基于数字信息素和领航算法的未知环境多智能体目标探测

邵燕; 赵志峰; 李荣鹏; 周裕庚

doi:10.1631/FITEE.1900659

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基于数字信息素和领航算法的未知环境多智能体目标探测

智能无人系统专辑 | Updated：2022-05-19

- 基于数字信息素和领航算法的未知环境多智能体目标探测
  Enhanced Publication
- Target detection for multi-UAVs via digital pheromones and navigation algorithm in unknown environments
- 信息与电子工程前沿（英文） 2020年21卷第5期页码：796-808
- Affiliations：
  
  College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China
  Zhejiang Lab, Hangzhou 311121, China
  Institute of Wanfeng Jinyuan Holding Group Co., Ltd., Shaoxing 312000, China
- Author bio：
  
  [ "Yan SHAO, E-mail: shaoy@zju.edu.cn" ]
  Zhi-feng ZHAO, E-mail:zhaozf@zhejianglab.com
  [ "Rong-peng LI, E-mail: lirongpeng@zju.edu.cn" ]
  [ "Yu-geng ZHOU, E-mail: yugeng.zhou@wfjyjt.com" ]
- Funds：
  
  Project supported by the National Key R & D Program of China (No. 2017YFB1301003), the National Natural Science Foundation of China (Nos. 61701439 and 61731002), the Zhejiang Key Research and Development Plan (Nos. 2019C01002 and 2019C03131), the Project sponsored by Zhejiang Lab (No. 2019LC0AB01), and the Zhejiang Provincial Natural Science Foundation of China (No. LY20F010016)
- DOI：10.1631/FITEE.1900659
  中图分类号： TP11
- 纸质出版日期：2020-05，
  
  收稿日期：2019-11-30，
  
  修回日期：2020-03-31，
- Accepted：
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邵燕, 赵志峰, 李荣鹏, 等. 基于数字信息素和领航算法的未知环境多智能体目标探测[J]. 信息与电子工程前沿（英文）, 2020,21(5):796-808.

SHAO YAN, ZHAO ZHI-FENG, LI RONG-PENG, et al. Target detection for multi-UAVs via digital pheromones and navigation algorithm in unknown environments. [J]. Frontiers of information technology & electronic engineering, 2020, 21(5): 796-808.
邵燕, 赵志峰, 李荣鹏, 等. 基于数字信息素和领航算法的未知环境多智能体目标探测[J]. 信息与电子工程前沿（英文）, 2020,21(5):796-808. DOI： 10.1631/FITEE.1900659.

SHAO YAN, ZHAO ZHI-FENG, LI RONG-PENG, et al. Target detection for multi-UAVs via digital pheromones and navigation algorithm in unknown environments. [J]. Frontiers of information technology & electronic engineering, 2020, 21(5): 796-808. DOI： 10.1631/FITEE.1900659.

摘要

在复杂且动态性强的环境中，指导多无人机系统协调运作是一项具有挑战性的技术。基于数字信息素和当前主流无人系统控制算法，提出一种有限先验知识下多无人机系统目标探测分布式算法。通过改进不同语义数字信息素的融合算法和个体行为决策方案，提出一种更合理、有效的信息素更新机制。同时，考虑到一些个体在感知和交流方面的局限性，以及受自然界蜂拥算法启发，在Olfati-Saber无人机群控制算法基础上，设计了新的领航算法模型。此外，使用矢量信息代替传统标量信息素，使无人机群具有更高探测效率。仿真结果表明，该算法在指定区域的探测覆盖率、目标获取及回访效率、避障能力等方面都有较好表现。

Abstract

Coordinating multiple unmanned aerial vehicles (multi-UAVs) is a challenging technique in highly dynamic and sophisticated environments. Based on digital pheromones as well as current mainstream unmanned system controlling algorithms

we propose a strategy for multi-UAVs to acquire targets with limited prior knowledge. In particular

we put forward a more reasonable and effective pheromone update mechanism

by improving digital pheromone fusion algorithms for different semantic pheromones and planning individuals' probabilistic behavioral decision-making schemes. Also

inspired by the flocking model in nature

considering the limitations of some individuals in perception and communication

we design a navigation algorithm model on top of Olfati-Saber's algorithm for flocking control

by further replacing the pheromone scalar to a vector. Simulation results show that the proposed algorithm can yield superior performance in terms of coverage

detection and revisit efficiency

and the capability of obstacle avoidance.

关键词

群体智能数字信息素人工势场领航算法

Keywords

Collective intelligenceDigital pheromonesArtificial potential fieldNavigation algorithm

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