Cipriano Galindo

Robot task planning using semantic maps

Task planning for mobile robots usually relies solely on spatial information and on shallow domain knowledge, such as labels attached to objects and places. Although spatial information is necessary for performing basic robot operations (navigation and localization), the use of deeper domain knowledge is pivotal to endow a robot with higher degrees of autonomy and intelligence. In this paper, we focus on semantic knowledge, and show how this type of knowledge can be profitably used for robot task planning. We start by defining a specific type of semantic maps, which integrates hierarchical spatial information and semantic knowledge. We then proceed to describe how these semantic maps can improve task planning in two ways: extending the capabilities of the planner by reasoning about semantic information, and improving the planning efficiency in large domains. We show several experiments that demonstrate the effectiveness of our solutions in a domain involving robot navigation in a domestic environment.

Mobile robot localization based on Ultra-Wide-Band ranging: A particle filter approach

This article addresses the problem of mobile robot localization using Ultra-Wide-Band (UWB) range measurements. UWB is a radio technology widely used for communications, that is recently receiving increasing attention for positioning applications. In these cases, the position of a mobile transceiver is determined from the distances to a set of fixed, well-localized beacons. Though this is a well-known problem in the scientific literature (the trilateration problem), the peculiarities of UWB range measurements (basically, distance errors and multipath effects) demand a different treatment to other similar solutions, as for example, those based on laser. This work presents a thorough experimental characterization of UWB ranges within a variety of environments and situations. From these experiments, we derive a probabilistic model which is then used by a particle filter to combine different readings from UWB beacons as well as the vehicle odometry. To account for the possible offset error due to multipath effects, the state tracked by the particle filter includes the offset of each beacon in addition to the planar robot pose (x,y,@f), both estimated sequentially. We show navigation results for a robot moving in indoor scenarios covered by three UWB beacons that validate our proposal.

Control Architecture for Human–Robot Integration: Application to a Robotic Wheelchair

Completely autonomous performance of a mobile robot within noncontrolled and dynamic environments is not possible yet due to different reasons including environment uncertainty, sensor/software robustness, limited robotic abilities, etc. But in assistant applications in which a human is always present, she/he can make up for the lack of robot autonomy by helping it when needed. In this paper, the authors propose human-robot integration as a mechanism to augment/improve the robot autonomy in daily scenarios. Through the human-robot-integration concept, the authors take a further step in the typical human-robot relation, since they consider her/him as a constituent part of the human-robot system, which takes full advantage of the sum of their abilities. In order to materialize this human integration into the system, they present a control architecture, called architecture for human-robot integration, which enables her/him from a high decisional level, i.e., deliberating a plan, to a physical low level, i.e., opening a door. The presented control architecture has been implemented to test the human-robot integration on a real robotic application. In particular, several real experiences have been conducted on a robotic wheelchair aimed to provide mobility to elderly people

Application of UWB and GPS technologies for vehicle localization in combined indoor-outdoor environments

Ultra-wide band (UWB) sensors are innovative devices constructed for efficient wireless communications that have recently being used for vehicle localization in indoor environments. In contrast, GPS sensors are well-known satellite-based positioning devices widely extended for outdoor applications. We evaluate in this paper the combination of both technologies for efficient positioning of vehicles in a mixed scenario (both indoor and outdoor situations), which is typical in applications such as automatic guided vehicles transporting and storing goods among warehouses. The framework we propose for combining sensor information is Monte Carlo localization (also known as particle filters), which is a versatile solution to the fusion of different sensory data and exhibits a number of advantages with respect to other localization techniques. In the paper we describe our approach and evaluate it with several simulated experiments that have yielded promising results. This work, supported by the European project CRAFT-COOP-CT-2005-017668, becomes a first step toward a robust and reliable localization system for automated industrial vehicles.

Inferring robot goals from violations of semantic knowledge

A growing body of literature shows that endowing a mobile robot with semantic knowledge and with the ability to reason from this knowledge can greatly increase its capabilities. In this paper, we present a novel use of semantic knowledge, to encode information about how things should be, i.e. norms, and to enable the robot to infer deviations from these norms in order to generate goals to correct these deviations. For instance, if a robot has semantic knowledge that perishable items must be kept in a refrigerator, and it observes a bottle of milk on a table, this robot will generate the goal to bring that bottle into a refrigerator. The key move is to properly encode norms in an ontology so that each norm violation results in a detectable inconsistency. A goal is then generated to bring the world back in a consistent state, and a planner is used to transform this goal into actions. Our approach provides a mobile robot with a limited form of goal autonomy: the ability to derive its own goals to pursue generic aims. We illustrate our approach in a full mobile robot system that integrates a semantic map, a knowledge representation and reasoning system, a task planner, and standard perception and navigation routines.

Multihierarchical Interactive Task Planning: Application to Mobile Robotics

To date, no solution has been proposed to human-machine interactive task planning that deals simultaneously with two important issues: 1) the capability of processing large amounts of information in planning (as it is needed in any real application) and 2) being efficient in human-machine communication (a proper set of symbols for human-machine interaction may not be suitable for efficient automatic planning and vice versa). In this paper, we formalize a symbolic model of the environment to solve these issues in a natural form through a human-inspired mechanism that structures knowledge in multiple hierarchies. Planning with a hierarchical model may be efficient even in cases where the lack of hierarchical information would make it intractable. However, in addition, our multihierarchical model is able to use the symbols that are most familiar to each human user for interaction, thus achieving efficiency in human-machine communication without compromising the task-planning performance. We formalize here a general interactive task-planning process which is then particularized to be applied to a mobile robotic application. The suitability of our approach has been demonstrated with examples and experiments.

Adaptable Web interfaces for networked robots

Most research in networked robots that use Web interfaces for robot control has been focused recently on the network part, since Ethernet involves poor (unpredictable) time performance. However, we believe that the problem to be addressed is more general and should not be restricted only to communication engineering: the interfaced system as a whole should adapt to get the most from the user, from the connection, and from the robot, even when no strict performance is possible. For that purpose, this paper introduces a new architecture for Web remote operation of robots that exhibits a high degree of flexibility in its adaptation to each particular user (through modular, configurable JAVA applets), to the system time-varying performance (through probability-guided, run-time adaptation of control loops), and to the robot software architecture (the standard CORBA is assumed as its middleware). Our approach constitutes an initial step for adapting comprehensively to all the mentioned issues, hence permitting to be employed in very different scenarios: realtime control, telecare, remote surveillance, etc.

Evaluation of a Telepresence Robot for the Elderly: A Spanish Experience

This paper copes with the evaluation of a robotic telepresence application for the elderly. It presents the experiences on using the Giraff telepresence robot within three real testsites in Spain for more than a year. Results from this evaluation are the basis for future improvements of this technology which very likely will find a field of application in the enhancement of the quality of life of the elderly. This work is framed in the AAL European Joint Programme.

SANCHO, a fair host robot. A description

This paper describes SANCHO, a mobile robot intended to perform within crowded areas as a servant, for instance as a fair or congress host. This robot has been constructed upon a commercial platform on which a number of sensors and devices have been integrated. A software control architecture has been implemented and adapted to this particular robot, enabling it to perform in human scenarios. Among the different subsystems of the control architecture developed for SANCHO, we highlight in this paper two of the most relevant ones: the navigation component which permits the robot to navigate in a safe and robust manner within crowded and dynamic environments, and the communication component which provides different possibilities for human-robot interaction. We illustrate the performance of SANCHO through a number of experiences carried out in public shows.

Life-Long Optimization of the Symbolic Model of Indoor Environments for a Mobile Robot

The use of a symbolic model of the spatial environment becomes crucial for a mobile robot that is intended to operate optimally and intelligently in indoor scenarios. Constructing such a model involves important problems that are not solved completely at present. One is called anchoring, which implies to maintain a correct dynamic correspondence between the real world and the symbols in the model. The other problem is adaptation: among the numerous possible models that could be constructed for representing a given environment, optimization involves the selection of one that improves as much as possible the operations of the robot. To cope with both problems, in this paper, we propose a framework that allows an indoor mobile robot to learn automatically a symbolic model of its environment and to optimize it over time with respect to changes in both the environment and the robot operational needs through an evolutionary algorithm. For coping efficiently with the large amounts of information that the real world provides, we use abstraction, which also helps in improving task planning. Our experiments demonstrate that the proposed framework is suitable for providing an indoor mobile robot with a good symbolic model and adaptation capabilities.