Getting the shot: Socially-aware viewpoints for autonomously observing tasks. Allevato, A., Sharp, A., & Pryor, M. In 2017 IEEE Workshop on Advanced Robotics and its Social Impacts (ARSO), pages 1–6, March, 2017. ISSN: 2162-7576
doi  abstract   bibtex   
In this work, we present an algorithm for autonomously determining the appropriate location from which to observe a human or robot agent (actor) while it completes a task in dynamic environments. We develop theory for selecting such a location using forward physical simulation of randomly-selected candidate viewpoints. The simulated points provide obstacle avoidance, and by incorporating a modified version of the Social Force Model, candidate viewpoints adjust themselves so that they do not encroach on the actor's personal space and/or safety region. The best observer position is chosen from these candidates to provide the most complete view of the task volume, taking into account the occlusion caused by the actor itself. We show that our algorithm works under a variety of task volume configurations, actor types (human and robot), and environmental constraints. Finally, the paper shows the results of hardware deployment on a two-robot system-one observer, and one actor. The paper concludes by examining the social impacts of deploying autonomous observation algorithms on real-world systems.
@inproceedings{allevato_getting_2017,
	title = {Getting the shot: {Socially}-aware viewpoints for autonomously observing tasks},
	shorttitle = {Getting the shot},
	doi = {10.1109/ARSO.2017.8025205},
	abstract = {In this work, we present an algorithm for autonomously determining the appropriate location from which to observe a human or robot agent (actor) while it completes a task in dynamic environments. We develop theory for selecting such a location using forward physical simulation of randomly-selected candidate viewpoints. The simulated points provide obstacle avoidance, and by incorporating a modified version of the Social Force Model, candidate viewpoints adjust themselves so that they do not encroach on the actor's personal space and/or safety region. The best observer position is chosen from these candidates to provide the most complete view of the task volume, taking into account the occlusion caused by the actor itself. We show that our algorithm works under a variety of task volume configurations, actor types (human and robot), and environmental constraints. Finally, the paper shows the results of hardware deployment on a two-robot system-one observer, and one actor. The paper concludes by examining the social impacts of deploying autonomous observation algorithms on real-world systems.},
	booktitle = {2017 {IEEE} {Workshop} on {Advanced} {Robotics} and its {Social} {Impacts} ({ARSO})},
	author = {Allevato, Adam and Sharp, Andrew and Pryor, Mitch},
	month = mar,
	year = {2017},
	note = {ISSN: 2162-7576},
	keywords = {Collision avoidance, Force, Mathematical model, Navigation, Observers, Robot sensing systems, actor personal space, actor safety region, autonomous observation algorithm, autonomous task observation, collision avoidance, dynamic environments, environmental constraint, forward physical simulation, mobile robots, multi-robot systems, observer position, observers, obstacle avoidance, randomly-selected candidate viewpoints, robot agent, social force model, socially-aware viewpoint, task volume configuration, two-robot system},
	pages = {1--6},
}

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