从规则驱动到群智涌现: 多机器人空地协同研究综述

郝肇铁; 郭斌; 赵凯星; 吴磊; 丁亚三; 李哲涛; 刘思聪; 於志文

doi:10.16383/j.aas.c230445

从规则驱动到群智涌现: 多机器人空地协同研究综述

doi: 10.16383/j.aas.c230445

郝肇铁^1,,
郭斌^1,,
赵凯星^2,,
吴磊^1,,
丁亚三^1,,
李哲涛^3,,
刘思聪^1,,
於志文^1,

1.
西北工业大学计算机学院西安 710129
2.
西北工业大学软件学院西安 710129
3.
暨南大学信息科学技术学院广州 510632

基金项目: 国家杰出青年科学基金(62025205), 国家自然科学基金(62032020, 62102317)资助

详细信息

作者简介:
郝肇铁：西北工业大学计算机学院硕士研究生. 2022年获得西北工业大学学士学位. 主要研究方向为空地协同, 深度强化学习. E-mail: haozhaotie@mail.nwpu.edu.cn

郭斌：西北工业大学计算机学院教授. 2009年获得庆应义塾大学博士学位. 主要研究方向为普适计算, 群体智能和移动群智感知. 本文通信作者. E-mail: guob@nwpu.edu.cn

赵凯星：西北工业大学软件学院助理教授. 2021年获得图卢兹大学博士学位. 主要研究方向为普适计算, 人机交互. E-mail: kaixing.zhao@nwpu.edu.cn

吴磊：西北工业大学计算机学院博士研究生. 2021年获得西北工业大学学士学位. 主要研究方向为模块化机器人, 深度强化学习. E-mail: leiwu@mail.nwpu.edu.cn

丁亚三：西北工业大学计算机学院博士研究生. 2018年获得西北工业大学学士学位. 主要研究方向为普适计算. E-mail: yasanding@mail.nwpu.edu.cn

李哲涛：暨南大学信息科学技术学院教授. 2010年获得湖南大学博士学位. 主要研究方向为物联网, 人工智能. E-mail: liztchina@hotmail.com

刘思聪：西北工业大学计算机学院副教授. 2020年获得西安电子科技大学博士学位. 主要研究方向为智能物联网. E-mail: scliu@nwpu.edu.cn

於志文：西北工业大学计算机学院教授. 2005年获得西北工业大学博士学位. 主要研究方向为智能物联网, 普适计算. E-mail: zhiwenyu@nwpu.edu.cn

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出版历程
- 收稿日期: 2023-07-20
- 录用日期: 2024-02-07
- 网络出版日期: 2024-09-06
- 刊出日期: 2024-10-21

From Rule-driven to Collective Intelligence Emergence: A Review of Research on Multi-robot Air-ground Collaboration

1.
School of Computer Science, Northwestern Polytechnical University, Xi＇an 710129
2.
School of Software, Northwestern Polytechnical University, Xi＇an 710129
3.
College of Information Science and Technology, Jinan University, Guangzhou 510632

Funds: Supported by the National Science Fund for Distinguished Young Scholars (62025205) and National Natural Science Foundation of China (62032020, 62102317)

More Information

Author Bio:
HAO Zhao-Tie　Master student at the School of Computer Science, Northwestern Polytechnical University. He received his bachelor degree from Northwestern Polytechnical University in 2022. His research interest covers air-ground collaboration and deep reinforcement learning

GUO Bin　Professor at the School of Computer Science, Northwestern Polytechnical University. He received his Ph.D. degree from Keio University in 2009. His research interest covers ubiquitous computing, crowd intelligence, and mobile crowd sensing. Corresponding author of this paper

ZHAO Kai-Xing　Assistant professor at the School of Software, Northwestern Polytechnical University. He received his Ph.D. degree from University of Toulouse in 2021. His research interest covers ubiquitous computing and human machine interaction

WU Lei　Ph.D. candidate at the School of Computer Science, Northwestern Polytechnical University. He received his bachelor degree from Northwestern Polytechnical University in 2021. His research interest covers modular robots and deep reinforcement learning

DING Ya-San　Ph.D. candidate at the School of Computer Science, Northwestern Polytechnical University. He received his bachelor degree from Northwestern Polytechnical University in 2018. His main research interest is ubiquitous computing

LI Zhe-Tao　Professor at the College of Information Science and Technology, Jinan University. He received his Ph.D. degree from Hunan University in 2010. His research interest covers Internet of Things and artificial intelligence

LIU Si-Cong　Associate professor at the School of Computer Science, Northwestern Polytechnical University. She received her Ph.D. degree from Xidian University in 2020. Her main research interest is intelligent Internet of Things

YU Zhi-Wen　Professor at the School of Computer Science, Northwestern Polytechnical University. He received his Ph.D. degree from Northwestern Polytechnical University in 2005. His research interest covers intelligent Internet of Things and ubiquitous computing

摘要

摘要: 多机器人空地协同系统作为一种在搜索救援、自主探索等领域具有广泛应用前景的异构机器人协作系统, 近年来受到研究者的高度关注. 针对限制空地协同系统自治性能的低智能性、弱自主性挑战, 如何增强个体智能、提高群体协同自主性是加快空地系统应用落地亟需解决的关键问题. 近年来, 随着以深度学习、群体智能为代表的人工智能(Artificial intelligence, AI)算法在感知、决策等领域的不断发展, 将其应用于空地协同系统成为了当前的研究热点. 基于空地协同的自主化程度, 总结从规则驱动到群智涌现不同协作水平下的空地协同工作, 强调通过增强个体智能涌现群体智慧. 同时, 构建并拓宽空地协同群智系统的概念及要素, 阐述其自组织、自适应、自学习与持续演化的群智特性. 最后, 通过列举空地协同代表性应用场景, 总结空地协同所面临的挑战, 并展望未来方向.
- 空地协同 /
- 群智涌现 /
- 人工智能 /
- 自主性
Abstract: The multi-robot air-ground collaboration system, which is crucial for search and rescue, exploration, and other fields, has garnered significant attention from researchers in recent years. Overcoming challenges related to limited intelligence and weak autonomy in such systems is essential to enhance individual intelligence and strengthen collective collaboration autonomy, thereby accelerating their practical applications. In recent years, with the continuous advancement of artificial intelligence (AI) algorithms in perception and decision-making, such as deep learning and collective intelligence, their applications to air-ground collaborative systems have become a research hotspot. Based on the level of autonomy in air-ground collaboration, this paper summarizes air-ground collaboration efforts at different collaboration levels, ranging from rule-driven approaches to collective intelligence emergence, emphasizing the enhancement of individual intelligence to achieve collective intelligence. Furthermore, this paper constructs the concepts and expands the features of the air-ground collaboration collective intelligence system, and outlines its self-organizing, self-adaptation, self-learning, and continuously evolving qualities. Finally, by listing representative application scenarios, this paper encapsulates the challenges and explores future directions in air-ground collaboration.
- Air-ground collaboration /
- collective intelligence emergence /
- artificial intelligence (AI) /
- autonomy
注释:

1) 1¹ 《中华人民共和国民用航空法》第七章第二节第七十五条规定

HTML全文

图 1 全文组织结构图

Fig. 1 Organization chart for chapters

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图 2 空地协同分类

Fig. 2 Air-ground collaboration classification

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图 3 空地协同群智系统

Fig. 3 Air-ground collaboration collective intelligence system

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图 4 空地协同流程

Fig. 4 Air-ground collaboration workflow

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图 5 空地机器人无线通信((a) 集中式; (b) 分布式; (c) 移动自组织网络)

Fig. 5 Wireless communications of air-ground robots ((a) Centralized; (b) Distributed; (c) Mobile ad-hoc network)

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图 6 空地协同群智系统自主化等级^{[80, 110, 113, 116]}

Fig. 6 Autonomy level of air-ground collaboration collective intelligence system^{[80, 110, 113, 116]}

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图 7 生物群智迁移映射空地协同群智系统

Fig. 7 Biological collective intelligence transfer and mapping air-ground collaboration collective intelligence system

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图 8 空地协同群智涌现基本特征^{[112, 135−137]}

Fig. 8 Basic characteristics of the emergence of air-ground collaboration collective intelligence^{[112, 135−137]}

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图 9 空地协同应用^{[9, 13, 38, 51, 112, 149]}

Fig. 9 Applications of air-ground collaboration^{[9, 13, 38, 51, 112, 149]}

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表 1 通信方式分析

Table 1 Analysis of communication methods

通信方式	设备	常见任务	常见环境	通信范围	特点	研究代表
有线通信	光缆	搜索救援	范围有限的室外区域	取决于光缆长度	准确性高, 不易出错, 但会影响空地主体机动性	[9, 48]
集中式	ZigBee WiFi节点	数据收集	室内, 范围有限的室外区域	0 ~ 100 m	依赖于中心节点, 准确性高, 但通信效率低	[49−51]
分布式	WiFi节点	大规模建图	室外	与空地机器人数目成正比	不依赖中心节点, 鲁棒性强, 但通信范围有限	[15, 53]
移动自组织网络	IEEE 802.11中继器 LoRA节点	搜索救援	隧道等通信设施缺乏的场景	可随节点个数增加不断扩大	不依赖中心节点, 抗干扰能力强, 通信范围广, 灵活部署	[9, 30, 42, 54]

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表 2 空地协同中的语义分割算法应用总结

Table 2 Application summary of semantic segmentation algorithm in air-ground collaboration

方法分类	文献	方法	分割对象	分割结果	实时性(ms)	任务
传统算法	[61]	HSV分类器	RGB图像	沥青、草地、障碍物与未知区域	730	地形分类
传统算法	[62]	Chow-Liu树点云聚类	RGB图像、点云	马路、墙壁、楼梯等	1770	确定可通行区域导航
深度学习类算法	[15]	ErfNet	RGB图像	马路、建筑、车辆、草地等	12	地形分类
	[13]	FCN	RGB全景图	全景图背景	330	相对定位
	[51]	LNSNet	RGB图像	可通行区域	55	确定可通行区域导航
	[53]	Deeplab	RGB图像、点云	道路、行人、树木、建筑物	46	理解环境

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表 3 基于深度学习的目标检测算法总结

Table 3 Summary of object detection algorithms based on deep learning

方法	按阶段划分	准确性(mAP)	推理速度(FPS)	空地协同研究代表
YOLOv3	单阶段	63.4	45.0	[9, 48, 74−75]
MobileNet-SSD	单阶段	77.2	46.0	[54, 70]
DeNet	单阶段	77.0	34.0	[76]
R-CNN	两阶段	58.5	0.03	—
Fast R-CNN	两阶段	70.0	0.50	—

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表 4 空地任务中常见地图总结

Table 4 Summary of common maps in air-ground missions

地图类型	表示空间	定义	特点	代表工作
拓扑地图	2D	节点表示位置, 边表示边界或可通过性	简单有效, 无法反映地图细节	[62]
2D栅格地图	2D	离散的网格单元中包含其覆盖信息	简洁、易于存储, 无法反映地图细节	[66]
高程图	2.5D	基于离散位置保存高度值	可表示地形起伏, 忽略细节	[9, 48]
3D点云地图	3D	点云表示的空间	点云信息无序, 占用资源较大	[39, 62]
体素地图	3D	将空间体积划分为体素单元	快速读取信息, 消耗内存资源大	[38, 80]
八叉树地图	3D	基于八叉树存储体素空间	内存占用小, 可实时更新	[81]

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表 5 空地协同等级分类方法总结

Table 5 Summary of classification methods for air-ground collaboration level

等级分类	代表研究	任务	方法	决策拓扑	实验环境
非自主等级协同	[9]	探索地下隧道	基于图的路径规划器, 地图融合算法	分布式	Sim2Real
非自主等级协同	[80]	协作攀爬	未知环境可穿越性地形判断	集中式	Real
弱自主等级协同	[59]	探索地下隧道	BPMN表示法, 有限状态机	集中式	Real
	[112]	区域搜索	神经进化算法	分布式	Sim
	[108]	海上平台作业	多角色目标分配	集中式	Sim
	[110]	野外建图	规约语言, 确定性有限状态机	集中式	Sim
	[111]	目标跟踪	多智能体强化学习	分布式	Sim
强自主等级协同	[113]	提供通信计算服务	目标层次分解	分布式	Sim
	[114]	协同作战	PDDL模型, 基于图的任务分解	集中式	Sim
	[115]	提供通信计算服务	Lyapunov优化法	集中式	Sim

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表 6 空地模拟器总结

Table 6 Summary of air-ground simulator

仿真环境	物理引擎	是否开源	特点	不足
Gazebo	支持ODE、Bullet、Simbody和DART	是	ROS集成使用, 支持多种插件	视觉渲染效果差
MORSE	BGE	是	分布计算, 自由度可控	同步性差, 无法精确动力学建模
Pybullet	Bullet	是	跨平台, 操作简单	运行效率慢
CoppeliaSim	支持ODE、Bullet和Vortex	是	分布式, 支持ROS接口, 支持多语言编程	视觉渲染效果差, 运行效率慢
AirSim	UE4	是	跨平台, 视觉逼真	动力学仿真效果差, 物理接口不足
Collaborative robots Sim	ODE	否	基于Gazebo强物理交互	视觉渲染效果差, 运行速率慢
Gibson Env	神经网络	是	融入真实数据, 逼真的渲染效果	需要采集大量真实数据

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