Dynamic Reconfiguration of Mission Parameters in Underwater Human-Robot Collaboration

TL;DR

This paper introduces a real-time, gesture-based communication framework for underwater robots that enhances robustness and usability without relying on artificial markers or complex language rules.

Contribution

It develops a simple, efficient gesture recognition and instruction mapping system enabling divers to easily control underwater robots.

Findings

The framework achieves high accuracy in gesture recognition.

It demonstrates robustness and efficiency in diverse underwater scenarios.

User studies show improved usability over existing methods.

Abstract

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 them to memorize 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 usability of our proposed interaction framework compared to the existing methods for underwater domains.