Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (9): 2732-2740.doi: 10.13229/j.cnki.jdxbgxb.20211187

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Dynamic obstacle avoidance strategy for flapping⁃wing micro air vehicles

Hao ZHENG1(),Li-jun YU1,Peng-peng ZHI1,2,Zhong-lai WANG1,2()   

  1. 1.School of Mechanical and Electrical Engineering，University of Electronic Science and Technology of China，Chengdu 611731，China
    2.Yangtze Delta Region Institute （Huzhou），University of Electronic Science and Technology of China，Huzhou 313099，China
  • Received:2021-11-14 Online:2023-09-01 Published:2023-10-09
  • Contact: Zhong-lai WANG E-mail:201821040136@std.uestc.edu.cn;wzhonglai@uestc.edu.cn

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Abstract:

Aiming at the dynamic obstacle avoidance problem during the flying process of the Flapping-wing Micro Air Vehicle （FWMAV）， a novel obstacle avoidance scheduling strategy integrating the global path planning and the locally dynamic path planning is proposed in this paper. The static comprehensive cost model is first built by considering both the performance constraints of the FWMAV and its threat constraints during the flight environment. Based on the static cost model， a time-varying collision constraint between the FWMAV and dynamic obstacles is defined and then the dynamic comprehensive cost model for the local obstacle avoidance is established. The improved ant colony algorithm is proposed for the obstacle avoidance scheduling strategy optimization. The results show that the proposed method can effectively handle the dynamic obstacle avoidance scheduling problem of the FWMAV under the known map and improve the dynamic obstacle avoidance scheduling strategy under the dynamic obstacles； meanwhile the improved ant colony algorithm can promote the efficiency of the dynamic path optimization to ensure the real-time requirement of the obstacle avoidance control of the FWMAV.

Key words: flapping-wing micro air vehicle, cost model, obstacle avoidance strategy, dynamic path planning

CLC Number: 

  • V276

Fig.1

Description of the flight trajectory"

Fig.2

Schematic diagram of the trajectories of the FWMAV and the obstacles"

Fig.3

Collision threshold ε selection"

Fig.4

Flow chart of the path planning algorithm for FWMAV"

Table 1

Performance parameters of the FWMAV prototype"

参数取值
翼展/cm26
机身重量/g30
额外负载/g20
飞行速度/（m·s-1）2
最远飞行距离/km2
最小转弯半径/m0.41
飞行高度范围/m［0，50］

Fig.5

Digital map of three-dimensional environment space"

Fig.6

Results of the global obstacle avoidance path planning for FWMAV"

Fig.7

Correlation between the iteration time and the fitness value"

Table 2

Path planning result comparison between the improved ACO and the conventional ACO algorithm"

传统ACO改进ACO算法改进提升
Len/mHgt/mTim/sRate/%Len/mHgt/mTim/sRate/%Len/mTim/sRate/%
162612723.6542.71587856.8061.33916.8518.6

Fig.8

Local dynamic path planning results of FWMAV"

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