Magnetic fields are used for localization and navigation in the field of robotics. In recent years, because of the spread of mobile devices equipped with magnetic sensors (e.g., smart phones), the use of magnetic fields has been extensive, especially for position tracking of mobile devices. One example application of such tracking is in identifying the position of a person with a mobile device. Development of this application requires a three-dimensional (3D) magnetic map that represents the magnetic distribution of a 3D environment since the device moves around in 3D space. It is, however, difficult to construct a 3D magnetic map of a large-scale environment because measuring the magnetic field is time consuming and expensive. In this paper we propose an efficient method for mapping the 3D magnetic field of a large-scale environment. The method uses a mobile manipulator to measure the 3D magnetic field, enabling 3D magnetic data to be automatically collected. Moreover, the method uses Gaussian processes (GPs) for regression of the magnetic field. In this study, we first evaluate the performance of the GPs and then describe the measurement robot. In an experiment, a 3D magnetic field of an indoor environment is visualized by using this method and the performance of the presented method is demonstrated.

The magnetic field that exists in an indoor environment includes rich magnetic fluctuations because buildings contain many magnetized materials (e.g., steel frames). These fluctuations can be used as landmarks, the use of which requires the creation of a magnetic map representing the distribution of the magnetic field. It is, however, difficult to build a large-scale magnetic map because of the narrow measurement range of a magnetic sensor. This paper proposes an efficient method for collecting magnetic data using a mobile robot and a method for building a magnetic map using Gaussian processes. The use of these methods make it possible to build a large-scale magnetic map efficiently. Moreover, this paper presents a particle filter-based localization method based on the magnetic map. The presented system enables a robot to identify its own position in large-scale buildings. Experiments are used to demonstrate the performance and usefulness of the presented system.

This paper presents a play-back navigation for outdoor mobile robots using trajectory tracking method based on environmental magnetic field. Two 3-axis magnetic sensors with horizontal xy-axes opposed to each other, are used to scan magnetic field occurred in the environment to build two different magnetic databases during the teaching stage. In the play-back navigation, the robot rotates 180 degrees and then uses one among the two magnetic sensors readings to perform trajectory tracking based on the magnetic database of another magnetic sensor. The experimental results show that the robot is able to perform the play-back navigation with a reliable accuracy.

This paper describes a trajectory tracking method using environmental magnetic field for outdoor autonomous mobile robots. In this research, a 3-axis magnetic sensor is used to scan DC magnetic field in the outdoor environment to build a database. The robot then performs trajectory tracking based on the database. The experimental results show that by applying the proposed method the robot is possibly able to navigate in the outdoor environment with a reliable accuracy.

As a flexible and robust intelligent robot system, we have been developing an autonomous and decentralized robot system called ACTRESS, which is composed of multiple robotic agents who have various kinds of functionalities. In order to achieve a given mission in multirobot environment, cooperative behaviors by autonomous agents are essential. In this paper, we focus on the case that multiple robots execute a task cooperatively with functional complement, especially for sensing function. We discuss cooperation of multiple robots using communication in a multi-agent robotic system, and propose a method of functional complement by multiple agents, including group organization and cooperative motion control. Finally, we show the experimental result to verify the proposed method.>

As a flexible and robust intelligent robot system, we have been developing an autonomous and decentralized robot system called ACTRESS, which is composed of multiple robotic agents. In this paper, introducing an idea of cooperative action with group organization and a strategy for cooperative task processing using communication, a method for team organization by efficient negotiation is presented. The efficient negotiation is realized by a newly developed communication functionality called groupcast and a learning mechanism utilizing historical records on past negotiation. The negotiation procedures for team organization are implemented, and as a result of simulation experiments, the efficiency of the learning mechanism is verified.>

This paper addresses a method for synchronizing motion by multiple autonomous mobile robots using radio communication. The autonomous and decentralized robot system, ACTRESS, is a multi-agent robotic system including multiple autonomous mobile robots. The autonomous mobile robots are developed so as not only to act individually, but also to cooperate with the other robots if they cannot accomplish the task alone. The synchronized motion is sometimes required to execute such cooperative tasks. Every agent of ACTRESS is equipped with the communication system, which enables a message passing between any robotic agents. The prototype ACTRESS system is introduced, and cooperation among multiple robotic agents is discussed. A method for synchronization using the communication system is proposed. The method is applied to the prototype system, and side-by-side motion on the actual mobile robots is implemented as a example of synchronized motion.

An autonomous and decentralized robot system, ACTRESS, is being developed as an intelligent robot system which can execute high level tasks. The concept of ACTRESS is introduced, and the communication framework between robotic agents that compose the robot system is proposed. Taking a task of pushing objects by multiple mobile robots in a certain working environment as a case study of ACTRESS, the strategy for organizing the robot system is discussed, and the required functions for the robot system are introduced. The optimal distribution of the functions is designed based on communication evaluation. An algorithm for static path planning and a strategy for dynamic path planning are proposed.>

An autonomous and decentralized robot system, ACTRESS, is being developed as an intelligent robot system which is composed of multiple robotic agents. The basic concept and strategy for path planning in a dynamically changing environment with multiple autonomous mobile robots are introduced. Collision avoidance with dynamic path planning according to the complexity of the situations is also discussed. A method for collision avoidance based on rules and negotiation by communication is presented. Finally, this method is applied to a developed prototype system consisting of two mobile robots, and the result of experiments operating on real robots is given.>