This paper presents a new coarse-to-fine global localization approach based on place learning with a 2-D range scan; we use a support vector machine (SVM) to train a set of classifiers for place recognition. Exploiting the graphical property of generalized Voronoi graph extracted from an occupancy grid map, we segment the map into local places with a spectral clustering algorithm and wavefront propagation algorithm. A bag-of-words approach is employed to build feature vectors for training the SVM classifiers, which utilizes the shape context descriptors computed from each scan point. The global localization process consists of two stages: coarse localization and fine localization. Coarse localization from the SVM-based place recognition selects candidate places where the robot may be located, and fine localization computes the relative poses to the candidate places with fast spectral scan matching and estimates the correct robot pose with a particle filter algorithm, in which the candidate poses are exploited as seeds for initial random sampling. The feasibility of the proposed methods is demonstrated by the results of extensive simulations and experiments.

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.

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 gait's 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.

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.

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.

This paper presents a framework to generate dynamic walking for biped robots. A set of self-stable gait primitives is first constructed. It is done by 1) representing parametric gait primitives, 2) utilizing state-dependent torque control, and 3) doing numerical optimization that takes into account the complex multi-body dynamics with frictional contact forces. Dynamic walking to follow the arbitrary path including a curve is then generated online via sequentially composing primitive motions. Results show that dynamic gaits are humanlike and efficient compared to the conventional knee bent walkers. Our proposed method is applied to a torque-controlled, human-sized biped robot platform, `Roboray' which is cable-driven partially for joint compliance. Following a discussion on robot design and control, experimental results are also reported.

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 robot's 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.

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.