TY - JOUR
T1 - Socially aware path planning for a flying robot in close proximity of humans
AU - Yoon, Hyung Jin
AU - Widdowson, Christopher
AU - Marinho, Thiago
AU - Wang, Ranxiao Frances
AU - Hovakimyan, Naira
N1 - Funding Information:
This material is based upon work supported by the National Science Foundation under National Robotics Initiative Grants No. 1528036 and No. 1830639. Authors’ addresses: H.-J. Yoon, T. Marinho, and N. Hovakimyan, University of Illinois at Urbana-Champaign, Mechanical Science and Engineering, 1206 W. Green Street, Urbana, IL, 61801; emails: {hyoon33, marinho, nhovakim}@illinois.edu; C. Widdowson and R. F. Wang, University of Illinois at Urbana-Champaign, Psychology, 603 E. Daniel Street, Champaign, IL, 61820; emails: {widdwsn2, wang18}@illinois.edu. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. © 2019 Association for Computing Machinery. 2378-962X/2019/09-ART41 $15.00 https://doi.org/10.1145/3341570
Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/10
Y1 - 2019/10
N2 - In this article, we present a preliminary motion planning framework for a cyber-physical system consisting of a human and a flying robot in vicinity. The motion planning of the flying robot takes into account the human's safety perception.We aim to determine a parametric model for the human's safety perception based on test data. We use virtual reality as a safe testing environment to collect safety perception data reflected on galvanic skin response (GSR) from the test subjects experiencing a flying robot in their vicinity. The GSR signal contains both meaningful information driven by the interaction with the robot and also disturbances from unknown factors. To address the issue, we use two parametric models to approximate the GSR data: (1) a function of the robot's position and velocity and (2) a random distribution. Intuitively, we need to choose the more likely model given the data.When GSR is statistically independent of the flying robot, then the random distribution should be selected instead of the function of the robot's position and velocity.We implement the intuitive idea under the framework of hidden Markov model (HMM) estimation. As a result, the proposed HMM-based model improves the likelihood compared to the Gaussian noise model, which does not make a distinction between relevant and irrelevant samples due to unknown factors. We also present a numerical optimal path planning method that considers the safety perception model while ensuring spatial separation from the obstacle despite the time discretization. Optimal paths generated using the proposed model result in a reasonably safe distance from the human. In contrast, the trajectories generated by the standard regression model with the Gaussian noise assumption, without consideration of unknown factors, have undesirable shapes.
AB - In this article, we present a preliminary motion planning framework for a cyber-physical system consisting of a human and a flying robot in vicinity. The motion planning of the flying robot takes into account the human's safety perception.We aim to determine a parametric model for the human's safety perception based on test data. We use virtual reality as a safe testing environment to collect safety perception data reflected on galvanic skin response (GSR) from the test subjects experiencing a flying robot in their vicinity. The GSR signal contains both meaningful information driven by the interaction with the robot and also disturbances from unknown factors. To address the issue, we use two parametric models to approximate the GSR data: (1) a function of the robot's position and velocity and (2) a random distribution. Intuitively, we need to choose the more likely model given the data.When GSR is statistically independent of the flying robot, then the random distribution should be selected instead of the function of the robot's position and velocity.We implement the intuitive idea under the framework of hidden Markov model (HMM) estimation. As a result, the proposed HMM-based model improves the likelihood compared to the Gaussian noise model, which does not make a distinction between relevant and irrelevant samples due to unknown factors. We also present a numerical optimal path planning method that considers the safety perception model while ensuring spatial separation from the obstacle despite the time discretization. Optimal paths generated using the proposed model result in a reasonably safe distance from the human. In contrast, the trajectories generated by the standard regression model with the Gaussian noise assumption, without consideration of unknown factors, have undesirable shapes.
KW - Hidden Markov model
KW - Human-robot interaction
KW - Optimal path planning
UR - http://www.scopus.com/inward/record.url?scp=85075672468&partnerID=8YFLogxK
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U2 - 10.1145/3341570
DO - 10.1145/3341570
M3 - Article
AN - SCOPUS:85075672468
SN - 2378-962X
VL - 3
JO - ACM Transactions on Cyber-Physical Systems
JF - ACM Transactions on Cyber-Physical Systems
IS - 4
M1 - 3341570
ER -