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Yansong Shan

dblp:120/5731 · DBLP profile ↗
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2ranked-venue papers
2as first author
0since 2021 · last 1995
—ORCID · none

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

Human-computer interaction and ubiquitous computing · 1 · 1 first-authorApplied, interdisciplinary, general and emerging computing · 1 · 1 first-author

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.

Artificial intelligence
1 paper
Motion planning and robot control · 81% Robot manipulation · 19%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control
motion planning
0.011995
Obstacle accommodation motion planning · IEEE Trans. Robotics Autom. 1995
Robotics › Robot manipulation
grasping
0.011995
Obstacle accommodation motion planning · IEEE Trans. Robotics Autom. 1995
Robotics › Motion planning and robot control › robot kinematics
kinematic constraints
0.011995
Obstacle accommodation motion planning · IEEE Trans. Robotics Autom. 1995

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

inverse kinematics · 0.0contact constraint formulation · 0.0
YearPublicationVenuePosition
1995 Obstacle accommodation motion planning
abstract
A new robot path planning methodology, called obstacle accommodation, has been introduced for robots in cluttered environments. This methodology does not attempt to avoid physical contact between the robot and obstacles. Instead, it controls the contact to prevent damage to the robot. Obstacle accommodation represents a new robotic paradigm in which obstacles can contact robots at unspecified positions, i.e., a contact point can be at any point on any link. Obstacle accommodation requires analysis in kinematics, motion planning, dynamics, and control. This paper deals with the development of the kinematic constraints and with motion planning under these constraints. We begin by providing a general formulation of the motion constraints due to contact with obstacles. Next, a new inverse kinematics is presented that provides joint motion for robots under contact constraints. Finally, the new motion planning algorithm for robot motion in a cluttered environment is provided. The motion planning algorithm is verified by two examples, one on a linkage robot and the other on a mobile robot.>
Yansong Shan, Yoram Koren
IEEE Trans. Robotics Autom.1
1993 Design and motion planning of a mechanical snake
abstract
Abstract-The paper presents the design and motion planning for a mechanical snake robot that was built at the University of Michigan. The structure of the robot enables it to move without wheels. It is constructed of a series of articulated links, each one with a motor and linear solenoid. Although each link has only one motor, this structure allows the body configuration to be easily controlled thereby enabling the robot to move in very cluttered environments. The motion planning system provides the robot with a basic motion pattern that can be easily modified for different tasks and environments. The mechanical snake does not avoid obstacles on its way, but rather “accommodates ” them by continuing its motion towards the target while in contact with the obstacles. With our design and motion planning, each link has a different number of degrees-of-freedom in each motion stage, providing the robot with great adaptability even during contact
Yansong Shan, Yoram Koren
IEEE Trans. Syst. Man Cybern.1