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Jun Okamoto Jr.

dblp:28/5229 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2005
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2Systems, architecture and hardware · 2

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
2 papers
Human-robot interaction · 100%
Artificial intelligence
2 papers
Motion planning and robot control · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Human-robot interaction
assistive robotics
0.122005
Usability Study of a Control Framework for an Intelligent Wheelchair · ICRA 2005
Incorporating User Inputs in Motion Planning for a Smart Wheelchair · ICRA 2004
Human-robot interaction › assistive robotics
smart wheelchair
0.122005
Usability Study of a Control Framework for an Intelligent Wheelchair · ICRA 2005
Incorporating User Inputs in Motion Planning for a Smart Wheelchair · ICRA 2004
Robotics › Motion planning and robot control
motion planning
0.122005
Incorporating User Inputs in Motion Planning for a Smart Wheelchair · ICRA 2004
Usability Study of a Control Framework for an Intelligent Wheelchair · ICRA 2005
Robotics › Motion planning and robot control › teleoperation
shared control
0.122005
Incorporating User Inputs in Motion Planning for a Smart Wheelchair · ICRA 2004
Usability Study of a Control Framework for an Intelligent Wheelchair · ICRA 2005

Methods — techniques the papers use, named apart from their topics

usability study · 0.1blended control · 0.1hierarchical control · 0.1deliberative and reactive planning · 0.1
YearPublicationVenuePosition
2005 Usability Study of a Control Framework for an Intelligent Wheelchair
abstract
We describe the development and assessment of a computer controlled wheelchair called the SMARTCHAIR. A shared control framework with different levels of autonomy allows the human operator to stay in complete control of the chair at each level while ensuring her safety. The framework incorporates deliberative motion plans or controllers, reactive behaviors, and human user inputs. At every instant in time, control inputs from these three different sources are blended continuously to provide a safe trajectory to the destination, while allowing the human to maintain control and safely override the autonomous behavior. In this paper, we present usability experiments with 50 participants and demonstrate quantitatively the benefits of human-robot augmentation.
Sarangi P. Parikh, Valdir Grassi Jr., Vijay Kumar 0001, Jun Okamoto Jr.
ICRA4
2004 Incorporating User Inputs in Motion Planning for a Smart Wheelchair
abstract
We describe the development and assessment of a computer controlled wheelchair equipped with a suite of sensors and a novel interface, called the SMARTCHAIR. The main focus of this paper is a shared control framework which allows the human operator to interact with the chair while it is performing an autonomous task. At the highest level, the autonomous system is able to plan paths using high level deliberative navigation behaviors depending on destinations or waypoints commanded by the user. The user is able to locally modify or override previously commanded autonomous behaviors or plans. This is possible because of our hierarchical control strategy that combines three independent sources of control inputs: deliberative plans obtained from maps and user commands, reactive behaviors generated by stimuli from the environment, and user-initiated commands that might arise during the execution of a plan or behavior. The framework we describe ensures the user's safety while allowing the user to be in complete control of a potentially autonomous system.
Sarangi P. Parikh, Valdir Grassi Jr., Vijay Kumar 0001, Jun Okamoto Jr.
ICRA4