Heechan Shin
Research Subjects
Heechan Shin has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Donghyuk Kim
Scalable Graphics, Vision and Robotics (SGVR) Lab, School of Computing, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
1 shared publication - Mincheul Kang
Mechatronics Research and Development Center, Samsung Electronics, Hwaseong-si, South Korea
1 shared publication - Sung-Eui Yoon
KAIST
1 shared publication
Latest Publications
- Open Access
TORM: Fast and Accurate Trajectory Optimization of Redundant Manipulator given an End-Effector Path
A redundant manipulator has multiple inverse kinematics solutions per end-effector pose. Accordingly, there can be many trajectories for joints that follow a given endeffector path in the Cartesian space. In this paper, we present a trajectory optimization of a redundant manipulator (TORM) to synthesize a trajectory that follows a given end-effector path accurately, while achieving smoothness and collisionfree manipulation. Our method holistically incorporates three desired properties into the trajectory optimization process by integrating the Jacobian-based inverse kinematics solving method and an optimization-based motion planning approach. Specifically, we optimize a trajectory using two-stage gradient descent to reduce potential competition between different properties during the update. To avoid falling into local minima, we iteratively explore different candidate trajectories with our local update. We compare our method with state-of-the-art methods in test scenes including external obstacles and two non-obstacle problems. Our method robustly minimizes the pose error in a progressive manner while satisfying various desirable properties.
Open AccessTORM: Fast and Accurate Trajectory Optimization of Redundant Manipulator given an End-Effector Path
A redundant manipulator has multiple inverse kinematics solutions per end-effector pose. Accordingly, there can be many trajectories for joints that follow a given endeffector path in the Cartesian space. In this paper, we present a trajectory optimization of a redundant manipulator (TORM) to synthesize a trajectory that follows a given end-effector path accurately, while achieving smoothness and collisionfree manipulation. Our method holistically incorporates three desired properties into the trajectory optimization process by integrating the Jacobian-based inverse kinematics solving method and an optimization-based motion planning approach. Specifically, we optimize a trajectory using two-stage gradient descent to reduce potential competition between different properties during the update. To avoid falling into local minima, we iteratively explore different candidate trajectories with our local update. We compare our method with state-of-the-art methods in test scenes including external obstacles and two non-obstacle problems. Our method robustly minimizes the pose error in a progressive manner while satisfying various desirable properties.