Performing disaster-drills before emergencies is an effective way to mitigate disasters. With technological developments, new evacuation systems are proposed in prevention planning. In recent times, digital promotion signage is being used recently for promotion and advertising in public and commercial spheres. Our questionnaire survey indicates that the signage attracts more attention than the traditional emergency-exit sign. We propose using the signage model to control human evacuation flow during emergencies. The new guidance system is based on a questionnaire survey and the effects of the guidance system are simulated using an agent-based simulation system. This paper also presents a discussion based on the experiment results and proposes future methods of evacuation guidance using the knowledge obtained from simulation results.

Since the first demonstration of the Virtual Robot Competition, USARSim has been used as the simulation interface and environment. The underlying simulation platform, Unreal Engine, has seen three major upgrades (UT2004, UT3 and UDK). These upgrades required a whole new USARSim simulator to be built from scratch. Yet, between those versions the USARSim interface has not been modified, which made USARSim a stable platform for more than 10 years. This stability allowed developers to concentrate on their control and perception algorithms. This paper describes a new prototype of the USARSim interface; implemented as plugin to Gazebo, the simulation environment native to ROS. This plugin would facilitate a shift of the maintenance of the simulation environment to the Open Source Robotics foundation and attract new teams to the Virtual Robot Competition. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

One of the main requisites for multi agent researchers is a multi-purpose infrastructure that enables them to create, test and evaluate protocols, methods and algorithms. RoboCupRescue simulation environment is a flexible and configurable environment that can fill this gap. With changes in its configuration files, it will gain different features, so different methods can be designed, implemented, and tested. This environment is actually suitable for most multi-agent research issues such as cooperation, coordination, agent communication languages, and agent micro level issues. Sensible roles and tasks in the system motivate more research and make this environment a suitable infrastructure for educational purposes. Arian is a set of implemented agents for RoboCupRescue system. Using Arian agents will speed up implementing agents for this environment. In this paper, we introduce RoboCupRescue simulation environment and Arian agents along with some examples of research issues in this environment. We also present some of RoboCupRescue sub-problems for specific research goals. Although RoboCupRescue can be considered as an application of multi-agent research, in this paper we focus on the suitability of this environment as an infrastructure for multi-agent research and education.

This paper introduces the RoboCup-Rescue Simulation Project, a contribution to the disaster mitigation, search and rescue problem. A comprehensive urban disaster simulator is constructed on distributed computers. Heterogeneous intelligent agents such as fire fighters, victims and volunteers conduct search and rescue activities in this virtual disaster world. A real world interface integrates various sensor systems and controllers of infrastructures in the real cities with the virtual world. Real-time simulation is synchronized with actual disasters, computing complex relationship between various damage factors and agent behaviors. A mission-critical man-machine interface provides portability and robustness of disaster mitigation centers, and augmented-reality interfaces for rescue parties in real disasters. It also provides a virtual reality training function for the public. This diverse spectrum of RoboCup-Rescue contributes to the creation of the safer social system.

The RoboCup Rescue project aims to simulate large urban disasters and rescue agents' activities. The simulator must support both simulation of heterogeneous agents such as fire fighters, victims' behaviors and interface to disaster's environments in the real world. The RoboCup Rescue simulator is a comprehensive urban disaster simulator into which a new disasters simulator or rescue agents can be easily plugged. In the paper, the simulator's specification based on the Hanshin-Awaji Earthquake and the simulator's architecture are described.

Methods have been proposed to generate robot motions from human demonstrations. Humans demonstrate tasks without indicating what they want to teach. In future, the robot programming methods from human demonstrations will notice the intention of the human's actions and generate or modify the program from demonstrations. This paper describes two elements which are necessary in analyzing human demonstrations. The first is a time normalization method of demonstration data which humans show at different speeds. The second is a method to detect various changes in data without prior knowledge of how many or what kind of changes will occur. The scheme of robot programming from demonstrations is presented and the wavelet analysis result of time normalized demonstration data are discussed.

A method is proposed for changing contact states during robotic assembly that is based on a multi-agent system. The agents that have its own model of the local contact state transition between corners of objects are prepared. Using its own model, each local agent individually senses the contact state, plans the motion for contact state transition, and suggests its motion to another agent. This agent integrates these suggestions and determines the robot motion for changing contact states. Without revising the model according to object shapes, our method determines the robot motion for changing contact states by changing the number of local agents. Simulation of single-peg and dual-peg insertion confirmed the effectiveness of this method.

The ability to execute similar tasks in various environments is important for robot systems, but there are few approaches for doing this. We recently proposed a "teaching by showing" system that recognizes teaching tasks and executes corresponding tasks in different environments. The teaching task is recognized from geometrical constraint and translated into a general task description (GTD). However, this method is difficult to apply in complex environments because of redundancy an description and lack of generalization. This paper presents an algorithm for both two- and three-dimensional environments that solves these problems. The improved GTD describes tasks in complex environments very compactly. It ignores objects that do not affect the task, which adds a useful robustness to the system.>

A "teaching by showing" system using a graphic interface is proposed. This system provides: 1) a user interface with which an inexperienced operator can easily teach a task, and 2) a task execution system that can operate the task in a different environment. The operator shows an example of assembly task movements in the virtual environmental created in the computer. A finite automaton dependently defined task is used to interpret the movements as a sequence of high-level representations of operations. When the system is commanded to operate the learning task, the system observes the task environment, checks the geometrical feasibility of the task in the environment, and if necessary replans the sequence of operations so that the robot can complete the task. Then the system uses task-dependent interpretation rules to translate the sequence of operations into manipulator-level commands and executes the task by replicating the operator's movement in the virtual environment.>

The authors propose a robot teaching interface which uses virtual reality. The teaching method provides a user interface with which a novice operator can easily direct a robot. The operator performs the assembly task in a virtual workspace generated by a computer. The operator's movements are recognized as robot task-level operations by using a finite automation. The system interprets the recognized operations into manipulator-level commands using task-dependent interpretation rules and a world model. A robot executes the assembly task in the workplace by replicating the operator's movements in the virtual workspace.>

The authors propose a robot teaching method which uses virtual reality. An operator wearing a VPL DataGlove performs a task in a virtual workspace which simulates a real workspace. The operator's movements are recognized that interpreted task-dependently using interpretation rules and a world model. A robot executes the task in the real workspace using sensors. The overall system architecture and experiments are presented.>