In this paper, we present a new approach to the problem of simultaneously localizing a group of mobile robots capable of sensing one another. Each of the robots collects sensor data regarding its own motion and shares this information with the rest of the team during the update cycles. A single estimator, in the form of a Kalman filter, processes the available positioning information from all the members of the team and produces a pose estimate for every one of them. The equations for this centralized estimator can be written in a decentralized form, therefore allowing this single Kalman filter to be decomposed into a number of smaller communicating filters. Each of these filters processes the sensor data collected by its host robot. Exchange of information between the individual filters is necessary only when two robots detect each other and measure their relative pose. The resulting decentralized estimation schema, which we call collective localization, constitutes a unique means for fusing measurements collected from a variety of sensors with minimal communication and processing requirements. The distributed localization algorithm is applied to a group of three robots and the improvement in localization accuracy is presented. Finally, a comparison to the equivalent decentralized information filter is provided.

Distributed multirobot localization

We present a distributed algorithm for converging autonomous mobile robots with limited visibility toward a single point. Each robot is an omnidirectional mobile processor that repeatedly: 1) observes the relative positions of those robots that are visible; 2) computes its new position based on the observation using the given algorithm; 3) moves to that position. The robots' visibility is limited so that two robots can see each other if and only if they are within distance V of each other and there are no other robots between them. Our algorithm is memoryless in the sense that the next position of a robot is determined entirely from the positions of the robots that it can see at that moment. The correctness of the algorithm is proved formally under an abstract model of the robot system in which: 1) each robot is represented by a point that does not obstruct the view of other robots; 2) the robots' motion is instantaneous; 3) there are no sensor and control error; 4) the issue of collision is ignored. The results of computer simulation under a more realistic model give convincing indication that the algorithm, if implemented on physical robots, will be robust against sensor and control error.

Distributed memoryless point convergence algorithm for mobile robots with limited visibility

A method and system is provided for detecting, commanding and controlling diverse home devices currently connected to a home network. An interface is provided for accessing the home devices that are currently connected to a home network. According to the method, a device link file is generated, wherein the device link file identifies home devices that are currently connected to the home network. A device link page is created, wherein the device link page contains a device button that is associated with each home device that is identified in the device link file. A hyper-text link is associated with each device button, wherein the hyper-text link provides a link to an HTML page that is contained on the home device that is associated with the device button, and the device link page is displayed on a browser based home device.

Method and apparatus for a home network auto-tree builder

Within the context of multiple mobile, and networked robot systems, this chapter explores the current state of the art. After a brief introduction, we first examine architectures for multirobot cooperation, exploring the alternative approaches that have been developed. Next, we explore communications issues and their impact on multirobot teams in Sect. 53.3, followed by a discussion of networked mobile robots in Sect. 53.4. Following this we discuss swarm robot systems in Sect. 53.5 and modular robot systems in Sect. 53.6. While swarm and modular systems typically assume large numbers of homogeneous robots, other types of multirobot systems include heterogeneous robots. We therefore next discuss heterogeneity in cooperative robot teams in Sect. 53.7. Once robot teams allow for individual heterogeneity, issues of task allocation become important; Sect. 53.8 therefore discusses common approaches to task allocation. Section 53.9 discusses the challenges of multirobot learning, and some representative approaches. We outline some of the typical application domains which serve as test beds for multirobot systems research in Sect. 53.10. Finally, we conclude in Sect. 53.11 with some summary remarks and suggestions for further reading.

Multiple Mobile Robot Systems

This paper presents a design method and experiments for whole-body locomotion by a modular robot. There are two types of locomotion for modular robots: a repeating self-reconfiguration and whole-body motion such as walking or crawling. For whole-body locomotion, designing a control method is more difficult than for ordinary robots because a modular robotic system can form various configurations, each of which has many degrees of freedom. This study proposes a unified framework for automatically designing an efficient locomotion controller suitable for any module configuration. The method utilizes neural oscillators (central pattern generators, CPGs), each of which works as a distributed joint controller of each module, and a genetic algorithm to optimize the CPG network. We verified the method by software simulations and hardware experiments, in which our modular robotic system, named M-TRAN II, performed stable and effective locomotion in various configurations.

Automatic locomotion design and experiments for a Modular robotic system

This paper presents a novel concept of self-organizing collective robots with morphogenesis in a vertical plane. It is potentially applicable to autonomous mobile robots. For physical reconfiguration of a swarm of robots against gravity, new types of mechanisms and control strategies are proposed and demonstrated. Prototype robots have been fabricated in order to confirm the basic feasibility of the mechanisms. Each robot is composed of a body and a pair of arms. The body can be regarded as a cube with edge length of 90 mm, and is equipped with permanent magnets for bonding with another robot. The arms change the bonding configuration by rotating and sliding motions. As for the control strategies, we proposed the algorithms which can locally generate specific global formations of robots, with minimum interactions between neighboring robots. The overall scheme is similar to cellular automata. The control algorithms proposed have been tested by simulations.

Self-organizing collective robots with morphogenesis in a vertical plane

A concept of distributed autonomous robotic systems (DARS) attracts many researchers' interests as one of the possible solutions which could realize flexible, robust and intelligent robotic systems. However, it can be observed that it is not possible for robots to carry out all the high-level tasks by themselves. A human operator should somehow operate the robotic system according to the requirements for the tasks. We have developed a human interface system for DARS. In this paper, a framework of human interface system for teleoperation is examined to clarify the requirements for those systems which require a single operator to operate multiple robots from a distant place. A prototype of the teleoperation system using the Internet as a medium for information transfer is developed and implemented onto an actual testing platform which consists of multiple omnidirectional mobile robots with cameras.

Teleoperation of multiple robots through the Internet

We discuss adaptive behavior acquisition for collision avoidance among multiple autonomous mobile robots which are equipped with the locally communicable infrared sensory system (LOCISS). The LOCISS is a local sensing device for collision avoidance by which robots can detect other robots and obstacles and discriminate them by exchanging relevant information. We (1996) reported previously a collision avoidance method between two robots based on the predetermined rules using LOCISS. It is, however, difficult to realize collision avoidance among three or more robots by the predetermined rules only because situations around the robots become more complicated as the number of robots increases. Thus, it is desirable for the robots to have an adaptive capability for acquisition of the behaviors to avoid collision with other robots and obstacles. To acquire the adaptive behavior, the reinforcement learning is introduced in this paper. It is shown that appropriate behaviors for collision avoidance can be successfully acquired through the proposed learning process.

Adaptive behavior acquisition of collision avoidance among multiple autonomous mobile robots

In this paper, we discuss autonomous navigation in multirobot environment based on local communication. To carry out cooperative tasks using multiple autonomous robots, navigation systems should be implemented to the robots because mutual alignment of their positions and collision avoidance to each other become important. Therefore, it seems a collision avoidance and a self-localization are indispensable as elemental techniques for the navigation. Although these techniques need to recognize situation of robots surrounding environment, conventional devices to acquire information around them can not work sufficiently in such environment. For this problem, we propose techniques based on the local communication and the navigation system by combining them for multirobot environment. It is shown that robots can arrive their goal precisely without collision among them through proposed navigation systems.

Realization of autonomous navigation in multirobot environment

This paper proposes a new strategy for knowledge sharing among multiple autonomous mobile robots using newly developed intelligent data carriers (IDCs) to make the multi-robot system more flexible and robust against complex environmental conditions and, to reduce the amount of global communication for mutual exchange of information which is useful locally in a limited area. The IDC is an immobile but portable device (data carrier) for information storage and management with a unique mushroom-like structure to be easily handled by a forklift mechanism which can be mounted on actual mobile robots. A robot can place a unit at a specific location in its working environment and write locally relevant knowledge into the unit. Other robots can share the knowledge which should indicate the existence of specific instruments, objects or topographies, e.g., dead ends, drop-offs, steps, etc., by reading from the placed IDC units. The handling method of the IDC unit is examined through the experiment using an omni-directional mobile robot. It is shown that the robot succeeded in placing the unit at an appropriate location to share the information indicating that a dead end exists.

T. Fujii

Papers

Self-organizing collective robots with morphogenesis in a vertical plane

Teleoperation of multiple robots through the Internet

Adaptive behavior acquisition of collision avoidance among multiple autonomous mobile robots

Realization of autonomous navigation in multirobot environment

Knowledge sharing among multiple autonomous mobile robots through indirect communication using intelligent data carriers