Information Rich Voxel Grid for Use in Heterogeneous Multi-Agent Robotics

被引:6
作者
Balding, Steven [1 ]
Gning, Amadou [1 ]
Cheng, Yongqiang [1 ]
Iqbal, Jamshed [1 ]
机构
[1] Univ Hull, Fac Sci & Engn, Sch Comp Sci, Kingston Upon Hull HU6 7RX, England
来源
APPLIED SCIENCES-BASEL | 2023年 / 13卷 / 08期
关键词
SLAM; robotic colony; robotic communication; decentralised robotic communication; heterogeneous robotic vision; voxel grid; environment mapping; SIMULTANEOUS LOCALIZATION; SLAM; TRACKING;
D O I
10.3390/app13085065
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Robotic agents are now ubiquitous in both home and work environments; moreover, the degree of task complexity they can undertake is also increasing exponentially. Now that advanced robotic agents are commonplace, the question for utilisation becomes how to enable collaboration of these agents, and indeed, many have considered this over the last decade. If we can leverage the heterogeneous capabilities of multiple agents, not only can we achieve more complex tasks, but we can better position the agents in more chaotic environments and compensate for lacking systems in less sophisticated agents. Environments such as search and rescue, agriculture, autonomous vehicles, and robotic maintenance are just a few examples of complex domains that can leverage collaborative robotics. If the use of a robotic agent is fruitful, the question should be: How can we provide a world state and environment mapping, combined with a communication method, that will allow these robotic agents to freely communicate? Moreover, how can this be decentralised such that agents can be introduced to new and existing environments already understood by other agents? The key problem that is faced is the communication method; however, when looking deeper we also need to consider how the change of an environment is mapped while considering that there are multiple differing sensors. To this end, we present the voxel grid approach for use in a decentralised robotic colony. To validate this, results are presented to show how the single-agent and multiagent systems compare.
引用
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页数:24
相关论文
共 28 条
[11]   Collaborative 3D Scene Reconstruction in Large Outdoor Environments Using a Fleet of Mobile Ground Robots [J].
Lewis, John ;
Lima, Pedro U. U. ;
Basiri, Meysam .
SENSORS, 2023, 23 (01)
[12]   Local control strategies for groups of mobile autonomous agents [J].
Lin, ZY ;
Broucke, M ;
Francis, B .
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2004, 49 (04) :622-629
[13]  
Liu KC, 2022, Arxiv, DOI arXiv:2212.05743
[14]   A Heterogeneous Robotic Swarm for Long-Term Monitoring of Marine Environments [J].
Loncar, Ivan ;
Babic, Anja ;
Arbanas, Barbara ;
Vasiljevic, Goran ;
Petrovic, Tamara ;
Bogdan, Stjepan ;
Miskovic, Nikola .
APPLIED SCIENCES-BASEL, 2019, 9 (07)
[15]   A Fast and Robust Solution for Common Knowledge Formation in Decentralized Swarm Robots [J].
Luo, Jie ;
Shu, Xiao ;
Zhai, Yuanzhao ;
Fu, Xiang ;
Ding, Bo ;
Xu, Jie .
JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS, 2022, 106 (04)
[16]  
Muglikar M, 2020, IEEE INT CONF ROBOT, P4181, DOI [10.1109/icra40945.2020.9197357, 10.1109/ICRA40945.2020.9197357]
[17]   ORB-SLAM: A Versatile and Accurate Monocular SLAM System [J].
Mur-Artal, Raul ;
Montiel, J. M. M. ;
Tardos, Juan D. .
IEEE TRANSACTIONS ON ROBOTICS, 2015, 31 (05) :1147-1163
[18]   MaskFusion: Real-Time Recognition, Tracking and Reconstruction of Multiple Moving Objects [J].
Runz, Martin ;
Buffier, Maud ;
Agapito, Lourdes .
PROCEEDINGS OF THE 2018 IEEE INTERNATIONAL SYMPOSIUM ON MIXED AND AUGMENTED REALITY (ISMAR), 2018, :10-20
[19]   Multiple-Robot Simultaneous Localization and Mapping: A Review [J].
Saeedi, Sajad ;
Trentini, Michael ;
Seto, Mae ;
Li, Howard .
JOURNAL OF FIELD ROBOTICS, 2016, 33 (01) :3-46
[20]   CORB2I-SLAM: An Adaptive Collaborative Visual-Inertial SLAM for Multiple Robots [J].
Saha, Arindam ;
Dhara, Bibhas Chandra ;
Umer, Saiyed ;
AlZubi, Ahmad Ali ;
Alanazi, Jazem Mutared ;
Yurii, Kulakov .
ELECTRONICS, 2022, 11 (18)