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On Sharing Spatial Data with Uncertainty Integration Amongst Multiple Robots Having Different Maps

School of Regional Innovation and Social Design Engineering, Faculty of Engineering, Kitami Institute of Technology, Kitami, Hokkaido 090-8507, Japan
Division of Human Mechanical Systems and Design, Faculty of Engineering, Hokkaido University, Sapporo, Hokkaido 060-8628, Japan
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Appl. Sci. 2019, 9(13), 2753;
Received: 30 May 2019 / Revised: 3 July 2019 / Accepted: 4 July 2019 / Published: 8 July 2019
(This article belongs to the Special Issue Multi-Robot Systems: Challenges, Trends and Applications)
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Information sharing is a powerful feature of multi-robot systems. Sharing information precisely and accurately is important and has many benefits. Particularly, smart information sharing can improve robot path planning. If a robot finds a new obstacle or blocked path, it can share this information with other remote robots allowing them to plan better paths. However, there are two problems with such information sharing. First, the maps of the robots may be different in nature (e.g., 2D grid-map, 3D semantic map, feature map etc.) as the sensors used by the robots for mapping and localization may be different. Even the maps generated using the same sensor (e.g., Lidar) can vary in scale or rotation and the sensors used might have different specifications like resolution or range. In such scenarios, the ‘correspondence problem’ in different maps is a critical bottleneck in information sharing. Second, the transience of the obstacles has to be considered while also considering the positional uncertainty of the new obstacles while sharing information. In our previous work, we proposed a ‘node-map’ with a confidence decay mechanism to solve this problem. However, the previous work had many limitations due to the decoupling of new obstacle’s positional uncertainty and confidence decay. Moreover, the previous work applied only to homogeneous maps. In addition, the previous model worked only with static obstacles in the environment. The current work extends our previous work in three main ways: (1) we extend the previous work by integrating positional uncertainty in the confidence decay mechanism and mathematically model the transience of newly added or removed obstacles and discuss its merits; (2) we extend the previous work by considering information sharing in heterogeneous maps build using different sensors; and (3) we consider dynamic obstacles like moving people in the environment and test the proposed method in complex scenarios. All the experiments are performed in real environments and with actual robots and results are discussed. View Full-Text
Keywords: information sharing; multi-robot systems; positional uncertainty; path planning; mapping information sharing; multi-robot systems; positional uncertainty; path planning; mapping

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Ravankar, A.; Ravankar, A.A.; Hoshino, Y.; Kobayashi, Y. On Sharing Spatial Data with Uncertainty Integration Amongst Multiple Robots Having Different Maps. Appl. Sci. 2019, 9, 2753.

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