Although dead reckoning based on odometry and inertial sensors is essential for a robotic localization system, none of previous works gives reliable and accurate position estimates on irregular terrain over long periods of time. Classical approaches use one estimator (such as a Kalman filter) with a single system model. However the single system model is not good to deal with both of slip and no-slip situations because of the dynamics changes. In this paper, a multiple model approach that uses two Kalman filters is presented: one Kalman filter accounting for no-slip condition and the other for slip condition. The Interacting Multiple Model (IMM) is adopted to switch two Kalman filters depending on whether slip occurs or not, and gives the weighted sum of two filter estimates. Experimental results are included to validate our approach.

This paper presents a novel approach to electronic compassing for robot in an indoor environment. Operation of compass (magnetometer) is assumed on a mobile robot that is capable to traverse a complete circular path. The electronic compass is used to estimate a robot absolute heading with respect to the magnetic North. This is only compass approach where an evaluation of quality of calibration and magnetic environment is important as much as calibration itself. In this method, compass is able to detect the external magnetic interference and estimate it numerically. The approach also relates to automatic calibration and requires one full 360-degree rotation and multiple points for further analysis. This enhanced calibration procedure is performed in the magnetic field domain and implemented using a non-iterative algebraic technique. The quality of calibration is a function of input data goodness and how successful is the fitting procedure. The magnetic environment evaluation is performed by the distortion factor that is calculated at the end of calibration and helps to estimate quantitatively local external magnetic distortions and their influence on a heading measurement. The validity of this approach has been verified experimentally by using robot with electronic compass.

The odometry information used in localization can be quite erroneous when the robot follows the curved path or suffers from slippage. Thus the use of the low-cost gyroscope to compensate for an angular error is considered by many researchers. Conventional Kalman filtering methods that fuse the odometry with the gyroscope may produce infeasible solution because the robot parameters are estimated regardless of their physical constraints. In this paper, we propose a constrained Kalman filtering method that applies general constrained optimization technique to the estimation of the robot parameters. The state observability is improved by the additional state variables and the accuracy is also improved by the nonapproximated Kalman filter design. Experimental results show the proposed method effectively compensates for the odometry error and yields feasible parameter estimation at the same time

Typical robot development requires that hardware be mostly functional before significant software development begins. Utilizing virtual prototype of hardware and its environment can reduce development time and manufacturing cost. Virtual prototyping is, however, a highly challenging task requiring in-depth knowledge in many disciplines. Few of simulation tools developed to alleviate this difficulty fully manage the complexity caused by the fact that developer must design and manage form (physical shape), function, and behavior altogether incrementally. Also, such simulation tools often do not support co-development of the target software and its test environment from requirements analysis to implementation. In this paper, we present our experience of developing a virtual prototype of Samsung Home Robot (SHR) with Samsung Advanced Institute of Technology (SAIT) using ASADAL/OBJ. Virtual prototyping in ASADAL/OBJ enables incremental co-development of both target system and its test environment in an object-oriented way. Furthermore ASADAL/OBJ facilitates concurrent development of form, behavior, and function. These features increase productivity as well as confidence through incremental refinement and validation. We give a brief background on the ASADAL/OBJ framework, then illuminate our experience of developing a virtual prototype of SHR.

With the advances of robotics, computer science, and other related areas, home service robots attract much attention from both academia and industry. Home service robots present interesting technical challenges to the community in that they have a wide range of potential applications, such as home security, patient caring, cleaning, etc., and that the services provided by the robots in each application area are being defined as markets are formed and, therefore, they change constantly.Without architectural considerations to address these challenges, robot manufacturers often focus on developing technical components (e.g., vision recognizer, speech processor, and actuator) and then attempt to develop service robots by integrating these components. When prototypes are developed for a new application, or when services are added, modified, or removed from existing robots, unexpected, undesirable, and often dangerous side-effects, which are known as feature interaction problem, happen frequently. Reengineering of such robots can make a serious impact in delivery time and development cost.In this paper, we present our experience of re-engineering a prototype of a home service robot developed by Samsung Advanced Institute of Technology. First, we designed a modular and hierarchical software architecture that makes interaction among the components visible. With the visibility of interactions, we could assign functional responsibilities to each component clearly. Then, we re-engineered existing codes to conform to the new architecture using a reactive language Esterel. As a result, we could detect and solve feature interaction problems and alleviate the dificulty of adding or updating components.

In home environments, demands for a robot to serve a user are on the increase, such as cleaning rooms, bringing something to the user, and so on. To achieve these tasks, it is essential for developing a natural way of human-robot interaction (HRI). One of the most natural ways is that the robot approaches the user to do some tasks after recognizing the user's call and localizing its position. In this case, user localization becomes a key technology. In this paper, we propose a novel audio visual user localization system. It consists of a microphone array with eight sensors and a video camera. Estimating calling direction is achieved by the spectral subtraction of the spatial spectra. In particular, a novel beam forming method is proposed to suppress the nonstationary audio noises where they always occur in a real world. Furthermore, a robust method for face detection is proposed to double check the user based on an Adaboost classifier. It is improved to reduce the false alarms remarkably through a new postprocessing on face candidates. Successful results in a real home environment show its efficacy and feasibility. The implementation issues, limitations, and their possible solutions are also discussed.

Speech recognition using circular microphone array is addressed in this paper. Eight microphones are located around the service robot to form a 2D microphone array. To enhance the speech quality, a novel adaptive beamformer composed of a delay-and-sum beamformer, adaptive blocking filters (ABFs) and adaptive cancelling filters (ACFs) is proposed. While the adaptive generalized sidelobe canceller (AGSC) connects the ABF and the ACF in feedforward, the proposed adaptive beamformer has them in feedback. The advantages of the proposed structure are the robustness to the steering vector errors and cross-talks and the reduced number of filter taps that gives the same speech quality compared to the AGSC with a larger number of filter taps. The experimental results show that the proposed structure is superior to the AGSC in objective and subjective evaluations. Speech recognition result shows that the proposed robust adaptive beamformer guarantees the recognition performance even in a low SNR and highly reverberant environment.