This paper presents a real-time programming and parameter reconfiguration method for autonomous underwater robots in human-robot collaborative tasks. Using a set of intuitive and meaningful hand gestures, we develop a syntactically simple framework that is computationally more efficient than a complex, grammar-based approach. In the proposed framework, a convolutional neural network is trained to provide accurate hand gesture recognition; subsequently, a finite-state machine- based deterministic model performs efficient gesture-to-instruction mapping and further improves robustness of the interaction scheme. The key aspect of this framework is that it can be easily adopted by divers for communicating simple instructions to underwater robots without using artificial tags such as fiducial markers or requiring memorization of a potentially complex set of language rules. Extensive experiments are performed both on field-trial data and through simulation, which demonstrate the robustness, efficiency, and portability of this framework in a number of different scenarios. Finally, a user interaction study is presented that illustrates the gain in the ease of use of our proposed interaction framework compared to the existing methods for the underwater domain.
|Original language||English (US)|
|Title of host publication||2018 IEEE International Conference on Robotics and Automation, ICRA 2018|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||8|
|State||Published - Sep 10 2018|
|Event||2018 IEEE International Conference on Robotics and Automation, ICRA 2018 - Brisbane, Australia|
Duration: May 21 2018 → May 25 2018
|Name||Proceedings - IEEE International Conference on Robotics and Automation|
|Conference||2018 IEEE International Conference on Robotics and Automation, ICRA 2018|
|Period||5/21/18 → 5/25/18|
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