Visual odometry is a popular approach to estimating robot motion using a monocular or stereo camera. This paper proposes a novel visual odometry scheme using a stereo camera for robust estimation of a 6 DOF motion in the dynamic environment. The false results of feature matching and the uncertainty of depth information provided by the camera can generate the outliers which deteriorate the estimation. The outliers are removed by analyzing the magnitude histogram of the motion vector of the corresponding features and the RANSAC algorithm. The features extracted from a dynamic object such as a human also makes the motion estimation inaccurate. To eliminate the effect of a dynamic object, several candidates of dynamic objects are generated by clustering the 3D position of features and each candidate is checked based on the standard deviation of features on whether it is a real dynamic object or not. The accuracy and practicality of the proposed scheme are verified by several experiments and comparisons with both IMU and wheel-based odometry. It is shown that the proposed scheme works well when wheel slip occurs or dynamic objects exist.
The development of a face robot basically targets very natural human-robot interaction (HRI), especially emotional interaction. So does a face robot introduced in this paper, named Buddy. Since Buddy was developed for a mobile service robot, it doesn’t have a living-being like face such as human’s or animal’s, but a typically robot-like face with hard skin, which maybe suitable for mass production. Besides, its structure and mechanism should be simple and its production cost low. This paper introduces the mechanisms and functions of mobile face robot named Buddy which can take on natural and precise facial expressions and make dynamic gestures driven by one laptop PC. Buddy also can perform lip-sync, eye-contact, face-tracking for lifelike interaction. In addition, by adopting a customized emotional reaction decision model, Buddy can create own personality, emotion and motive using various sensor data input. Based on this model, Buddy can interact probably with users and perform real-time learning using personality factors. The interaction performance of Buddy is successfully demonstrated by experiments and simulations.
Reliable functionalities for autonomous navigation and object recognition/handling are key technologies to service robots for executing useful services in human environments. A considerable amount of research has been conducted to make the service robot perform these operations with its own sensors, actuators and a knowledge database. With all heavy sensors, actuators and a database, the robot could have performed the given tasks in a limited environment or showed the limited capabilities in a natural environment. With the new paradigms on robot technologies, we attempted to apply smart environments technologies-such as RFID, sensor network and wireless network- to robot functionalities for executing reliable services. In this paper, we introduce concepts of proposed smart environments based robot navigation and object recognition/handling method and present results on robot services. Even though our methods are different from existing robot technologies, successful implementation result on real applications shows the effectiveness of our approaches. Keywords:Smart Environments, Service Robot, Navigation
People have expected a humanoid robot to move as naturally as a human being does. The natural movements of humanoid robot may provide people with safer physical services and communicate with persons through motions more correctly. This work presented a methodology to generate the natural motions for a humanoid robot, which are converted from human motion capture data. The methodology produces not only kinematically mapped motions but dynamically mapped ones. The kinematical mapping reflects the human-likeness in the converted motions, while the dynamical mapping could ensure the movement stability of whole body motions of a humanoid robot. The methodology consists of three processes: (a) Human modeling, (b) Kinematic mapping and (c) Dynamic mapping. The human modeling based on optimization gives the ZMP (Zero Moment Point) and COM (Center of Mass) time trajectories of an actor. Those trajectories are modified for a humanoid robot through the kinematic mapping. In addition to modifying the ZMP and COM trajectories, the lower body (pelvis and legs) motion of the actor is then scaled kinematically and converted to the motion available to the humanoid robot considering dynamical aspects. The KIST humanoid robot, Mahru, imitated a dancing motion to evaluate the methodology, showing the good agreement in the motion.
This paper is concerned with face recognition for human-robot interaction (HRI) in robot environments. For this purpose, we use Tensor Subspace Analysis (TSA) to recognize the user's face through robot camera when robot performs various services in home environments. Thus, the spatial correlation between the pixels in an image can be naturally characterized by TSA. Here we utilizes face database collected in u-robot test bed environments in ETRI. The presented method can be used as a core technique in conjunction with HRI that can naturally interact between human and robots in home robot applications. The experimental results on face database revealed that the presented method showed a good performance in comparison with the well-known methods such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) in distant-varying environments.
Recently, simultaneous localization and mapping (SLAM) approaches employing Rao-Blackwellized particle filter (RBPF) have shown good results. However, no research is conducted to analyze the result representation of SLAM using RBPF (RBPF-SLAM) when particle diversity is preserved. After finishing the particle filtering, the results such as a map and a path are stored in the separate particles. Thus, we propose several result representations and provide the analysis of the representations. For the analysis, estimation errors and their variances, and consistency of RBPF-SLAM are dealt in this study. According to the simulation results, combining data of each particle provides the better result with high probability than using just data of a particle such as the highest weighted particle representation.
A new soft finger mechanism using a spring as a backbone is proposed in this work. It is a 4 DOF mechanism that consists of a spring and 3 cylinders, which behave like joints with 3 up-and-down rotations and 1 left-and-right rotation. To control each joint, cylinders have small holes in their cross-sectional areas, and wires of different length are penetrated into these holes. We can control each joint by pulling the corresponding wire. The forward kinematics is solved by using the geometry of mechanism. And the relationship (Jacobian) between the linear velocity of the wires and the joint angular rate is obtained. A virtual simulator is developed to test the validity of the kinematic model. In the experiment, first, the position control is conducted by tracking a given trajectory. Second, to verify the flexibility and safety, we show that the soft finger deflects in a safe manner, in spite of the collision with environment.
This work deals with a 4-DOF flexible continuum robot that employs a spring as its backbone. The mechanism consists of two modules and each module has 2 DOF. The special features of the proposed mechanism are the flexibility and the backdrivability of the whole body by using a spring backbone. Thus, even in the case of collision with human body, this device can ensure safety. The design and the kinematics for this continuum mechanism are introduced. The performance of this continuum mechanism was shown through simulation and experiment.
The field of robots is being widely accepted as a new technology today. Many robots are produced continuously to impart amusement to people. Especially the robot which operates with a wheelbarrow was enough of a work of art to arouse excitement in the audiences. All the wheelbarrow robots share the same technology in that the direction of roll and pitch are acting as balance controllers, allowing the robots to maintain balance for a long period by continuously moving forward and backward. However one disadvantage of this technology is that they cannot avoid obstacles in their way. Therefore movement in sideways is a necessity. For the control of rotation of yawing direction, the angle and direction of rotation are adjusted according to the velocity and torque of rotation of a motor. Therefore this study aimed to inquire into controlling yawing direction, which is responsible for rotation of a robot. This was followed by creating a simulation of a wheelbarrow robot and equipping the robot with a yawing direction controlling device in the center of the body so as to allow sideway movements.
Most omni-directional mobile robots have to change their trajectory for avoiding obstacles regardless of the size of the obstacles. However, an omni-directional mobile robot having kinematic redundancy can maintain the trajectory while the robot avoids small obstacles. This works deals with the kinematic modeling and motion planning of an omni-directional mobile robot with kinematic redundancy. This robot consists of three wheel mechanisms. Each wheel mechanism is modeled as having four joints, while only three joints are necessary for creating the omni-directional motion. Thus, each chain has one kinematic redundancy. Two types of wheel mechanisms are compared and its kinematic modeling is introduced. Finally, several motion planning algorithms using the kinematic redundancy are investigated. The usefulness of this robot is shown through experiment.
This study aims to propose the concept design of oil spill protection robot which can rapidly intervene to control the oil spillage situation at the sea. Taking into account the fact that a huge amount of oil is transported trans-continentally by oil tanker, none of industrialized countries are completely safe from the marine oil spill which results in social, economical and ecological damages to their communities. The employment of double hull-oil tanker, pipe line transporting can be most safe way. Yet complete prevention of oil spill is probably not realistic. Accordingly the alternative solution to control marine oil spill and minimize the damages caused by the incident using intelligent robot technology based on swarm control method is proposed. The main features of oil spill protection(OSP) robot is explained via following three perspectives. Firstly, from functional point of view, OSP robot system safely and efficiently replaces oil boom installation manually conducted by human workers with intelligent robot technology based on swarm control theory. For second, its modular architecture brings efficient storage of main components including oil boom and facilitates maintenance. For the last, its geometric form and shape enables whole system to be installed to helicopter, boat or oil tanker itself with ease and to rapidly deploy the units to the oil spill area.