16 citations · 16 across the 5 of their papers we have counts for
6 papers
: Sampling-Based Kinodynamic Replanning and Feedback Control over Approximate, Identified Models of Vehicular Systems
Aravind Sivaramakrishnan, Sumanth Tangirala, Dhruv Metha Ramesh +2
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. Scen…
A Survey on the Integration of Machine Learning with Sampling-based Motion Planning
Troy McMahon, Aravind Sivaramakrishnan, Edgar Granados +1
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 o…
Data-Efficient Characterization of the Global Dynamics of Robot Controllers with Confidence Guarantees
Ewerton R. Vieira, Aravind Sivaramakrishnan, Yao Song +5
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 contro…
Data-Efficient Learning of High-Quality Controls for Kinodynamic Planning used in Vehicular Navigation
Seth Karten, Aravind Sivaramakrishnan, Edgar Granados +2
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 promis…
Improving Kinodynamic Planners for Vehicular Navigation with Learned Goal-Reaching Controllers
Aravind Sivaramakrishnan, Edgar Granados, Seth Karten +2
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 ident…
Refined Analysis of Asymptotically-Optimal Kinodynamic Planning in the State-Cost Space
Michal Kleinbort, Edgar Granados, Kiril Solovey +3
We present a novel analysis of AO-RRT: a tree-based planner for motion planning with kinodynamic constraints, originally described by Hauser and Zhou (AO-X, 2016). AO-RRT explores…