7 research outputs found

    General Concepts for Human Supervision of Autonomous Robot Teams

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    For many dangerous, dirty or dull tasks like in search and rescue missions, deployment of autonomous teams of robots can be beneficial due to several reasons. First, robots can replace humans in the workspace. Second, autonomous robots reduce the workload of a human compared to teleoperated robots, and therefore multiple robots can in principle be supervised by a single human. Third, teams of robots allow distributed operation in time and space. This thesis investigates concepts of how to efficiently enable a human to supervise and support an autonomous robot team, as common concepts for teleoperation of robots do not apply because of the high mental workload. The goal is to find a way in between the two extremes of full autonomy and pure teleoperation, by allowing to adapt the robots’ level of autonomy to the current situation and the needs of the human supervisor. The methods presented in this thesis make use of the complementary strengths of humans and robots, by letting the robots do what they are good at, while the human should support the robots in situations that correspond to the human strengths. To enable this type of collaboration between a human and a robot team, the human needs to have an adequate knowledge about the current state of the robots, the environment, and the mission. For this purpose, the concept of situation overview (SO) has been developed in this thesis, which is composed of the two components robot SO and mission SO. Robot SO includes information about the state and activities of each single robot in the team, while mission SO deals with the progress of the mission and the cooperation between the robots. For obtaining SO a new event-based communication concept is presented in this thesis, that allows the robots to aggregate information into discrete events using methods from complex event processing. The quality and quantity of the events that are actually sent to the supervisor can be adapted during runtime by defining positive and negative policies for (not) sending events that fulfill specific criteria. This reduces the required communication bandwidth compared to sending all available data. Based on SO, the supervisor is enabled to efficiently interact with the robot team. Interactions can be initiated either by the human or by the robots. The developed concept for robot-initiated interactions is based on queries, that allow the robots to transfer decisions to another process or the supervisor. Various modes for answering the queries, ranging from fully autonomous to pure human decisions, allow to adapt the robots’ level of autonomy during runtime. Human-initiated interactions are limited to high-level commands, whereas interactions on the action level (e. g., teleoperation) are avoided, to account for the specific strengths of humans and robots. These commands can in principle be applied to quite general classes of task allocation methods for autonomous robot teams, e. g., in terms of specific restrictions, which are introduced into the system as constraints. In that way, the desired allocations emerge implicitly because of the introduced constraints, and the task allocation method does not need to be aware of the human supervisor in the loop. This method is applicable to different task allocation approaches, e. g., instantaneous or time-extended task assignments, and centralized or distributed algorithms. The presented methods are evaluated by a number of different experiments with physical and simulated scenarios from urban search and rescue as well as robot soccer, and during robot competitions. The results show that with these methods a human supervisor can significantly improve the robot team performance

    Path planning for first responders in the presence of moving obstacles

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    Navigation services have gained much importance for all kinds of human activities ranging from tourist navigation to support of rescue teams in disaster management. However, despite the considerable amount of route guidance research that has been performed, many issues that are related to navigation for first responders still need to be addressed. During disasters, emergencies can result in different types of moving obstacles (e.g., fires, plumes, floods), which make some parts of the road network temporarily unavailable. After such incidents occur, responders have to go to different destinations to perform their tasks in the environment affected by the disaster. Therefore they need a path planner that is capable of dealing with such moving obstacles, as well as generating and coordinating their routes quickly and efficiently. During the past decades, more and more hazard simulations, which can modify the models with incorporation of dynamic data from the field, have been developed. These hazard simulations use methods such as data assimilation, stochastic estimation, and adaptive measurement techniques, and are able to generate more reliable results of hazards. This would allow the hazard simulation models to provide valuable information regarding the state of road networks affected by hazards, which supports path planning for first responders among the moving obstacles. The objective of this research is to develop an integrated navigation system for first responders in the presence of moving obstacles. Such system should be able to navigate one or more responders to one or multiple destinations avoiding the moving obstacles, using the predicted information of the moving obstacles generated from by hazard simulations. In this dissertation, the objective we have is expressed as the following research question: How do we safely and efficiently navigate one or more first responders to one or more destinations avoiding moving obstacles? To address the above research questions, this research has been conducted using the following outline: 1). literature review; 2). conceptual design and analysis; 3). implementation of the prototype; and 4). assessment of the prototype and adaption. We investigated previous research related to navigation in disasters, and designed an integrated navigation system architecture, assisting responders in spatial data storage, processing and analysis.Within this architecture, we employ hazard models to provide the predicted information about the obstacles, and select a geo-database to store the data needed for emergency navigation. Throughout the development of the prototype navigation system, we have proposed: a taxonomy of navigation among obstacles, which categorizes navigation cases on basis of type and multiplicity of first responders, destinations, and obstacles; a multi-agent system, which supports information collection from hazard simulations, spatio-temporal data processing and analysis, connection with a geo-database, and route generation in dynamic environments affected by disasters; data models, which structure the information required for finding paths among moving obstacles, capturing both static information, such as the type of the response team, the topology of the road network, and dynamic information, such as changing availabilities of roads during disasters, the uncertainty of the moving obstacles generated from hazard simulations, and the position of the vehicle; path planning algorithms, which generate routes for one or more responders in the presence of moving obstacles. Using the speed of vehicles, departure time, and the predicted information about the state of the road network, etc., three versions (I, II, and III) of Moving Obstacle Avoiding A* (MOAAStar) algorithms are developed: 1). MOAAstar– I/Non-waiting, which supports path planning in the case of forest fires; 2). MOAAstar–II/Waiting, which introduces waiting options to avoid moving obstacles like plumes; 3). MOAAstar–III/Uncertainty, which can handle the uncertainty in predictions of moving obstacles and incorporate the profile of responders into the routing. We have applied the developed prototype navigation system to different navigation cases with moving obstacles. The main conclusions drawn from our applications are summarized as follows: In the proposed taxonomy, we have identified 16 navigation cases that could occur in disaster response and need to be investigated. In addressing these navigation problems, it would be quite useful to employ computer simulations and models, which can make reliable predicted information about responders, the targets, and obstacles, in finding safe routes for the responders. The approach we provide is general and not limited to the cases of plumes and fires. In our data model, the data about the movement of hazards is represented as moving polygons. This allows the data model to be easily adjusted to merge and organize information from models of different types of disasters. For example, the areas that are affected by floods can also be represented as moving polygons. To facilitate the route calculation, not only the data of obstacles but also the information about the state of road networks affected by obstacles need to be structured and stored in the database. In planning routes for responders, the routing algorithms should incorporate the dynamic data of obstacles to be able to avoid the hazards. Besides, other factors, such as the operation time of tasks, the required arrival time, and departure time, also need to be considered to achieve the objectives in a rescue process, e.g., to minimize the delays caused by the moving obstacles. The profile of responders is quite important for generation of feasible routes for a specific disaster situation. The responders may have different protective equipment that allows them to pass through different types of moving obstacles, and thus can have different classification of risk levels to define the state of the road network. By taking into account the profile of the responders, the navigation system can propose customized and safe routes to them, which would facilitate their disaster response processes. On the basis of our findings, we suggest the following topics for future work: As presented Wang and Zlatanova (2013c), there are still a couple of navigation cases that need to be addressed, especially the ones that involve dynamic destinations. More algorithms would be needed to solve these navigation problems. Besides, some extreme cases (e.g., the obstacle covers the target point during the course of an incident) also need to be investigated. Using standard Web services, an Android navigation application, which can provide navigation services in the environment affected by hazards, needs to be developed and tested in both the daily practice and real disasters. In this application, a user interface with various styling options should also be designed for different situations, e.g., waiting and moving, day and night, and urgent and non-urgent. Because the communication infrastructure may not be available or work properly during a disaster response, a decentralized method is needed to allow different users to negotiate with each other and to make local agreements on the distribution of tasks in case there is no support from the central planning system. Another type of multi-agent system would be needed to handle this situation. Introduce variable traveling speed into the re-routing process. The vehicle speed plays an important role in generation of routes avoiding moving obstacle, and can be influenced by many factors, such as the obstacles, the type of vehicles, traffic conditions, and the type of roads. Therefore, it would be needed to investigate how to derive the current and future speed from trajectories of vehicles. Apply the system to aid navigation in various types of natural disasters, using different hazard simulation models (e.g., flood model). More types of agents would be needed and integrated into the system to handle heterogeneous data from these models. Extensions of the data model are also required to meet a wider range of informational needs when multiple disasters occur simultaneously

    Mecanismos de cooperación en robots como agentes móviles

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    Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, octubre de 201

    Relevanzbasierte Informationsbeschaffung für die informierte Entscheidungsfindung intelligenter Agenten

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    This dissertation introduces relevance-based information acquisition for intelligent software agents based on Howard s information value theory and decision networks. Active information acquisition is crucial in domains with partial observability in order to establish situation awareness of autonomous systems for deliberate decisions. The new semi-myopic approach addresses the complexity challenge of decision-theoretic relevance computation by reducing the set of variables to be evaluated in the first place. Links in a decision network encode stochastic dependencies of variables. Through utility dependency analysis using Pearl s d-separation criterion, the set of relevant variables can be efficiently reduced to a proven minimum without actually computing information value. In addition to an implementation with detailed runtime performance analysis, the applicability of the approach is shown in the domain of intelligent logistics control
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