Hyoin Kim

Charge-ordering cascade with spin-orbit Mott dimer states in metallic iridium ditelluride

Spin-orbit coupling results in technologically-crucial phenomena underlying magnetic devices like magnetic memories and energy-efficient motors. In heavy element materials, the strength of spin-orbit coupling becomes large to affect the overall electronic nature and induces novel states such as topological insulators and spin-orbit-integrated Mott states. Here we report an unprecedented charge-ordering cascade in IrTe2 without the loss of metallicity, which involves localized spin-orbit Mott states with diamagnetic Ir(4+)-Ir(4+) dimers. The cascade in cooling, uncompensated in heating, consists of first order-type consecutive transitions from a pure Ir(3+) phase to Ir(3+)-Ir(4+) charge-ordered phases, which originate from Ir 5d to Te 5p charge transfer involving anionic polymeric bond breaking. Considering that the system exhibits superconductivity with suppression of the charge order by doping, analogously to cuprates, these results provide a new electronic paradigm of localized charge-ordered states interacting with itinerant electrons through large spin-orbit coupling.

Path planning and control of multiple aerial manipulators for a cooperative transportation

This paper presents planning and control of multiple aerial manipulators for cooperative transportation. Individual aerial manipulators which consist of a hexacopter and 2-DOF robotic arm are controlled by an augmented adaptive sliding mode controller based on a closed-chain robot dynamics. The desired path for each aerial manipulator is obtained by using RRT* to transport an object to the desired position. It also considers the constraints about the grasping point at the end effector. To validate the proposed planning and control algorithm, an experimental result with multiple custom-made aerial manipulators is presented, which involves two aerial manipulators tracking the user-guided command and planned trajectory.

Planning and Control for Collision-Free Cooperative Aerial Transportation

This paper presents planning and control synthesis for multiple aerial manipulators to transport a common object. Each aerial manipulator that consists of a hexacopter and a two-degree-of-freedom robotic arm is controlled by an augmented adaptive sliding mode controller based on a closed-chain robot dynamics. We propose a motion planning algorithm by exploiting rapidly exploring random tree star (RRT*) and dynamic movement primitives (DMPs). The desired path for each aerial manipulator is obtained by using RRT* with Bezier curve, which is designed to handle environmental obstacles, such as buildings or equipments. During aerial transportation, to avoid unknown obstacle, DMPs modify the trajectory based on the virtual leader–follower structure. By the combination of RRT* and DMPs, the cooperative aerial manipulators can carry a common object to keep reducing the interaction force between multiple robots while avoiding an obstacle in the unstructured environment. To validate the proposed planning and control synthesis, two experiments with multiple custom-made aerial manipulators are presented, which involve user-guided trajectory and RRT*-planned trajectory tracking in unstructured environments.Note to Practitioners—This paper presents a viable approach to autonomous aerial transportation using multiple aerial manipulators equipped with a multidegree-of-freedom robotic arm. Existing approaches for cooperative manipulation based on force decomposition or impedance-based control often require a heavy or expensive force/torque sensor. However, this paper suggests a method without using a heavy or expensive force/torque sensor based on closed-chain dynamics in joint space and rapidly exploring random tree star (RRT*) that generates the desired trajectory of aerial manipulators. Unlike conventional RRT*, in this paper, our method can also avoid an unknown moving obstacle during aerial transportation by exploiting RRT* and dynamic movement primitives. The proposed planning and control synthesis is tested to demonstrate performance in a lab environment with two custom-made aerial manipulators and a common object.

Motion planning with movement primitives for cooperative aerial transportation in obstacle environment

This paper presents a motion planning approach for cooperative transportation using aerial robots. We describe a framework based on Parametric Dynamic Movement Primitives (PDMPs) for coordinating multiple aerial robots and their manipulators quickly in an environment cluttered with obstacles. In order to emulate the optimal motion, we combine PDMPs and Rapidly Exploring Randomized Trees star (RRT∗) by using the results of RRT∗ as demonstrations for PDMPs. For efficient description of the motions corresponding to the environment, we utilize Gaussian Process Regression (GPR) to acquire of the explicit relationship between environmental parameters and style parameters of PDMPs which decide the motions. Simulation and experiment results are attached to validate the proposed framework.