Conclusions and Discussions, Acknowledgment and Referencesby@heuristicsearch

Conclusions and Discussions, Acknowledgment and References

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Semantic SLAM involves the extraction and integration of semantic understanding with geometric data to produce detailed, multi-layered maps.
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This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license.


(1) Akash Chikhalikar, Graduate School of Engineering, Department of Robotics, Tohoku University;

(2) Ankit A. Ravankar, Graduate School of Engineering, Department of Robotics, Tohoku University;

(3) Jose Victorio Salazar Luces, Graduate School of Engineering, Department of Robotics, Tohoku University;

(4) Yasuhisa Hirata, Graduate School of Engineering, Department of Robotics, Tohoku University.


In this study, we have successfully demonstrated the feasibility of target search in indoor environments by effectively locating multiple targets using a novel heuristic. Our proposed approach offers a reliable region-to-region navigation strategy that can accommodate user preferences during the search. The region-to-region navigation is efficient in terms of time as well as energy. It is inherently more robust to obstacles and occlusions as compared to point-topoint navigation. Furthermore, our system can perform these tasks in real-time and is suitable for small to medium sized indoor spaces such as homes and offices.

The system can be improved using decision making strategies that optimize long horizon navigation planners. Another non-trivial extension of this work is to add a manipulator to the system that can perform high-level object pickup tasks and extend the current system to accommodate more complex and challenging environments.


This work was partially supported by JST Moonshot R&D [Grant Number JPMJMS2034], JSPS Kakenhi [Grant Number JP21K14115], and JST SPRING [Grant Number JPMJSP2114].


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