Jae-Han Park

Spatial uncertainty model for visual features using a Kinect™ sensor.

This study proposes a mathematical uncertainty model for the spatial measurement of visual features using Kinect™ sensors. This model can provide qualitative and quantitative analysis for the utilization of Kinect™ sensors as 3D perception sensors. In order to achieve this objective, we derived the propagation relationship of the uncertainties between the disparity image space and the real Cartesian space with the mapping function between the two spaces. Using this propagation relationship, we obtained the mathematical model for the covariance matrix of the measurement error, which represents the uncertainty for spatial position of visual features from Kinect™ sensors. In order to derive the quantitative model of spatial uncertainty for visual features, we estimated the covariance matrix in the disparity image space using collected visual feature data. Further, we computed the spatial uncertainty information by applying the covariance matrix in the disparity image space and the calibrated sensor parameters to the proposed mathematical model. This spatial uncertainty model was verified by comparing the uncertainty ellipsoids for spatial covariance matrices and the distribution of scattered matching visual features. We expect that this spatial uncertainty model and its analyses will be useful in various Kinect™ sensor applications.

Building a smart home environment for service robots based on RFID and sensor networks

This paper is concerned with constructing a prototype smart home environment which has been built in the research building of Korea Institute of Industrial Technology (KITECH) to demonstrate the practicability of a robot-assisted future home environment. Key functionalities that a home service robot must provide are localization, navigation, object recognition and object handling. 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. We initiated a smart home environment project for light-weight service robots to provide reliable services by interacting with the environment through the wireless sensor networks. This environment consists of the following three main components: smart objects with an radio frequency identification (RFID) tag and smart appliances with sensor network functionality; the home server that connects smart devices as well as maintains information for reliable services; and the service robots that perform tasks in collaboration with the environment. In this paper, we introduce various types of smart devices which are developed for assisting the robot by providing sensing and actuation, and present our approach on the integration of these devices to construct the smart home environment. Finally, we discuss the future directions of our project.

RoboMaidHome: A Sensor Network-based Smart Home Environment for Service Robots

Over the past few years, many research groups have attempted to build smart environments. Recently, several groups have been actively conducting research into the construction of smart home environment for service robots to be used as assistants. A home service robot must have are localization, navigation, object recognition, and object handling functionalities. These operations are usually performed by a service robot by itself and the robot can perform the given tasks in a limited environment. In addition, they have shown only limited capabilities in a natural environment. We initiated a smart home environment project, RoboMaidHome, for light-weight service robots to provide reliable services by interacting with the environment through wireless sensor networks. This environment consists of the following three main components: (i) smart objects and smart appliances with sensor network functionality; (ii) a home server that connects smart devices as well as maintains information for reliable services; and (iii) service robots that perform tasks in collaboration with the environment. In this paper, we explain the basic concepts and the architecture of our project and address some key issues related to the project. Finally, we discuss the future directions of our project.

Intuitive peg-in-hole assembly strategy with a compliant manipulator

To realize a peg-in-hole assembly with a multi-degree of freedom (DOF) manipulator, the precise position of the hole is required. If the hole is not circular, orientation information is also necessary. A force/torque (F/T) sensor is widely used to sense the position and orientation of the hole. Attaching the F/T sensor on the wrist of a manipulator helps in estimating the position between the peg and hole. However, a person does not need precise information of an object when completing an assembly task. For example, when inserting a plug into an outlet, a person does not need to know the exact information and coordination about the position and orientation of the plug and outlet. A closer look at the process shows that the person tries to place a plug near the outlet and then finds the two holes by rubbing the plug against the outlet without looking. This paper introduces an intuitive assembly strategy inspired by this human behavior. This strategy does not need the precise location of the hole. Instead of an F/T sensor, this strategy adopts hybrid force/position control and passive compliance control for successful peg-in-hole assembly. The feasibility of the proposed strategy was verified through simulation and a hardware experiment.

A new object recognition system for service robots in the smart environment

This paper describes a new object recognition system for service robots in a smart environment full of RFID tags and connected by wireless networks. Object recognition is one of the basic functionalities a service robot should perform and many researchers have attempted to make the service robot recognize objects through vision processing in natural environments. However there is no conventional vision system that can recognize target objects robustly in a real-world workspace. In our smart environment, flexibility and robustness in object recognition are provided with the help of RFID tags and communication networks. RFID tags provide the presence and identity information of the object and the robot can recognize the object through vision processing based on a detected RFID code and downloaded visual descriptor information. For feature descriptors, we adopted MPEG-7 visual descriptors due to its concise and unambiguous description of the complex media contents. This paper focuses on developing our object recognition engine based on visual descriptors information in the smart environment. To this end, we propose a new object recognition architecture for service robots, and present a implemented matching algorithm on the basis of MPEG-7 visual descriptors such as color and texture. Experimental results show that the proposed system works well with good performance in terms of recognition rate. This object recognition system will be incorporated into our service robot platform and a pose estimation module is to be included in the next version of our object recognition system.

An object recognition system for a smart home environment on the basis of color and texture descriptors

A prototype smart home environment for service robots has been constructed in the research building of Korea Institute of Industrial Technology (KITECH) to demonstrate the feasibility of a robot-assisted future home environment. An inexpensive service robot outfitted with a camera, a Radio Frequency Identification (RFID) reader, and a communication module is installed in the building as a future service robot system which is cheap but robust. In our robotic system, the RFID reader gets the rough location data and object information, and then the robot performs the object recognition scheme to get the exact position of the object in order to grasp it. Due to its concise and unambiguous description of the complex media contents, we adopted MPEG-7 visual descriptors for our object recognition system. In this paper, we propose a fast object recognition system for our smart home environment project, on the basis of MPEG-7 visual descriptors including color and texture. Experimental results show that our proposed system works well with good performance in terms of speed and recognition rate. This object recognition system will be incorporated into our mobile robotics platform and a shape descriptor is to be included in the next version of our object recognition system.

Implementation of a ZigBee-based high performance sensor node for the robot environment platform

In this paper, a ZigBee-based wireless sensor network is configured for the robot to effectively communicate with the environment platform where each sensor node is implemented using a high performance microcontroller. The localization and the navigation functions are also required for the robot and the functions are executed with various types of sensor information. A new type of ZigBee stack is developed using the RUM(Router Under MAC) of the Atmel Corp. and it is applied to 32-bit ARM core microcontroller for the high performance sensor data manipulation and transmission. It is verified by experiments that the wireless sensor network consisting of developed high performance sensor nodes can be effectively used for the robot environment platform.

Reliable feature point detection and object pose estimation using photometric quasi-invariant SIFT

Object pose estimation from stereo images with unknown correspondence is a thoroughly studied problem in the computer vision and robot engineering literatures. Especially, it is important to detect the desirable corresponding points from images for object pose estimation. For this, many approaches have been proposed. Among them, the local feature descriptor, which describe the feature points that are robust to image deformations in an object or image, is one of the most promising approaches that has been applied to the stable feature detection problem successfully. Although any descriptors including the SIFT represent superior performance, these are based on luminance information rather than color information thereby resulting in instability to photometric variations such as shadows, highlights, and illumination changes. Therefore, we propose a novel method which extracts the interest points that are insensitive to both geometric and photometric variations in order to estimate more accurate and desirable object pose. In this method, we use photometric quasi-invariant features based on the dichromatic reflection model in order to achieve photometric invariance, and the SIFT is used for geometric invariance as well. The performance of the proposed method is evaluated with other local descriptors. Experimental results show that our method gives similar performance or outperforms them with respect to various imaging conditions. Finally, we estimate object pose by using the features extracted via the proposed method.

An intelligent navigation method for service robots in the smart environment

Autonomous navigation is one of primitive functionalities which service robots should have; nevertheless, the navigation problem for a service robot still has many difficulties because the real environment where service robots should works is so complex and dynamical. This paper proposes a framework of intelligent navigation for service robots based on a semantic map of the smart environment. In the smart environment, the robot can receive his position information from location sensors by sensor networks, and it can eliminate the accumulated localization errors. So the robot can accomplish confidential localization in the dynamic and complex environment. With the topological information in the semantic map, path planning problem can be simple even in the wide and complex spaces, and topological path from semantic map can be divided into several sub goals. Robust navigation can be accomplished by moving towards these sub goals with reactive navigation algorithm which has robust characteristics in the dynamic environments. Our approaches have ascertained the good performance on the localization and the navigation, and the feasibility of these methods could be confirmed with the result of experiments in the real environment.

Design of an anthropomorphic dual-arm robot with biologically inspired 8-DOF arms

From the perspective of kinematics, dual-arm manipulation in robots differs from single-arm manipulation in that it requires high dexterity in a specific region of the manipulator’s workspace. This feature has motivated research on the specialized design of manipulators for dual-arm robots. These recently introduced robots often utilize a shoulder structure with a tilted angle of some magnitude. The tilted shoulder yields better kinematic performance for dual-arm manipulation, such as a wider common workspace for each arm. However, this method tends to reduce total workspace volume, which results in lower kinematic performance for single-arm tasks in the outer region of the workspace. To overcome this trade-off, the authors of this study propose a design for a dual-arm robot with a biologically inspired four degree-of-freedom shoulder mechanism. This study analyzes the kinematic performance of the proposed design and compares it with that of a conventional dual-arm robot from the perspective of workspace and single-/dual-arm manipulability. The comparative analysis revealed that the proposed structure can significantly enhance single- and dual-arm kinematic performance in comparison with conventional dual-arm structures. This superior kinematic performance was verified through experiments, which showed that the proposed method required shorter settling time and trajectory-following performance than the conventional dual-arm robot.