72 citations · 188 across the 36 of their papers we have counts for
6 papers · 1 filter
Optimal Control-Based UAV Path Planning with Dynamically-Constrained TSP with Neighborhoods
Dae-Sung Jang, Hyeok-Joo Chae, Han-Lim Choi
This paper addresses path planning of an unmanned aerial vehicle (UAV) with remote sensing capabilities (or wireless communication capabilities). The goal of the path planning is t…
Multiscale Inverse Reinforcement Learning using Diffusion Wavelets
Jung-Su Ha, Han-Lim Choi
This work presents a multiscale framework to solve an inverse reinforcement learning (IRL) problem for continuous-time/state stochastic systems. We take advantage of a diffusion wa…
Informative Path Planning and Mapping with Multiple UAVs in Wind Fields
Doo-Hyun Cho, Jung-Su Ha, Sujin Lee +2
Informative path planning (IPP) is used to design paths for robotic sensor platforms to extract the best/maximum possible information about a quantity of interest while operating u…
Multiscale Abstraction, Planning and Control using Diffusion Wavelets for Stochastic Optimal Control Problems
Jung-Su Ha, Han-Lim Choi
This work presents a multiscale framework to solve a class of stochastic optimal control problems in the context of robot motion planning and control in a complex environment. In o…
Iterative Methods for Efficient Sampling-Based Optimal Motion Planning of Nonlinear Systems
Jung-Su Ha, Han-Lim Choi, Jeong hwan Jeon
This paper extends the RRT* algorithm, a recently developed but widely-used sampling-based optimal motion planner, in order to effectively handle nonlinear kinodynamic constraints.…
Topology-Guided Path Integral Approach for Stochastic Optimal Control in Cluttered Environment
Jung-Su Ha, Soon-Seo Park, Han-Lim Choi
This paper addresses planning and control of robot motion under uncertainty that is formulated as a continuous-time, continuous-space stochastic optimal control problem, by develop…