Cooperative obstacle avoidance algorithm based on improved artificial potential field and consensus protocol

doi:10.11772/j.issn.1001-9081.2022070967

Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (8): 2644-2650.DOI: 10.11772/j.issn.1001-9081.2022070967

Special Issue: 前沿与综合应用

• Frontier and comprehensive applications • Previous Articles

Cooperative obstacle avoidance algorithm based on improved artificial potential field and consensus protocol

Zhongyuan ZHANG¹, Wei DAI¹, Guangyu LI¹, Xiaoqing CHEN², Qibo DENG¹()

^1.School of Aircraft Engineering，Nanchang Hangkong University，Nanchang Jiangxi 330063，China
^2.National Innovation Institute of Defense Technology，Academy of Military Sciences，Beijing 100850，China

Received:2022-07-05 Revised:2022-09-19 Accepted:2022-09-20 Online:2023-01-15 Published:2023-08-10
Contact: Qibo DENG
About author:ZHANG Zhongyuan， born in 1998， M. S. candidate. His research interests include Unmanned Aerial Vehicle （UAV） swarm control.
DAI Wei， born in 1993， M. S. candidate. His research interests include space fluid management.
LI Guangyu， born in 1988， Ph. D.， lecturer. His research interests include multi-aircraft collaborative autonomous control and intelligent decision-making.
CHEN Xiaoqing， born in 1982， Ph. D.， senior engineer. His research interests include hypersonic vehicle aerodynamics， thermal calculation.
Supported by:
National Natural Science Foundation of China(52065044);Nanchang Hangkong University Graduate Innovation Special Fund(YC2021054)

基于改进人工势场和一致性协议的协同避障算法

张钟元¹, 戴炜¹, 李光昱¹, 陈小庆², 邓启波¹()

^1.南昌航空大学飞行器工程学院，南昌 330063
^2.中国人民解放军军事科学院国防科技创新研究院，北京 100850

通讯作者: 邓启波
作者简介:张钟元（1998—），男（苗族），湖南花垣人，硕士研究生，主要研究方向：无人机（UAV）集群控制
戴炜（1993—），男，江西上饶人，硕士研究生，主要研究方向：空间流体管理
李光昱（1988—），男，江西赣州人，讲师，博士，主要研究方向：多飞行器协同自主控制与智能决策
陈小庆（1982—），男，江苏泰州人，高级工程师，博士，主要研究方向：高超声速飞行器气动力、热计算；
基金资助:
国家自然科学基金资助项目(52065044);南昌航空大学研究生创新专项(YC2021054)

Abstract

Abstract:

Cooperative obstacle avoidance is one of the key technologies of Unmanned Aerial Vehicle （UAV） system. While there exist problems of formation loss， mission failure， and increasing energy consumption during the obstacle avoidance of UAV swarm. For solving these problems， a cooperative obstacle avoidance algorithm based on improved artificial potential field and consensus protocol was proposed. First， according to the control law of multi-rotor UAVs， a control protocol to keep speed and position consistency was designed， and the artificial potential field force was scaled and transformed by normalization and high-order exponents， thereby solving the problem of oscillation failure caused by the excessive variation of the potential field force. Then， the artificial potential field force was introduced to modify the expectation formation of consensus protocol for solving the control conflict problem of the combination algorithm of artificial potential field method and consensus protocol. The proposed algorithm was simulated and compared with the formation division obstacle avoidance algorithm and dynamic window obstacle avoidance algorithm in complex obstacle environment. The results show that the proposed algorithm has the average formation loss degree reduced by 82.60% and 64.38% respectively， the average failure degree of task decreased by 98.66% and 86.01% respectively， and the total length of flight path reduced by 9.95% and 17.63% respectively. It can be seen that the proposed algorithm is suitable for the complex flight environment with multiple obstacles.

Key words: Unmanned Aerial Vehicle (UAV), formation stability, artificial potential field method, consensus protocol, complex obstacle environment

摘要：

协同避障是无人机（UAV）系统的关键技术之一，而UAV集群避障期间存在队形丢失、任务失效和能源消耗增加等问题。为解决这些问题，提出了一种基于改进人工势场和一致性协议的协同避障算法。首先，根据多旋翼UAV的控制律来设计保持速度、位置一致的控制协议，并采用归一化和高阶指数缩放变换人工势场力，从而解决势场力变化幅度过大导致的振荡失效问题；然后，引入人工势场力调整一致性协议期望编队，从而解决人工势场法与一致性协议组合算法的控制冲突问题。在复杂障碍环境下，所提算法与编队划分避障算法、动态窗口避障算法进行对比仿真的结果表明，所提算法的队形平均损失程度分别下降82.60%、64.38%，任务平均失效程度分别下降98.66%、86.01%，飞行路径总长度分别下降9.95%、17.63%。可见，所提算法适用于多障碍复杂飞行环境。

关键词: 无人机, 队形稳定性, 人工势场法, 一致性协议, 复杂障碍环境

CLC Number:

V279

Zhongyuan ZHANG, Wei DAI, Guangyu LI, Xiaoqing CHEN, Qibo DENG. Cooperative obstacle avoidance algorithm based on improved artificial potential field and consensus protocol[J]. Journal of Computer Applications, 2023, 43(8): 2644-2650.

张钟元, 戴炜, 李光昱, 陈小庆, 邓启波. 基于改进人工势场和一致性协议的协同避障算法[J]. 《计算机应用》唯一官方网站, 2023, 43(8): 2644-2650.

Figures/Tables 11

Fig. 1 Flow of formation division obstacle avoidance algorithm

Fig. 2 Schematic graph of UAV obstacle avoidance oscillation

Fig. 3 Schematic graph of UAV flight stability loss

Fig. 4 Schematic graph of UAV obstacle avoidance failure

Fig. 5 Flow of collaborative obstacle avoidance algorithm

Fig. 6 Experimental design of UAV swarm cooperative obstacle avoidance

Tab. 1 Simulation parameters

参数	数值	参数	数值
$ρ o b s$	12 m	$ρ 0$	10
$k m$	15	$k n$	4
$K 1$	［-2.00，-2.00］	$K 2$	［0.047，0.067］
$ξ$	0.01	$η$	3 000

Tab. 1 Simulation parameters

参数	数值	参数	数值
$ρ o b s$	12 m	$ρ 0$	10
$k m$	15	$k n$	4
$K 1$	［-2.00，-2.00］	$K 2$	［0.047，0.067］
$ξ$	0.01	$η$	3 000

Fig. 7 Simulation experimental results of different formation obstacle avoidance algorithms

Fig. 8 Analysis results of formation integrity

Fig. 9 Analysis results of mission failure degree

Tab. 2 Comprehensive performance analysis results of different algorithms

算法	曲线			直线			综合
算法	队形平均损失	任务平均失效	无人机总路径/m	队形平均损失	任务平均失效	无人机总路径/m	队形平均损失	任务平均失效	无人机总路径/m
文献［11］算法	0.219 3	0.207 9	649.173 3	0.085 2	0.089 4	401.246 1	0.152 3	0.148 7	525.209 7
文献［18］算法	0.142 4	0.025 9	757.108 3	0.006 4	0.002 8	391.229 0	0.074 4	0.014 3	574.168 7
本文算法	0.048 3	0.001 8	570.636 6	0.004 8	0.002 1	375.226 6	0.026 5	0.002 0	472.931 6

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Cooperative obstacle avoidance algorithm based on improved artificial potential field and consensus protocol

基于改进人工势场和一致性协议的协同避障算法

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