROSPACE UNIVERSITY
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