Optimal path-planning for mobile robots to find a hidden target in an unknown environment based on machine learning View Full Text


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Article Info

DATE

2019-05

AUTHORS

S. M. Sombolestan, A. Rasooli, S. Khodaygan

ABSTRACT

Using mobile robots in disaster areas can reduce risks and the search time in urban search and rescue operations. Optimal path-planning for mobile robotics can play a key role in the reduction of the search time for rescuing victims. In order to minimize the search time, the shortest path to the target should be determined. In this paper, a new integrated Reinforcement Learning—based method is proposed to search and find a hidden target in an unknown environment in the minimum time. The proposed algorithm is developed in two main phases. Depending on whether or not the mobile robot receives the signal from the hidden target, phases I or II of the proposed algorithm can be carried out. Then, the proposed algorithm is implemented on an e-puck robot in an urban environment which is simulated within Webots software. Finally, to demonstrate the efficiency of the proposed method and to verify it, the computational results from the proposed method are compared with three conventional methods from the literature. More... »

PAGES

1841-1850

References to SciGraph publications

  • 2010. Dynamic Obstacle Avoidance for an Omnidirectional Mobile Robot in JOURNAL OF ROBOTICS
  • 2018-11. Multi-criteria shortest path for rough graph in JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING
  • 2015-12. Parallel and distributed computing for UAVs trajectory planning in JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING
  • 2017-10-25. Secure system based on UAV and BLE for improving SAR missions in JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING
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    http://scigraph.springernature.com/pub.10.1007/s12652-018-0777-4

    DOI

    http://dx.doi.org/10.1007/s12652-018-0777-4

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