State-Feedback Optimal Motion Planning in the Presence of Obstacles-Reference-Cited by-同舟云学术

State-Feedback Optimal Motion Planning in the Presence of Obstacles

Published:2023-12 Issue:12 Volume:8 Page:8406-8413
ISSN:2377-3766
Container-title:IEEE Robotics and Automation Letters
language:
Short-container-title:IEEE Robot. Autom. Lett.

Author:

Rousseas Panagiotis¹^ORCID,Bechlioulis Charalampos P.²^ORCID,Kyriakopoulos Kostas J.³^ORCID

Affiliation:

1. School of Mechanical Engineering, Control Systems Laboratory, National Technical University of Athens, Athens, Greece

2. Department of Electrical and Computer Engineering, University of Patras, Patras, Greece

3. Center of AI & Robotics (CAIR), New York University, Abu Dhabi, UAE

Funder

Hellenic Foundation for Research and Innovation

4th Call for HFRI PhD Fellowships

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Control and Optimization,Computer Science Applications,Computer Vision and Pattern Recognition,Mechanical Engineering,Human-Computer Interaction,Biomedical Engineering,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/7083369/10287676/10305251.pdf?arnumber=10305251

Reference45 articles.

1. Randomized Kinodynamic Planning

2. Learning robust perceptive locomotion for quadrupedal robots in the wild

3. D*lite;koenig;Proc Nat Conf Artif Intell,0

4. Learning quadrupedal locomotion over challenging terrain

5. Probabilistic roadmaps for path planning in high-dimensional configuration spaces

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. An Efficient Solution to Optimal Motion Planning With Provable Safety and Convergence;IEEE Open Journal of Control Systems;2024