Kyung Shik Roh

Biped humanoid robot Mahru III

This paper presents a new biped humanoid robot Mahru III (150 cm, 62 Kg) developed by Samsung Electronics Co., Ltd in 2007. The ultimate goal of this project is to design biped humanoid robots connected to a wireless network environment so that they can be used in our daily life. As the first step, Mahru III was built as a pilot platform for future household companion robots. In this paper we focus on the design of the mechanical system, electrical/electronic parts, and the control algorithm with some experimental results. We designed the mechanical system to achieve lightweight, high stiffness and high energy efficiency through CAE(computer aided engineering) analysis and DOE(design of experiments)-based optimization. We also built an extensible electrical/electronic system to provide for future network-based applications. Walking control algorithm is realized to make stable walking on an uneven floor possible. The experiments of the first phase shows that our humanoid can walk at 1.3 km/h and overcome 1 cm protrusions successfully.

Modeling and control of robotic surgical platform for single-port access surgery

In this paper, we present a modeling and control method for a single-port access robot developed by our robotics group at the Samsung Advanced Institute of Technology. The surgical robot consists of a snake-like 6-degree-of-freedom (DOF) guide tube, two 7-DOF tools, a 3-DOF stereo camera, and a 5-DOF slave arm. The robot is capable of reaching various surgical sites inside the abdominal cavity from a single incision on the body. To estimate the workspace and control the guide tube to a desired location, we first obtain the forward kinematics model of the guide tube and then propose a Cartesian-level controller. The wire actuation mechanism for the tools exhibit nonlinear backlash behavior because of wire compliance and friction between the wire and Teflon-coated conduit. We compensate for the backlash in the tool joints by adding the backlash inverse with smoothing term as a feedforward term.

Towards natural bipedal walking: Virtual gravity compensation and capture point control

To achieve dynamic balancing and natural walking for a bipedal robot we propose a novel force-based control framework. Given 6-dimensional pose vector representing robots posture and attitude, desired force and moment in the task space are computed. To generate the force and moment as desired, we propose the use of virtual gravity compensation (VGC), essentially a dynamic controller that outputs joint torques. By using the VGC-based balancing controller, the robot can maintain a desired pose stably even on a tilting plate. We also propose to extend the VGC-based balancing controller to implement a walking algorithm that controls the desired pose in terms of capture point using a finite state machine. The control algorithm was tested with torque-controlled humanoid platforms developed by our group to demonstrate robust and natural gaits under various walking environments. The robot walked robustly on irregular surfaces and recovered from external pushes. The robot also exhibited natural walking motions such as pendulum-like leg swings and heel-to-toe transitions, a characteristic feature of human gait, all without explicitly designating joint angle trajectories.

Balancing control of a biped robot

We propose a balancing control framework for a torque-controlled biped robot, Roboray. Roboray has two 6 DOF legs and torque sensors are integrated at all the leg joints. It has a new cable-driven joint module as a pitch joint drive, which is highly back-drivable and elastic. Using these hardware characteristics, we propose a new balancing control algorithm. This algorithm is the combination of gravity compensation, virtual gravity control and damping control. A friction compensation technique is also introduced in order to eliminate the nonlinearity of damping and to improve the performance of torque tracking. Our proposed method is applied to a simple inverted pendulum system and Roboray. Experimental results show that these two system keep their balance when they are pushed slightly.

Control design to achieve dynamic walking on a bipedal robot with compliance

We propose a control framework for dynamic bipedal locomotion with compliant joints. A novel 3D dynamic walking is achieved by utilizing natural dynamics of the system. It is done by 1) driving robot joints directly with the posture-based state machine and 2) controlling tendon-driven compliant actuators. To enlarge gaits basin attraction for stable walking, we also adaptively plan step-to-step motion and compensate stance/swing motion. Final joint input is described by a superposition of state machine control torques and compensation torques of balancers. Various walking styles are easily generated by composing straight and turning gait-primitives and such walking is effectively able to adapt on various environments. Our proposed method is applied to a torque controlled robot platform, Roboray. Experimental results show that gaits are able to traverse inclined and rough terrains with bounded variations, and the result gaits are human-like comparing the conventional knee bent walkers.

On-board odometry estimation for 3D vision-based SLAM of humanoid robot

This paper addresses a vision-based 3D motion estimation framework for humanoid robots, which copes with human-like walking pattern. A humanoid robot, called Roboray, is designed for dynamic walking control with heel-toe motion like a human. In spite of stability and energy efficiency of the dynamic walking, it accompanies larger swaying motion and more uncertainty in camera movement than the conventional ZMP (Zero Moment Point)-based walking does. The framework effectively uses on-board odometry information from the robot to improve the performance of the visionbased motion estimation. To accomplish this, we propose an onboard odometry filter which fuses kinematic odometry, visual odometry, and raw IMU data. And the odometry filter is combined with vision-based SLAM to provide accurate motion model, so it enhances the SLAM estimates. Experimental results in indoor environment verify that the framework can successfully estimate the pose of Roboray in real-time.

Robust descriptors for 3D point clouds using Geometric and Photometric Local Feature

The robust perception of robots is strongly needed to handle various objects skillfully. In this paper, we propose a novel approach to recognize objects and estimate their 6-DOF pose using 3D feature descriptors, called Geometric and Photometric Local Feature (GPLF). The proposed descriptors use both the geometric and photometric information of 3D point clouds from RGB-D camera and integrate those information into efficient descriptors. GPLF shows robust discriminative performance regardless of characteristics such as shapes or appearances of objects in cluttered scenes. The experimental results show how well the proposed approach classifies and identify objects. The performance of pose estimation is robust and stable enough for the robot to manipulate objects. We also compare the proposed approach with previous approaches that use partial information of objects with a representative large-scale RGB-D object dataset.

Real-time 3D simultaneous localization and map-building for a dynamic walking humanoid robot

In this paper, we develop an onboard real-time 3D visual simultaneous localization and mapping system for a dynamic walking humanoid robot. With the constraints of processing and real-time operation, the system uses a lightweight localization and mapping approach based around the well-known extended Kalman filter but that features a robust and real-time relocalization system able to allow loop-closing and robust localization in 6D. The robot is controlled by torque references at the joints using its dynamic properties. This results in more energy efficient motion but also in lager movement than the one found in a conventional ZMP-based humanoid which carefully maintains the position of the center of mass on the plane. These more agile motions pose challenges for a visual mapping system having to operate in real time. The developed system features a combination of stereo camera, robust visual descriptors, and motion model switching to compensate for the larger motion and uncertainty. We provide practical implementation details of the system and methods, and test on the real humanoid robot. We compare our results with motion obtained with a motion capture system.

Vision-Based Obstacle Detection and Avoidance: Application to Robust Indoor Navigation of Mobile Robots

We propose a more practical and efficient method for obstacle detection and avoidance. In this paper, a robot detects obstacles based on the projective invariants of stereo cameras, fuses this information with two-dimensional scanning sensor data, and finally builds up a more informative and conservative occupancy map. Although this approach is not supposed to recognize the exact shape of the obstacles, this shortcoming is overcome in the actual application by its fast calculation time and robustness against the illumination conditions. To avoid detected obstacles, a new reactive obstacle avoidance strategy is also presented. To evaluate the proposed method, we applied it to the mobile robot iMARO-III. In this test, iMARO-III has succeeded in long-term operation for 7 days continuously without any intervention of engineers and any collision in the real office environment.

Global localization of mobile robot using omni-directional image correlation

This paper presents a localization method using circular correlation of omni-directional image. Mobile robot localization, especially in indoor conditions, is a key component in the development of service robots. Though stereo vision is widely used to find location, the performance is limited due to computational complexity and its view angle. To compensate for this, we utilize a single omni-directional camera which can capture 360( panoramic images around a robot at one time. Position of a mobile robot can be estimated by the correlation between CHL (Circular Horizontal Line) of the landmark image and CHL of image captured at the robot position. To accelerate computation, correlation values are calculated based on FFT (Fast Fourier Transform) and to increase reliability, CHLs are warped and correlation values are recalculated. Experimental results and performance in the real home environment show the feasibility of the method.