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Pei-Feng Chen

dblp:60/4375 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2003
—ORCID · none

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

Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1

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 · 79% Legged, aerial and field robots · 21%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control › motion planning › legged locomotion planning
footstep planning
0.012003
Motion planning for humanoid walking in a layered environment · ICRA 2003
Robotics › Motion planning and robot control
motion planning
0.012003
Motion planning for humanoid walking in a layered environment · ICRA 2003
Robotics › Motion planning and robot control › path planning
collision-free path planning
0.012003
Motion planning for humanoid walking in a layered environment · ICRA 2003
Robotics › Legged, aerial and field robots › humanoid robot
humanoid walking
0.012003
Motion planning for humanoid walking in a layered environment · ICRA 2003
Robotics › Legged, aerial and field robots › legged robots
legged robot locomotion
0.012003
Motion planning for humanoid walking in a layered environment · ICRA 2003

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

sampling-based planning · 0.0local planning · 0.0global planning · 0.0
YearPublicationVenuePosition
2003 Motion planning for humanoid walking in a layered environment
abstract
Motion planning is one of the key capabilities for autonomous humanoid robots. Previous researches have focused on weight balancing, collision detection, and gait generation. Most planners either assume that the environment can be simplified to a 2D workspace or assume that the path is given. In this paper, we propose a motion planning system capable of generating both global and local motions for a humanoid robot in a layered or two and half dimensional environment. The planner can generate a gross motion that moves the humanoid vertically as well as horizontally to avoid obstacles in the environments. The gross motion is further realized by a local planner that determines the most efficient footsteps and locomotion over uneven terrain. If the local planner fails, the failure is feedback to the global planner to consider other alternative paths. The implemented humanoid planning system is an interactive tool that can compute collision-free motions for a humanoid robot in an online manner.
Tsai-Yen Li, Pei-Feng Chen, Pei-Zhi Huang
ICRA2