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_6 , the authors use the motion of the hand and arm instead of the hand gestures to perform interaction. A problem of this interaction scheme is that during interaction, operators have to hold the arm up and draw shapes by moving the hand and arm aloft. Such an interaction method is not so natural and it is unable for operators to draw shapes by the hand and arm precisely. The result provided by the authors only reaches about 97 In @cite_10 , the authors employ gloves with colored markers to implement gesture recognition underwater rather than directly basing the recognition on the whole hand or the glove covering the whole hand. The authors in the paper reveal neither the details of their gesture recognition algorithm nor the performance of their algorithm. However, a conceivable shortage is that the kinds of gestures that their algorithm can cover are limited since the recognition may be achievable only when there is enough space of colored markers shown in the camera.
@cite_10 @cite_6
1518898410 2166974693
Underwater environment is characterized by harsh conditions and is difficult to monitor. The CADDY project deals with the development of a companion robot devoted to support and to monitor human operations and activities during the dive. In this scenario the communication and correct reception of messages between the diver and the robot are essential for success of the dive goals. However, the underwater environment poses a set of technical constraints hardly limiting the communication possibilities. For such reasons the solution proposed is to develop a communication language based on the consolidated and standardized diver gestures, commonly employed during professional and recreational dives, thus leading to the definition of a CADDY language, called CADDIAN, and a communication protocol. This article focuses on the creation of the language providing alphabet, syntax and semantics: future work will explain the part of recognition of gestures that is still in progress. A gesture-based interaction framework is presented for controlling mobile robots. This natural interaction paradigm has few physical requirements, and thus can be deployed in many restrictive and challenging environments. We present an implementation of this scheme in the control of an underwater robot by an on-site human operator. The operator performs discrete gestures using engineered visual targets, which are interpreted by the robot as parametrized actionable commands. By combining the symbolic alphabets resulting from several visual cues, a large vocabulary of statements can be produced. An iterative closest point algorithm is used to detect these observed motions, by comparing them with an established database of gestures. Finally, we present quantitative data collected from human participants indicating accuracy and performance of our proposed scheme.