Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Sivaramakrishnan, Aravind"'
Autor:
Sivaramakrishnan, Aravind, Tangirala, Sumanth, Ramesh, Dhruv Metha, Granados, Edgar, Bekris, Kostas E.
This paper aims to increase the safety and reliability of executing trajectories planned for robots with non-trivial dynamics given a light-weight, approximate dynamics model. Scenarios include mobile robots navigating through workspaces with imperfe
Externí odkaz:
http://arxiv.org/abs/2409.11522
Autor:
Vieira, Ewerton R., Sivaramakrishnan, Aravind, Tangirala, Sumanth, Granados, Edgar, Mischaikow, Konstantin, Bekris, Kostas E.
Publikováno v:
2024 IEEE International Conference on Robotics and Automation (ICRA), Yokohama, Japan, 2024, pp. 27-33
Estimating the region of attraction (${\tt RoA}$) for a robot controller is essential for safe application and controller composition. Many existing methods require a closed-form expression that limit applicability to data-driven controllers. Methods
Externí odkaz:
http://arxiv.org/abs/2310.03246
Autor:
Sivaramakrishnan, Aravind, Tangirala, Sumanth, Granados, Edgar, Carver, Noah R., Bekris, Kostas E.
Publikováno v:
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Abu Dhabi, United Arab Emirates, 2024
This paper aims to improve the computational efficiency of motion planning for mobile robots with non-trivial dynamics through the use of learned controllers. Offline, a system-specific controller is first trained in an empty environment. Then, for t
Externí odkaz:
http://arxiv.org/abs/2310.03239
Publikováno v:
Foundations and Trends in Robotics: Vol. 9: No. 4, pp 266-327 (2022)
Sampling-based methods are widely adopted solutions for robot motion planning. The methods are straightforward to implement, effective in practice for many robotic systems. It is often possible to prove that they have desirable properties, such as pr
Externí odkaz:
http://arxiv.org/abs/2211.08368
Autor:
Vieira, Ewerton R., Sivaramakrishnan, Aravind, Song, Yao, Granados, Edgar, Gameiro, Marcio, Mischaikow, Konstantin, Hung, Ying, Bekris, Kostas E.
This paper proposes an integration of surrogate modeling and topology to significantly reduce the amount of data required to describe the underlying global dynamics of robot controllers, including closed-box ones. A Gaussian Process (GP), trained wit
Externí odkaz:
http://arxiv.org/abs/2210.01292
Autor:
Vieira, Ewerton R., Granados, Edgar, Sivaramakrishnan, Aravind, Gameiro, Marcio, Mischaikow, Konstantin, Bekris, Kostas E.
Understanding the global dynamics of a robot controller, such as identifying attractors and their regions of attraction (RoA), is important for safe deployment and synthesizing more effective hybrid controllers. This paper proposes a topological fram
Externí odkaz:
http://arxiv.org/abs/2202.08383
Publikováno v:
Machine Learning for Motion Planning (MLMP) Workshop at ICRA 2021, Xi'an, China
This paper aims to improve the path quality and computational efficiency of kinodynamic planners used for vehicular systems. It proposes a learning framework for identifying promising controls during the expansion process of sampling-based motion pla
Externí odkaz:
http://arxiv.org/abs/2201.02254
This paper aims to improve the path quality and computational efficiency of sampling-based kinodynamic planners for vehicular navigation. It proposes a learning framework for identifying promising controls during the expansion process of sampling-bas
Externí odkaz:
http://arxiv.org/abs/2110.04238
Planning for systems with dynamics is challenging as often there is no local planner available and the only primitive to explore the state space is forward propagation of controls. In this context, tree sampling-based planners have been developed, so
Externí odkaz:
http://arxiv.org/abs/1907.07876
Publikováno v:
Foundations & Trends in Robotics; 2021, Vol. 9 Issue 4, p266-327, 62p
