Naotaka Hatao

System integration of a daily assistive robot and its application to tidying and cleaning rooms

This paper describes a software system integration of daily assistive robots. Several tasks related to cleaning and tidying up rooms are focused on. Recognition and motion generation functions needed to perform daily assistance are developed, and these functions are used to design various behaviors involved in daily assistance. In our approach, the robot behaviours are divided into simple units which consist of 3 functions as check/plan/do, it provides us with high reusable and flexible development environment. Because sequential task execution can be achieved only after functions about failure detection and recovery, we also try to implement such functions in keeping with this approach. In addition to using simple behavior unit, multilayer error handling is effective. Experiments doing several daily tasks with handling daily tools showed the effectiveness of our system.

Nested iGMM recognition and multiple hypothesis tracking of moving sound sources for mobile robot audition

The paper proposes two modules for a mobile robot audition system: 1) recognizing surrounding acoustic event, 2) tracking moving sound sources. We propose nested infinite Gaussian mixture model (iGMM) for recognizing frame based feature vectors. The main advantage is that the number of classes is allowed to increase without bound, if necessary, to represent unknown audio input. The multiple hypothesis tracking module provides time-series of separated audio stream using localized directions and recognition results at each frame. Not only for continuous sounds, the proposed tracker automatically detects appearing and disappearing point of stream from multiple hypothesis. These two modules are connected to microphone array based sound localization and separation, and the combined robot audition system achieved tracking of multiple moving sounds including intermittent sound source.

Outdoor 3D map generation based on planar feature for autonomous vehicle navigation in urban environment

This paper describes a 3D textured map generation method for autonomous vehicle in urban outdoor environment, where GPS signals can not be reached. Constructed map will be used for short cycle and accurate localization and for obstacle detection using onbody laser scanner.

Real-time navigation for a personal mobility in an environment with pedestrians

This paper describes a navigation system in a dynamic environment for a two-wheeled inverted pendulum mobile robot, PMR. Our system is organized by localization, detection and tracking of pedestrians, and trajectory planner. The localization is robust to effects of moving obstacles and pitching movements of the robot, and the trajectory planner creates a path with a certain smoothness considering movements of pedestrians. In addition, the planner introduces strategies to avoid pedestrians to be friendly to pedestrians around the robot. Besides, our system can run on two laptop PCs in real time. Finally, we show experimental results as well.

Tidying and cleaning rooms using a daily assistive robot

This paper describes a system integration of daily assistive robots. Several tasks related to cleaning and tidying up rooms are focused on. Recognition and motion generation functions were integrated onto the robot, and these provided failure detection and in some cases, its recovery. Experiments of several daily tasks handling daily tools showed the effectiveness of our system.

Building Environmental Maps of Human Activity for a Mobile Service Robot at the ”Miraikan” Museum

This paper describes environmentmaps that are comprised of the following three types of information, 1) 3D environmental changes that represents human activities, 2) human trajectories in 2D that represent how humans move in the environment, and 3) human posture data. These maps are utilized in order to plan safer, quicker and/or non-human-disturbingpaths for a mobile service robot at themuseum “Miraikan”. Experiments are conducted within “Miraikan” and results are shown.

Pedestrian detection using a LRF and a small omni-view camera for outdoor personal mobility robot

This paper describes a pedestrian detection method using a LRF and a small omni-view camera. In outdoor environment, the resolutions of LRFs are too low to recognize human reliably, and high resolution image requires high calculation cost for detecting walking persons. We propose a combination approach using these data. Particle filter based tracking and HOG (Histogram of Oriented Gradients) feature based identification enables to detect pedestrians with high reliability and effectiveness. Although the pedestrian identification from omni-view image needs computational effort for searching large area, LRF based tracking provides the identification process with limited interest regions in advance. We also reports how to construct a discriminative function which is able to cope with various resolution images. Finally, the proposed method was combined with gesture recognition and other robot system, and then an application to taxi service is introduced. The robot could find a person standing 15 meter away, recognized his swinging hand, and moved to the front of him.

Autonomous mapping and navigation system in rooms and corridors for indoor support robots

In this research, the mapping and navigation system in a wide space in a building for mobile robots which have laser range finders and stereo cameras was achieved. The map was described as a combination of topological corridor maps and metric room maps. The topological corridor maps have nodes which represent entrances of each room, and the grid-based room maps have accurate information about positions of obstacles. Using this mapping method, we achieved the system of indoor support humanoid which can perform several tasks with long-distance movement.

Cluster-Based SJPDAFs for Classification and Tracking of Multiple Moving Objects

This paper describes a method for classifying and tracking multiplemoving objects with a laser range finder (LRF). As moving objects are tracked in the framework of sample-based joint probabilistic data association filters (SJPDAFs), the proposed method is robust against occlusions or false segmentation of LRF scans. It divides tracking targets and corresponding LRF segments into clusters and able to classify each cluster as a car or a group of pedestrians. In addition, it can correct false segmentation of LRF scans. We implemented the proposed method and obtained experimental results demonstrating its effectiveness in outdoor environments and crowded indoor environments.

Construction of Semantic Maps for Personal Mobility Robots in Dynamic Outdoor Environments

In this paper, a construction system of outdoor semantic maps by personal mobility robots that move in dynamic outdoor environments is proposed. The maps have topological forms based on understanding of road structures. That is, the nodes of maps are intersections, and arcs are roads between each pair of intersections. Topological framework significantly reduces computer resources, and enables consistent map building in environments which include loops. Trajectories of moving objects, landmarks, entrances of buildings, and traffic signs are added along each road. This framework enables personal mobility robots to recognize dangerous points or regions. The proposed system uses two laser range finders (LRFs) and one omni-directional camera. One LRF is swung by a tilt unit, and reconstruct 3D shapes of obstacles and the ground. The other LRF is fixed on the body of the robot, and is used for moving objects detection and tracking. The camera is used for localization and loop closings. We implemented the proposed system in a personal mobility robot, and demonstrated its effectiveness in outdoor environments.