TY - JOUR
T1 - Passenger discomfort map for autonomous navigation in a robotic wheelchair
AU - Morales, Yoichi
AU - Watanabe, Atsushi
AU - Ferreri, Florent
AU - Even, Jani
AU - Shinozawa, Kazuhiro
AU - Hagita, Norihiro
N1 - Funding Information:
This research was supported by the Ministry of Internal Affairs and Communications with a contract entitled “Novel and innovative R&D making use of brain structures”. The authors would like to thank Nagasrikanth Kallakuri and Philip Chan for their help in performing the experiments. Part of this work was supported by JSPS KAKENHI Grants Number JP16K21719 and JP26118006 .
Publisher Copyright:
© 2018 Elsevier B.V.
PY - 2018/5
Y1 - 2018/5
N2 - This work presents a navigational approach that takes into consideration the perception of comfort by a human passenger. Comfort is the state of being at ease and free from stress; thus, comfortable navigation is a ride that, in addition to being safe, is perceived by the passenger as being free from anxiety and stress. This study considers how to compute passenger comfortable paths. To compute such paths, passenger discomfort is studied in locations with good visibility and those with no visibility. In locations with good visibility, passenger preference to ride in the road is studied. For locations with non-visible areas, the relationship between passenger visibility and discomfort is studied. Autonomous-navigation experiments are performed to build a map of human discomfort that is used to compute global paths. A path planner is proposed that minimizes a three-variable cost function: location discomfort cost, area visibility cost, and path length cost. Planner parameters are calibrated toward a composite trajectory histogram built with data taken from participant self-driving trajectories. Finally, autonomous navigation experiments with 30 participants show that the proposed approach is rated as more comfortable than the state-of-the-art shortest planner approach.
AB - This work presents a navigational approach that takes into consideration the perception of comfort by a human passenger. Comfort is the state of being at ease and free from stress; thus, comfortable navigation is a ride that, in addition to being safe, is perceived by the passenger as being free from anxiety and stress. This study considers how to compute passenger comfortable paths. To compute such paths, passenger discomfort is studied in locations with good visibility and those with no visibility. In locations with good visibility, passenger preference to ride in the road is studied. For locations with non-visible areas, the relationship between passenger visibility and discomfort is studied. Autonomous-navigation experiments are performed to build a map of human discomfort that is used to compute global paths. A path planner is proposed that minimizes a three-variable cost function: location discomfort cost, area visibility cost, and path length cost. Planner parameters are calibrated toward a composite trajectory histogram built with data taken from participant self-driving trajectories. Finally, autonomous navigation experiments with 30 participants show that the proposed approach is rated as more comfortable than the state-of-the-art shortest planner approach.
KW - Autonomous navigation
KW - HRI
KW - Human comfort
KW - Human factors
UR - http://www.scopus.com/inward/record.url?scp=85044867810&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85044867810&partnerID=8YFLogxK
U2 - 10.1016/j.robot.2018.02.002
DO - 10.1016/j.robot.2018.02.002
M3 - Article
AN - SCOPUS:85044867810
VL - 103
SP - 13
EP - 26
JO - Robotics and Autonomous Systems
JF - Robotics and Autonomous Systems
SN - 0921-8890
ER -