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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.19459 |
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| _version_ | 1866910624282312704 |
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| author | Simons, Cody Liu, Zhichao Marcus, Brandon Roy-Chowdhury, Amit K. Karydis, Konstantinos |
| author_facet | Simons, Cody Liu, Zhichao Marcus, Brandon Roy-Chowdhury, Amit K. Karydis, Konstantinos |
| contents | In this paper, we develop an embodied AI system for human-in-the-loop navigation with a wheeled mobile robot. We propose a direct yet effective method of monitoring the robot's current plan to detect changes in the environment that impact the intended trajectory of the robot significantly and then query a human for feedback. We also develop a means to parse human feedback expressed in natural language into local navigation waypoints and integrate it into a global planning system, by leveraging a map of semantic features and an aligned obstacle map. Extensive testing in simulation and physical hardware experiments with a resource-constrained wheeled robot tasked to navigate in a real-world environment validate the efficacy and robustness of our method. This work can support applications like precision agriculture and construction, where persistent monitoring of the environment provides a human with information about the environment state. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_19459 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Language-guided Robust Navigation for Mobile Robots in Dynamically-changing Environments Simons, Cody Liu, Zhichao Marcus, Brandon Roy-Chowdhury, Amit K. Karydis, Konstantinos Robotics Computer Vision and Pattern Recognition In this paper, we develop an embodied AI system for human-in-the-loop navigation with a wheeled mobile robot. We propose a direct yet effective method of monitoring the robot's current plan to detect changes in the environment that impact the intended trajectory of the robot significantly and then query a human for feedback. We also develop a means to parse human feedback expressed in natural language into local navigation waypoints and integrate it into a global planning system, by leveraging a map of semantic features and an aligned obstacle map. Extensive testing in simulation and physical hardware experiments with a resource-constrained wheeled robot tasked to navigate in a real-world environment validate the efficacy and robustness of our method. This work can support applications like precision agriculture and construction, where persistent monitoring of the environment provides a human with information about the environment state. |
| title | Language-guided Robust Navigation for Mobile Robots in Dynamically-changing Environments |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.19459 |