Kenri Kodaka

Pose estimation of a mobile robot on a lattice of RFID tags

A method of estimating pose of a robot on a lattice of RFID tags is described. In recent years, radio frequency identification (RFID) technology has become a very popular method for localizing robots because it is robust to disturbances such as lighting and obstacles, which adversely affect the conventional methods that use cameras, supersonic waves and so on. Despite the advantage that RFID tags, especially passive tags, can be inexpensively mass-produced, previous studies using RFID have not targeted the detailed work of robots because they have made use of RFID tags dotted over a wide area as landmarks. Therefore, it is still difficult to use the technology at home. There is a model room in WABOT-HOUSE Laboratory of Waseda University where the floor is equipped with a lattice of RFID tags at 300 mm intervals, simulating a future home environment where robots interact symbiotically with humans. We speculate that such an environment, where the tags are distributed at regular intervals, is one of the most probable infrastructures of the near future and propose a method that use Monte Carlo localization to estimate the pose of robot on the lattice. Our experiments show that robots can localize their position more precisely than the interval of tags and also estimate their orientation successfully by using the proposed method when two readers are placed in appropriate positions.

Active localization of a robot on a lattice of RFID tags by using an entropy map

We have developed a novel way for robots to estimate their pose dynamically in an environment in which RFID tags have been arranged. We previously developed a method for localizing robots using a particle filter. Testing in a room equipped with a lattice of RFID tags at 300-mm intervals revealed that the estimation fails when the robots RFID readers are near the center of the robots rotation because the reader could not detect enough tags by rotating movements when the robots positions are not suitable. We have overcome this problem by developing an active localization algorithm that generates an entropy map from the RFID arrangement information, predicts the pose using a particle filter, and attracts the robot to the target using a dynamic model, the fundamental unit of which is rotation-based angular velocity. Testing demonstrated that a robot using this algorithm and an entropy map can estimate its pose robustly without falling into a dead zone by moving only about 20 cm at most.

Transport services on indoor map generating system using spatially distributed RFID tags for home environment

This paper proposes a methodology for generating an indoor map of the home environment using widely installed RFID tags, which is being experimentally developed in the model room in the WABOT-HOUSE Laboratory[1]. Our laboratory is studying a system that generates and renews an environmental map of a room in real time in cooperation with three subsystems: a tag infrastructure where many RFID (Radio Frequency Identification) tags are widely installed over the environment, indoor RT(Robot Technology) modules, such as robots and furniture with embedded computing technology, and a home server module that monitors their spatial information. In this paper, we describe the basic concept and contents of the system, then we propose a method of localizing the robot and of moving to a destination while avoiding obstacles. Finally we report an experiment on which the robot succeed in advancing to a resident in the room, as an example of transport services making use of our system.

Discussion on the Forward–Backward Configuration Effect of Reader Antennas in a Floor-Installed RFID System

A significant discussion is provided on the configuration of reader antennas (RAs) affecting a robots positioning performance in a floor-installed RFID infrastructure for two-wheeled robots. RFID systems where IC tags are installed under/on floors have been widely utilized for positioning infrastructures. The RAs should be properly placed on a robot so that such an environment can give full play to its potential capabilities of positioning the robot. This problem calls for guidelines in designing the configuration of RAs. Our previous simulation study extracted an effect involving RA configuration in front of and behind the robots moving direction (the forward–backward configuration effect), which can be a critical parameter for positioning accuracy, while it lacked a full investigation of the cause of the effect. This paper not only discusses an extraction of the effect, but also presents a detailed discussion on how it occurs through data analysis, a mathematically schematic proof and a control experiment for validation. Our discussions are widely applicable to localization methods using landmarks on/under the floor because our model assumes a simple and general detecting condition.

Rhythm-based adaptive localization in incomplete RFID landmark environments

This paper proposes a novel hybrid-structured model for the adaptive localization of robots combining a stochastic localization model and a rhythmic action model, for avoiding vacant spaces of landmarks efficiently. In regularly arranged landmark environments, robots may not be able to detect any landmarks for a long time during a straight-like movement. Consequently, locally diverse and smooth movement patterns need to be generated to keep the position estimation stable. Conventional approaches aiming at the probabilistic optimization cannot rapidly generate the detailed movement pattern due to a huge computational cost; therefore a simple but diverse movement structure needs to be introduced as an alternative option. We solve this problem by combining a particle filter as the stochastic localization module and the dynamical action model generating a zig-zagging motion. The validation experiments, where virtual-line-tracing tasks are exhibited on a floor-installed RFID environment, show that introducing the proposed rhythm pattern can improve a minimum error boundary and a velocity performance for arbitrary tolerance errors can be improved by the rhythm amplitude adaptation fed back by the localization deviation.

Active-localization methods for mobile robots in a coarsely structured environment with floor-embedded RFID tags and indoor GPS

Two active-localization methods for mobile robots, namely, “rhythm-based adaptive localization” for floor-embedded RFID tags and “real-time kinematic Doppler positioning” for indoor GPS (an indoor messaging system, or IMES), are proposed. Both methods increase the positioning accuracy of a robot by moving the sensor attached to the robot. The results of a positioning experiment with floor-embedded RFID tags show that the rhythm-based adaptive-localization method can achieve a positioning accuracy of 25 cm under the conditions that noise is intentionally added to the value of a wheel encoder, the installation interval of tags is 30 cm, and the tag installation is incomplete (i.e., some tags are missing). On the other hand, the results of a positioning experiment using IMES show that the real-time kinematic Doppler-positioning method can achieve centimeter to decimeter-level positioning accuracy even if the number of visible transmitters is only one (i.e., trilateration is not used).

Exploring movable space using rhythmical active touch in disordered obstacle environment

We propose a novel navigation system for adaptively exploring an obstacle space using diverse ways of touching an object. Conventional navigation models are typically based on the avoidance of obstacles, i.e., avoiding collision. However, actual disordered space may be full of various kinds of obstacles. To reach a destination in such a space, a robot requires an active approach for avoiding a deadlock with obstacles or changing the obstacle configuration to find an open space using diverse ways of touching an object. We solved this problem by generating locally diverse moving patterns by using an action model with rhythmical oscillation in addition to a localization model using a particle filter. The proposed model was demonstrated to be effective through an experiment where a robot navigated to a destination behind partially movable obstacles using rhythmical active touch.