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 robot's RFID readers are near the center of the robot's rotation because the reader could not detect enough tags by rotating movements when the robot's 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.

Wabot-House Research Laboratory is working on a project that will enable integrating robots into our everyday life. We believe that a “structured environment” (SE) will be one of most important concepts for this project. An SE generally means that objects near people that have some database or intelligence provide certain information to those people. An SE will also assist robot recognition or movement planning. Now, we focus on a global positioning system (GPS), which is a global SE that gives users or robots their positions whenever and wherever they are outdoors all over the world. GPS will strongly support robot self-positioning. However, GPS has the problem that it cannot be used when the robots are indoors. To solve this problem, we experimentally mounted four pseudolites (‘pseudo’ means imitated and ‘lite’ means satellite) in our laboratory and developed indoor GPS. The system worked well unless the robot was near the wall, where cycle slip often occurred. To examine the characteristics and reason for cycle slip, we measured the radio-wave environment in the laboratory. The first half of this paper introduces this system. The last reports results and findings about this experiment.

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.

Occlusion is a problem for range finders; ranging systems using cameras or lasers cannot be used to estimate distance to an object (hidden object) that is occluded by another (obstacle). We developed a method to estimate the distance to the hidden object by applying acoustic diffraction of audible sound. Our method is based on time-of-flight (TOF), which has been used in ultrasound ranging systems. We determined the best frequency of audible sound and designed its optimal modulated signal for our system. We determined that the system estimates the distance to the hidden object as well as the obstacle. However, the measurement signal obtained from the hidden object was weak. Thus, interference from sound signals reflected from other objects or walls was not negligible. Therefore, we combined acoustical holography (AH) and TOF, which enabled a partial analysis of the reflection sound intensity field around the obstacle and hidden object. Our method was effective for ranging two objects of the same size within a 1.2 m depth range. The accuracy of our method was 3 cm for the obstacle, and 6 cm for the hidden object.

This paper describes a new acoustic localization method using audible sound, which can be applied over a broader range of search directions. In the field of robotics, most conventional indoor localization systems based on sonar range finders use ultrasound to obtain a highly accurate distance. Because ultrasound has high directivity, many measurements are required to localize objects in a large space. To achieve localization with one-time measurement, we use audible sounds. We then calculate an intensity field of the reflection sound to estimate object positions. Although acoustical holography (AH) is a well-known technique to do this, it has problems in that it generates false images. We propose multiple AH (MAH) to solve this problem. The method is used to divide a measurement plane into sub-planes and to apply AH to each sub-plane. By integrating the results of applying AH to the sub-planes, false images can be suppressed because the positions of the false images differ depending on the position of the sub-plane. In addition, we use multiple frequencies to advance an accuracy of the localization based on MAH in a real environment. We constructed a localization system with only one speaker and a microphone array. In both simulation and actual experiments, we confirmed that MAH was effective method for the suppression of false image and could be used within the range of an angle view of 120 deg