Ellis, Kirsty;
Zhang, Henry;
Stoyanov, Danail;
Kanoulas, Dimitrios;
(2022)
Navigation Among Movable Obstacles with Object Localization using Photorealistic Simulation.
In:
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
(pp. pp. 1711-1716).
IEEE: Kyoto, Japan.
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Abstract
While mobile navigation has been focused on obstacle avoidance, Navigation Among Movable Obstacles (NAMO) via interaction with the environment, is a problem that is still open and challenging. This paper, presents a novel system integration to handle NAMO using visual feedback. In order to explore the capabilities of our introduced system, we explore the solution of the problem via graph-based path planning in a photorealistic simulator (NVIDIA Isaac Sim), in order to identify if the simulation-to-reality (sim2real) problem in robot navigation can be resolved. We consider the case where a wheeled robot navigates in a warehouse, in which movable boxes are common obstacles. We enable online real-time object localization and obstacle movability detection, to either avoid objects or, if it is not possible, to clear them out from the robot planned path by using pushing actions. We firstly test the integrated system in photorealistic environments, and we then validate the method on a real-world mobile wheeled robot (UCL MPPL) and its on-board sensory and computing system.
| Type: | Proceedings paper |
|---|---|
| Title: | Navigation Among Movable Obstacles with Object Localization using Photorealistic Simulation |
| Event: | 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) |
| ISBN-13: | 9781665479271 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/IROS47612.2022.9981587 |
| Publisher version: | http://doi.org/10.1109/IROS47612.2022.9981587 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Location awareness, Visualization, Navigation, Computational modeling, System integration, Robot sensing systems, Real-time systems |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10152290 |
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