The uncertainty and variability of underwater environment propose the request to control underwater robots in real time and dynamically, especially in the scenarios where human and robots need to work collaboratively in the field. However, the underwater environment imposes harsh restrictions on the application of typical control and communication methods. Considering that gestures are a natural and efficient interactive way for human, we, utilizing convolution neural network, implement a real-time gesture-based recognition system, who can recognize 50 kinds of gestures from images captured by one normal monocular camera, and apply this recognition system in human and underwater robot interaction. We design A Flexible and Extendable Interaction Scheme (AFEIS) through which underwater robots can be programmed in situ underwater by human operators using customized gesture-based sign language. This paper elaborates the design of gesture recognition system and AFEIS, and presents our field trial results when applying this system and scheme on underwater robots.
In @cite_2 , the authors use ARTags to interact with robots underwater. In order to use this interaction method, operators must print ARTags and bind together into book form before going into the water. ARTags must be waterproofed in advance, and it is also a problem for operators to pick out the expected one from dozens of ARTags, which is unable to provide any intuitive meaning to readers.
@cite_2
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We describe an interaction paradigm for controlling a robot using hand gestures. In particular, we are interested in the control of an underwater robot by an on-site human operator. Under this context, vision-based control is very attractive, and we propose a robot control and programming mechanism based on visual symbols. A human operator presents engineered visual targets to the robotic system, which recognizes and interprets them. This paper describes the approach and proposes a specific gesture language called "RoboChat". RoboChat allows an operator to control a robot and even express complex programming concepts, using a sequence of visually presented symbols, encoded into fiducial markers. We evaluate the efficiency and robustness of this symbolic communication scheme by comparing it to traditional gesture-based interaction involving a remote human operator