Bayesian-inferred Flexible Path Generation in Human-Robot Collaborative Networks
William Bentz, Dimitra Panagou
Abstract
This paper presents a novel method for generating the trajectory of a robot assisting a human in servicing a set of tasks embedded in a convex 2-D domain. This method makes use of Bayesian inference to predict human intent in task selection. Rather than following optimal trajectory towards a single task, the robot computes a set of potentially optimal tasks each weighted by the human's posterior probability and superimposes them into a cost function that is designed to minimize the weighted Euclidean distance relative to set. The effect is a flexible path human-robot collaborative network that is shown in simulation to complete all tasks in a given domain in less time than existing methods for a certain class of highly impulsive humans, i.e., humans that tend to randomly switch tasks at times generated by a Poisson counting process. The algorithm is also illustrated through an experimental demonstration.
BibTeX
@inproceedings{iros2018_bayesianinferred,
title = {Bayesian-inferred Flexible Path Generation in Human-Robot Collaborative Networks},
author = {William Bentz and Dimitra Panagou},
booktitle = {IROS 2018},
year = {2018}
}