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Master-followed Multiple Robots Cooperation SLAM Adapted to Search and Rescue Environment

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  • Control Theory and Applications
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Abstract

The master-followed multiple robots interactive cooperation simultaneous localization and mapping (SLAM) schemes were designed in this paper, which adapts to search and rescue (SAR) cluttered environments. In our multi-robots SLAM, the proposed algorithm estimates each of multiple robots’ current local sub-map, in this occasion, a particle represents each of moving multi-robots, and simultaneously, also represents the pose of a motion robot. The trajectory of the robot’s movement generated a local sub-map; the sub-maps can be looked on as the particles. Each robot efficiently forms a local sub-map; the global map integrates over these local sub-maps; identifying SAR objects of interest, in which, each of multi-robots acts as local-level features collector. Once the object of interest (OOI) is detected, the location in the global map could be determined by the SLAM. The designed multi-robot SLAM architecture consists of PC remote control center, a master robot, and multi-followed robots. Through mobileRobot platform, the master robot controls multi-robots team, the multiple robots exchange information with each other, and then performs SLAM tasks; the PC remote control center can monitor multi-robot SLAM process and provide directly control for multi-robots, which guarantee robots conducting safety in harsh SAR environments. This SLAM method has significantly improved the objects identification, area coverage rate and loop-closure, and the corresponding simulations and experiments validate the significant effects.

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Author information

Authors and Affiliations

  1. School of Control Science and Engineering, Shandong University, Jinan, 250061, China

    Hongling Wang, Chengjin Zhang & Bao Pang

  2. School of Mechanical, Electrical and Information Engineering, Shandong University at Weihai, Weihai, 264209, China

    Chengjin Zhang & Yong Song

Authors
  1. Hongling Wang
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  4. Bao Pang
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Corresponding author

Correspondence to Chengjin Zhang.

Additional information

Recommended by Associate Editor DaeEun Kim under the direction of Editor Euntai Kim. This work was supported by the NSFC (National Nature Science Foundation of China) under grant no. 61573213, 61473174, 61473179, by the Natural Science Foundation of Shandong Province under grant no. ZR2015PF009, ZR2014FM007, by Shandong Province Science and Technology Development Program under grant no. 2014GGX103038, and Special Technological Program of Transformation of Initiatively Innovative Achievements in Shandong Province under grant no. 2014ZZCX04302.

Hongling Wang received a bachelor’s degree of engineering and an M.S. degree from Shandong Jianzhu University, respectively. She is currently working toward a Ph.D. in Shandong University. Her current research interests include search and rescue robotics, intelligent robot control, and simultaneous localization and mapping.

Chengjin Zhang received his M.S. degree from Shandong University of Science and Technology in 1992, and a Ph.D. from Northeastern University in 1997. He is currently a professor in Shandong University. His current research interests include control theory and applications, intelligent robot control, and bioinformatics.

Yong Song received his Ph.D. in Pattern Recognition and Intelligent Systems from Shandong University in 2012. He is currently a professor in Shandong University. His current research interests include mobile robot navigation, machine learning, neural networks, control of intelligent robots, and swarm intelligence robotics.

Bao Pang received his Bachelor’s degree in science from Changzhi University, and an MS from University of Science and Technology Liaoning. He is currently working toward a Ph.D. in Shandong University. His current research interests include swarm intelligence robotics, intelligent control, and mobile robot navigation.

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Wang, H., Zhang, C., Song, Y. et al. Master-followed Multiple Robots Cooperation SLAM Adapted to Search and Rescue Environment. Int. J. Control Autom. Syst. 16, 2593–2608 (2018). https://doi.org/10.1007/s12555-017-0227-7

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  • DOI: https://doi.org/10.1007/s12555-017-0227-7

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