A Natural Interaction Interface for UAVs Using Intuitive Gesture Recognition
The popularity of unmanned aerial vehicles (UAVs) is increasing as technological advancements boost their favorability for a broad range of applications. One application is science data collection. In fields like earth and atmospheric science, researchers
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Abstract The popularity of unmanned aerial vehicles (UAVs) is increasing as technological advancements boost their favorability for a broad range of applications. One application is science data collection. In fields like earth and atmospheric science, researchers are seeking to use UAVs to augment their current portfolio of platforms and increase their accessibility to geographic areas of interest. By increasing the number of data collection platforms, UAVs will significantly improve system robustness and allow for more sophisticated studies. Scientists would like the ability to deploy an available fleet of UAVs to traverse a desired flight path and collect sensor data without needing to understand the complex low-level controls required to describe and coordinate such a mission. A natural interaction interface for a Ground Control System (GCS) using gesture recognition is developed to allow non-expert users (e.g., scientists) to define a complex flight path for a UAV using intuitive hand gesture inputs from the constructed gesture library. The GCS calculates the combined trajectory on-line, verifies the trajectory with the user, and sends it to the UAV controller to be flown. Keyword Natural interaction Non-expert user
Gesture
Trajectory
Flight path
UAV
M. Chandarana (&) K. Shimada Carnegie Mellon University, Pittsburgh, PA, USA e-mail: [email protected] K. Shimada e-mail: [email protected] A. Trujillo B. Danette Allen NASA Langley Research Center, Hampton, VA, USA e-mail: [email protected] B. Danette Allen e-mail: [email protected] © Springer International Publishing Switzerland 2017 P. Savage-Knepshield and J. Chen (eds.), Advances in Human Factors in Robots and Unmanned Systems, Advances in Intelligent Systems and Computing 499, DOI 10.1007/978-3-319-41959-6_32
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1 Introduction Rapid technological advancements to unmanned aerial systems foster the use of these systems for a plethora of applications [1] including but not limited to search and rescue [2], package delivery [3], disaster relief [4], and reconnaissance [5]. Many of these tasks are repetitive or dangerous for a human operator [6]. Historically, unmanned aerial vehicles (UAVs) are piloted remotely using radio remotes [7], smartphones [8], or ground control systems. This is done at the control surface level and requires a deep understanding of the extensive workings of the internal controller (much like a pilot) and the various algorithms used for system autonomy. Recently, advances in autonomy have enabled UAVs to fly with preprogrammed mission objectives, required trajectories, etc. As their manufacturing costs decrease so does the cost of entry, thereby making UAVs more accessible to the masses. UAVs have also gained traction due to more robust wireless communication networks, smaller and more powerful processors, and embedded sensors [9]. Although these systems have the potential to perform a myriad of intricate tasks whose number increases with the complexity of the technology, very few pe
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