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Situational awareness in rescue operations can be provided by teams of autonomous mobile robots. Human operators are required to teleoperate the current generation of mobile robots for such applications; however, teleoperation is increasingly difficult as the number of robots is expanded. As the number of robots is increased, each robot may also interfere with one another and eventually decrease mapping performance. As presented here, through careful consideration of robot team coordination and exploration strategy, large numbers of mobile robots can be allocated to accomplish the mapping task more quickly and accurately. We present both the coordination and exploration strategies and present results from experiments in simulation as well as with up to nine mobile platforms.

This paper presents a distributed approach to enabling mobile robot swarms to track multiple targets moving unpredictably. The proposed approach consists of two constituent algorithms: local interaction and target tracking. When the robots are faster than the targets, Lyapunov theory can be applied to show that the robots converge asymptotically to each vertex of the desired equilateral triangular configurations while tracking the targets. Toward practical implementation of the algorithms, it is important to realize the observation capability of individual robots in an inexpensive and efficient way. A new proximity sensor that we call dual rotating infrared (DRIr) sensor is developed to meet these requirements. Both our simulation and experimental results employing the proposed algorithms and DRIr sensors confirm that the proposed distributed multi-target tracking method for a swarm of robots is effective and easy to implement.

This paper presents practical design and hardware implementation issues of self-configuring swarms of autonomous mobile robots. For the purpose, we develop a new low-cost position detection system that we call dual rotating infrared (DRIr) sensor. The DRIr sensor can provide robots with advanced sensing capabilities that give reliable information about the position and surface geometry of neighboring robots and obstacles. Special focus is placed on how to realize the observation and object identification of mobile robots through the use of DRIr sensors. We verify the functionality and performance of the DRIr sensors mounted on a commercial mobile robot. Experimental results show that a swarm of mobile robots equipped with the DRIr sensors can autonomously configure themselves into an area.

We address the problem of dispersing a large number of autonomous mobile robots toward building wireless ad hoc sensor networks performing environmental monitoring and control. For the purpose, we propose the adaptive triangular mesh generation algorithm that enables robots to generate triangular meshes of various sizes adapting to changing environmental conditions. A locally interacting, geometric technique allows robots to generate each triangular mesh with their two neighbor robots. Specifically, we have assumed that robots are not allowed to have the identifier, any pre-determined leaders or common coordinate systems, and any explicit communication. Under such minimal conditions, the positions of the robots were shown to converge to the desired distribution, which was mathematically proven and also verified through extensive simulations. Our preliminary results indicate that the proposed algorithm can be applied to the problem regarding the coverage of an area of interest by a swarm of mobile sensors.

An annotated data set is presented meant to help researchers in developing, evaluating, and comparing various approaches in robotics for building space representations appropriate for communicating with humans. The data consist of omnidirectional images, laser range scans, sonar readings, and robot odometry. A set of base-level human spatial concepts is used to annotate the data.

The armed forces have a considerable amount of experience in using robots for bomb removal and mine clearing. Emerging technology also enables the targeting of other applications. To evaluate if real deployment of new technology is justified, tactical advantages gained have to be compared to drawbacks imposed. Evaluation calls for realistic tests which in turn require methods dictating how to deploy the new features. The present study has had two objectives: first, to gain a comprehensive view of a potential user of man-portable robots; second, to embed a robot system with users for assessment of present technology in real deployment. In this project we investigated an army company specialized in urban operations performing their tasks with the support of the iRobot Packbot Scout. The robot was integrated and deployed as an ordinary piece of equipment which required modifying and retraining a number of standard behaviors. The reported results were acquired through a long-term test ranging over a period of six months. This paper focuses on the characteristics of the users and their current ways of operation; how the robot was implemented and deployed. Additionally, this paper describes benefits and drawbacks from the users' perspective. A number of limitations in current robot technology are also identified. The findings show that the military relies on precise and thoroughly trained actions that can be executed with a minimum of ambiguity. To make use of robots, new behavioral schemes, which call for tactical optimization over several years, are needed. The most common application during the trials was reconnaissance inside buildings with uncertain enemy presence when time was not critical. Deploying the robot took more time than completing the task by traditional means, but in return kept the soldiers out of harm's way and enabled them to decrease weapon deployment. The range of the radio link, limited video feedback, and the bulky operator control unit were the features constraining the system's overall performance the most. On the other hand, did properties of the system, such as ruggedness, size, weight, terrain ability, and endurance, prove to match the application. The users were of the opinion that robots such as the Packbot Scout would be a valuable standard feature in urban intervention. © 2007 Wiley Periodicals, Inc.

Robots should be able to reason about using objects in the environment to assist task completion. If faced with an unexpected situation, such as a critically damaged floor, robots should be able to evaluate different ways to resolve the situation rather than directly declaring failure. In this paper, we present the first online framework that enables robots to reason about using environment objects as tools to overcome obstructions, allowing robots to solve previously unsolvable tasks. The framework utilizes constraint relaxed planning to detect unavoidable obstructions. In case the robot becomes disconnected from the goal by an obstruction, the system guides the robot to search for and utilize an object in the environment to overcome the obstruction using the concept of inverse affordances. We verified the framework in a realistic simulation environment for the PR2 robot.

For a mobile manipulator to interact with large everyday objects, such as office tables, it is often important to have dynamic models of these objects. However, as it is infeasible to provide the robot with models for every possible object it may encounter, it is desirable that the robot can identify common object models autonomously. Existing methods for addressing this challenge are limited by being either purely kinematic, or inefficient due to a lack of physical structure. In this paper, we present a physics-based method for estimating the dynamics of common non-holonomic objects using a mobile manipulator, and demonstrate its efficiency compared to existing approaches.

Autonomous navigation is an essential capability for domestic service robots, however at times direct remote control may be desired for cases where robot and user are co-located. In this work, we propose a remote control method that allows a user to control the robot with smartphone gestures. The robot moves with respect to the user's coordinate frame and avoids obstacles if a collision is imminent. We think that interpreting the commands from human's perspective would decrease the cognitive load of the user, therefore allowing efficient operation.

This paper describes the basic structure and activities of European Robotics Network (EURON). EURON was established primarily to ensure broad involvement of robotics across many different fields of applications and research. It currently involves about 145 groups covering almost all countries in Europe.

Navigation in large-scale environments is composed of different local tasks. To achieve smooth switching between these tasks and thus a continuous control signal, usually a precise map of the environment and an exact pose estimate of the robot are needed. Both are hard to fulfil for experiments in real-world settings. We present a system that shows how one can relax the need for accurate metric models of the environment while at the same time achieving smooth task switching. To facilitate this scheme the dynamical systems approach is used, which incorporates behaviour coordination through competition in a dynamic framework. Feature detectors use sonar data to provide means for local navigation. This ability combined with a simple topological map constitutes a complete navigation system for large-scale office environments. Experiments showed that a Scout robot using this scheme is able to successfully navigate through our whole institute. Through the use of the dynamic behaviour coordination, switching between the navigational tasks occurs in a smooth manner leading to continuous control of the platform.

Behavior coordination is a notorious problem in mobile robotics. Behaviors are either in competition or collaborating to achieve the goals of a system, which leads to requirements for arbitration a...

In this paper we make a comparative study of some successful software architectures for mobile robot systems. The objective is to gather experience for the future design of a new robot architecture. Three architectures are studied more closely, Saphira, TeamBots and BERRA. Qualities such as portability, ease of use, software characteristics, programming and run-time efficiency are evaluated. In order to get a true hands-on evaluation, all the architectures are implemented on a common hardware robot platform. A simple reference application is made with each of these systems. All the steps necessary to achieve this are discussed and compared. Run-time data are also gathered. Conclusions regarding the results are made, and a sketch for a new architecture is made based on these results.

Multi-robot patrolling is a problem that has im- portant applications in security and surveillance. However, the optimal task assignment is known to be NP-hard. We consider evenly spacing the robots in a cyclic Traveling Sales- man Problem (TSP) tour or partitioning the graph of the environment. The trade-off in performance, overall team travel cost and coordination is analyzed in this paper. We provide both a theoretical analysis and simulation results across multiple environments. The results demonstrate that generally cyclic- based strategies are superior, especially when small teams are used but at the expense of greater team cost, whereas partitioning strategies are especially suitable for larger teams and unbalanced graph topologies. The reported results show that graph topology and team size are fundamental to determine the best choice for a patrol strategy. I. INTRODUCTION Advances on autonomous mobile robots have been evident in the last couple of decades. In particular, the patrolling problem with a team of cooperative agents has received much focus. The problem, which is also known as repet- itive sweeping, has unquestionable utility in society and finds its applications in surveillance systems, infrastructure security and inspection, search and rescue, mine clearing, military operations, environmental monitoring, intelligent transportation, household cleaning, and several other areas. Being monotonous, these tasks may also be dangerous. Thus, improving safety and reducing fatigue is a major advantage of multi-robot patrolling systems. In this context, robots are required to continuously travel in the environment, and the key challenge is to design efficient routes in order to optimize a certain performance criterion. Like most existing work in the literature (1-4), it is assumed that robots are homogeneous, travel with constant speed, and are expected to visit every strategic position of the environment. Therefore, having adequate sensing range, complete coverage of the environment is achieved by visiting all the important locations in the area. Despite several recent contributions to the problem, one question still remains open: what is the optimal patrol strategy for a given generic environment using R robots? This work was supported by a PhD scholarship (SFRH/BD/64426/2009), the CHOPIN research project (PTDC/EEA-CRO/119000/2010) and by the Institute of Systems and Robotics (project PEst-C/EEI/UI0048/2011), all of them funded by the Portuguese science agency "Fundac ¸ ˜ ao para a Cie a

Autonomous mobile robotic teams are increasingly used in exploration of indoor environments. Accurate modeling of the world around the robot and describing the interaction of the robot with the world greatly increases the ability of the robot to act autonomously. This paper demonstrates the ability of autonomous robotic teams to find objects of interest. A novel feature of our approach is the object discovery and the use of it to augment the mapping and navigation process. The generated map can then be decomposed into into semantic regions while also considering the distance and line of sight to anchor points. The advantage of this approach is that the robot can return a dense map of the region around an object of interest. The robustness of this approach is demonstrated in indoor environments with multiple platforms with the objective of discovering objects of interest. 1. MOTIVATION Robots have shown a lot of potential in exploration, mapping and navigation of indoor environments. Robots are ideal for applications such as discovery of Improvised Explosive Devices (IEDs). Robotic teams can achieve this task faster, provide dense maps of regions around an object of interest and a model of the discovered object. The model of the discovered object can then be viewed by a human user and take the necessary actions. Semantic mapping and object discovery have gained interest independently. The intersection of these areas provides an interesting insight into using robots to discover objects of interest in indoor environments. Given the intended application, an object guided approach to indoor navigation is ideal. Robots can use knowledge of the environment and the semantic labeling to guide their ability to autonomously navigate and discover objects of interest. To achieve this, we propose an algorithm that combines the knowledge of discovered objects into the mapping process. This can provide an insight into the environment and aid the process of modeling the discovered objects.

We present a navigation guidance system that guides a human to a goal point with a tactile belt interface and a stationary laser scanner. We make use of ROS local navigation planner to find an obstacle-free path by modeling the human as a non-holonomic robot. Linear and angular velocities to keep the ’robot’ on the path are dynamically calculated, which are then converted to vibrations and applied by the tactile belt. We define directional and rotational vibration patterns and evaluate which ones are suitable for guiding humans. Continuous patterns for representing directions had the least average angular error with 8.4◦, whereas rotational patterns were recognized with near-perfect accuracy. All patterns had a reaction time slightly more than 1 seconds. The person is tracked in laser scans by fitting an ellipse to the torso. Average tracking error is found to be 5cm in position and 14◦ in orientation in our experiments with 23 people. Best tracking results was achieved when the person is 2.5m away and is facing the sensor or the opposite way. The human control system as a whole is successfully demonstrated in a navigation guidance scenario.

Keeping track of the position and orientation over time using sensor data, i.e., pose tracking, is a central component in many mobile robot systems. In this paper, we present a Kalman filter-based approach utilizing a minimalistic environmental model. By continuously updating the pose, matching the sensor data to the model is straightforward and outliers can be filtered out effectively by validation gates. The minimalistic model paves the way for a low-complexity algorithm with a high degree of robustness and accuracy. Robustness here refers both to being able to track the pose for a long time, but also handling changes and clutter in the environment. This robustness is gained by the minimalistic model only capturing the stable and large scale features of the environment. The effectiveness of the pose tracking is demonstrated through a number of experiments, including a run of 90 min., which clearly establishes the robustness of the method.

In this paper we present a new technique for on-line extraction of natural point landmarks in an indoor environment. The landmarks are filtered out from sonar data taken from a mobile platform. By storing point landmark positions in a reference map, we can achieve absolute robot localization by doing matching between recently collected landmarks and the reference map. The matching procedure also takes advantage of compass readings to increase computational speed and make the performance more robust. The technique is easily extended to also encompass local robot localization, which is used when the robot navigates autonomously. The position accuracy of the robot as well as its capability to navigate using natural landmarks is illustrated in a number of real world experiments.

In this paper a sensor fusion scheme, called triangulation-based fusion (TBF) of sonar data, is presented. This algorithm delivers stable natural point landmarks, which appear in practically all indoor environments, i.e., vertical edges like door posts, table legs, and so forth. The landmark precision is in most cases within centimeters. The TBF algorithm is implemented as a voting scheme, which groups sonar measurements that are likely to have hit the same object in the environment. The algorithm has low complexity and is sufficiently fast for most mobile robot applications. As a case study, we apply the TBF algorithm to robot pose tracking. The pose tracker is implemented as a classic extended Kalman filter, which use odometry readings for the prediction step and TBF data for measurement updates. The TBF data is matched to pre-recorded reference maps of landmarks in order to measure the robot pose. In corridors, complementary TBF data measurements from the walls are used to improve the orientation and position estimate. Experiments demonstrate that the pose tracker is robust enough for handling kilometer distances in a large scale indoor environment containing a sufficiently dense landmark set.

Cleaning is a major problem associated with pools. Since the manual cleaning is tedious and boring there is an interest in automating the task. This paper presents methods for autonomous localization and navigation for a pool cleaner to enable full coverage of pools. Path following cannot be ensured through use of internal position estimation methods alones therefore sensing is needed. Sensor based estimation enable automatic correction of slippage. For this application we use ultrasonic sonars. Based on an analysis of the overall task and performance of the system a strategy for cleaning/navigation is developed. For the automatic localization a Kalman filtering technique is proposed: the Kalman filter uses sonar measurements and a dynamic model of the robot to provide estimates of the pose of the pool cleaner. Using this localization method we derive an optimal control strategy for traversal of a pool. The system has been implemented and successfully tested on the “WEDA B400” pool cleaner.

This paper will explore the relationship between sensory accuracy and Simultaneous Localization and Mapping (SLAM) performance. As inexpensive robots are developed with commodity components, the relationship between performance level and accuracy will need to be determined. Experiments are presented in this paper which compare various aspects of sensor performance such as maximum range, noise, angular precision, and viewable angle. In addition, mapping results from three popular laser scanners (Hokuyo’s URG and UTM30, as well as SICK’s LMS291) are compared.

Widespread use of mobile robots can only be achieved when framworks that enable specification, design and implementation of systems are available. These frameworks must provide a level of abstraction that enables use of the same methods for different tasks/missions to facilitate fast prototyping and design at low cost. In this paper a Task Description Language (TDL) for task specification is outlined. The tasks are specified as network of processes. The processes are described in terms of finite state machines (FSMs) and their composition is achieved via a set of composition operators, common to many process algebra models. From the obtained description of the task a Discrete Event Systems supervisory controller can be synthesized. To demonstrate that such an approach is suitable basis for description of robot tasks a set of experiments with two different platforms situated in two different laboratories is described. The elementary processes necessary to carry out these experiments are presented. Obtained results are reported for three different experiments. The results demonstrate that the presented framework has the required characteristics.

Mobile robots are already widely used by first responders both in civilian and military operations. Our current goal is to provide the human team with all the information available from an unknown environment quickly and accurate. Also, the robots need to explore autonomous because tele-operating more than two robots is very difficult and demands one person per robot to do it. In this paper the authors describe the results of several experiments on behalf of the MAST CTA. Our exploration strategies developed for the experiments use from two to nine robots which sharing information are able to explore and map an unknown environment. Each robot has a local map of the environment and transmit the measurements information to a central computer which fusion all the data to make a global map. This computer called map coordinator send exploration goals to the robot teams in order to explore the environment in the fastest way available. The performance of our exploration strategies were evaluated in different scenarios and tested in two different mobile robot platforms.

Context is an important factor for domestic service robots to consider when interpreting their environments to perform tasks. In people's homes, rooms are laid out in a specific arrangement to enable comfortable and efficient living; for example, the living room is central to the house, and the dining room is adjacent to the kitchen. The identity of the objects in a room are a strong cue for determining that room's purpose. This paper will present a planner for an autonomous mobile robot system which uses room connectivity topology and object understanding as context for an object search task in a domestic environment.

We present a method for a mobile robot to follow a person autonomously where there is an interaction between the robot and human during following. The planner takes into account the predicted trajectory of the human and searches future trajectories of the robot for the path with the highest utility. Contrary to traditional motion planning, instead of determining goal points close to the person, we introduce a task dependent goal function which provides a map of desirable areas for the robot to be at, with respect to the person. The planning framework is flexible and allows encoding of different social situations with the help of the goal function. We implemented our approach on a telepresence robot and conducted a controlled user study to evaluate the experiences of the users on the remote end of the telepresence robot. The user study compares manual teleoperation to our autonomous method for following a person while having a conversation. By designing a behavior specific to a flat screen telepresence robot, we show that the person following behavior is perceived as safe and socially acceptable by remote users. All 10 participants preferred our autonomous following method over manual teleoperation.

Efficient and accurate 3D mapping is desirable in disaster recovery as well as urban warfare situations. The speed with which these maps can be generated is vital to provide situational awareness in these situations. A team of mobile robots can work together to build maps more quickly. We present an algorithm by which a team of mobile robots can merge 2D and 3D measurements to build a 3D map, together with experiments performed at a military test facility.

For navigation in a partially known environment it is possible to provide a model that may be used for guidance in the navigation and as a basis for selective sensing. In this paper a navigation system for an autonomous mobile robot is presented. Both navigation and sensing is built around a graphics model, which enables prediction of the expected scene content. The model is used directly for prediction of line segments which, through matching, allow estimation of position and orientation. In addition, the model is used as a basis for a hierarchical stereo matching that enables dynamic updating of the model with unmodelled objects in the environment. For short-term path planning a set of reactive behaviours is used. The reactive behaviours include use of inverse perspective mapping for generation of occupancy grids, a sonar system and simple gaze holding for monitoring of dynamic obstacles. The full system and its component processes are described and initial experiments with the system are briefly outlined.

The goal of simultaneous localization and mapping (SLAM) is to compute the posterior distribution over landmark poses. Typically, this is made possible through the static world assumption - the landmarks remain in the same location throughout the mapping procedure. Some prior work has addressed this assumption by splitting maps into static and dynamic sets, or by recognizing moving landmarks and tracking them. In contrast to previous work, we apply an Expectation Maximization technique to a graph based SLAM approach and allow landmarks to be dynamic. The batch nature of this operation enables us to detect moveable landmarks and factor them out of the map. We demonstrate the performance of this algorithm with a series of experiments with moveable landmarks in a structured environment.

Classification of spatial regions based on semantic information in an indoor environment enables robot tasks such as navigation or mobile manipulation to be spatially aware. The availability of contextual information can significantly simplify operation of a mobile platform. We present methods for automated recognition and classification of spaces into separate semantic regions and use of such information for generation of a topological map of an environment. The association of semantic labels with spatial regions is based on Human Augmented Mapping. The methods presented in this paper are evaluated both in simulation and on real data acquired from an office environment.

This paper describes the robot vision track that has been proposed to the ImageCLEF@ICPR2010 participants. The track addressed the problem of visual place classification. Participants were asked to classify rooms and areas of an office environment on the basis of image sequences captured by a stereo camera mounted on a mobile robot, under varying illumination conditions. The algorithms proposed by the participants had to answer the question "where are you?" (I am in the kitchen, in the corridor, etc) when presented with a test sequence imaging rooms seen during training (from different viewpoints and under different conditions), or additional rooms that were not imaged in the training sequence. The participants were asked to solve the problem separately for each test image (obligatory task). Additionally, results could also be reported for algorithms exploiting the temporal continuity of the image sequences (optional task). A total of eight groups participated to the challenge, with 25 runs submitted to the obligatory task, and 5 submitted to the optional task. The best result in the obligatory task was obtained by the Computer Vision and Geometry Laboratory, ETHZ, Switzerland, with an overall score of 3824.0. The best result in the optional task was obtained by the Intelligent Systems and Data Mining Group, University of Castilla-La Mancha, Albacete, Spain, with an overall score of 3881.0.

In this paper we describe the problem of Visual Place Categorization (VPC) for mobile robotics, which involves predicting the semantic category of a place from image measurements acquired from an autonomous platform. For example, a robot in an unfamiliar home environment should be able to recognize the functionality of the rooms it visits, such as kitchen, living room, etc. We describe an approach to VPC based on sequential processing of images acquired with a conventional video camera. We identify two key challenges: Dealing with non-characteristic views and integrating restricted-FOV imagery into a holistic prediction. We present a solution to VPC based upon a recently-developed visual feature known as CENTRIST (CENsus TRansform hISTogram). We describe a new dataset for VPC which we have recently collected and are making publicly available. We believe this is the first significant, realistic dataset for the VPC problem. It contains the interiors of six different homes with ground truth labels. We use this dataset to validate our solution approach, achieving promising results.

In this paper, we discuss a user-centered vision of future domestic robots based on 48 householders' depiction of their ideal home robots. Through users' creative responses, we aim to identify domestic tasks desired for robotic assistance, and hence guide the design effort to better reflect user needs. Our study results show that householders want domestic robots for tasks including Time-consuming Drudgeries, House-sitting, and Personal Attendance. Further, we present three design lessons we learned to increase householders' acceptance of these robots. The design of domestic robots needs to provide a certain amount of human control, be compatible with the user's domestic environment, and take gender into consideration.

The combination of a mobile platform and a manipulator, known as a mobile manipulator, provides a highly flexible system, which can be used in a wide range of applications, especially within the field of service robotics. One of the challenges with mobile manipulators is the construction of control systems, enabling the robot to operate safely in potentially dynamic environments. In this paper we will present work in which a mobile manipulator is controlled using the dynamical systems approach. The method presented is a two level approach in which competitive dynamics are used both for the overall coordination of the mobile platform and the manipulator as well as the lower level fusion of obstacle avoidance and target acquisition behaviors.

Robots are gradually being deployed for everyday environments, that is environments with no or very limited engineering taking place before deployment of the system. As manipulators and mobile platforms come together to deliver truly ubiquitous functionality the number of possible applications open up. The design of such systems require careful design of methods for navigation in dynamic environments, posture control, object recognition, visual servoing, grasp planning and integration. In this presentation a system for mobile manipulation in everyday environments will be discussed. The general design of the system will be outlined and the different component systems will be presented with a discussion of the alternatives for successful performance. Results from a real demonstrator system will also be presented to illustrate performance. Finally a number of challenges for the future in terms of basic performance, transfer of results and deployment will be presented.

Sensing and estimation are essential aspects of the design of any robotic system. At a very basic level, the state of the robot itself must be estimated for feedback control. At a higher level, perception, which is defined here to be task-oriented interpretation of sensor data, allows the integration of sensor information across space and time to facilitate planning.

Localization and context interpretation are two key competences for mobile robot systems. Visual place recognition, as opposed to purely geometrical models, holds promise of higher flexibility and association of semantics to the model. Ideally, a place recognition algorithm should be robust to dynamic changes and it should perform consistently when recognizing a room (for instance a corridor) in different geographical locations. Also, it should be able to categorize places, a crucial capability for transfer of knowledge and continuous learning. In order to test the suitability of visual recognition algorithms for these tasks, this paper presents a new database, acquired in three different labs across Europe. It contains image sequences of several rooms under dynamic changes, acquired at the same time with a perspective and omnidirectional camera, mounted on a socket. We assess this new database with an appearance- based algorithm that combines local features with support vector machines through an ad-hoc kernel. Results show the effectiveness of the approach and the value of the database.

An important problem in the design of mobile robot systems for operation in natural environments for everyday tasks is the safe handling of encounters with people. People-People encounters follow certain social rules to allow co-existence even in cramped spaces. These social rules are often described according to the classification termed proxemics. In this paper we present an analysis of how the physical interaction with people can be modelled using the rules of proxemics and discuss how the rules of embodied feedback generation can simplify the interaction with novice users. We also provide some guidelines for the design of a control architecture for a mobile robot moving among people. The concepts presented are illustrated by a number of real experiments that verify the overall approach to the design of systems for navigation in human-populated environments.

Robots have a potential to be a significant aid in high risk, unstructured and stressing situations such as experienced by police, fire brigade, rescue workers and military. In this project we have explored the abilities of today's robot technology in the mentioned fields. This was done by, studying the user, identifying scenarios where a robot could be used and implementing a robot system for these cases. We have concluded that highly portable field robots are emerging to be an available technology but that the human-robot interaction is currently a major limiting factor of today's systems. Further we have found that operational protocols, stating how to use the robots, have to be designed in order to make robots an effective tool in harsh and unstructured field environments.

This paper explores the problem of embodied interaction between a service robot and a person in a hallway setting. For operation in environments with people that have limited experience with robots, a behaviour that signals awareness of the persons and safety of motion is essential. A control strategy based on human spatial behaviour studies is presented that adopts human-robot interaction patterns similar to those used in person-person encounters. The results of a pilot study with human subjects are presented in which the users have evaluated the acceptability of the robot behaviour patterns during passage with respect to three basic parameters: the robot speed, the signaling distance at which the robot starts the maneuver and the lateral distance from the person for safe passage. The study has shown a good overall user response and has provided some useful indications on how to design a hallway passage behaviour that could be most acceptable to human users.

Scan-matching based on data from a laser scanner is frequently used for mapping and localization. This paper presents an scan-matching approach based instead on visual information from a stereo system. The Scale Invariant Feature Transform (SIFT) is used together with epipolar constraints to get high matching precision between the stereo images. Calculating the 3D position of the corresponding points in the world results in a visual scan where each point has a descriptor attached to it. These descriptors can be used when matching scans acquired from different positions. Just like in the work with laser based scan matching a map can be defined as a set of reference scans and their corresponding acquisition point. In essence this reduces each visual scan that can consist of hundreds of points to a single entity for which only the corresponding robot pose has to be estimated in the map. This reduces the overall complexity of the map. The SIFT descriptor attached to each of the points in the reference allows for robust matching and detection of loop closing situations. The paper presents real-world experimental results from an indoor office environment.

An important competence for a mobile robot system is the ability to localize and perform context interpretation. This is required to perform basic navigation and to facilitate local specific services. Usually localization is performed based on a purely geometric model. Through use of vision and place recognition a number of opportunities open up in terms of flexibility and association of semantics to the model. To achieve this the present paper presents an appearance based method for place recognition. The method is based on a large margin classifier in combination with a rich global image descriptor. The method is robust to variations in illumination and minor scene changes. The method is evaluated across several different cameras, changes in time-of-day and weather conditions. The results clearly demonstrate the value of the approach.

Human interaction with a service robot requires a shared representation of the environment for spoken dialogue and task specification where names used for particular locations are depending on personal preferences. A question is how such human oriented models can be tied to the geometric robotic models needed for precise localisation and navigation. We assume that this integration can be based on the information potential users give to a service robot about its working environment. We further believe that this information is best given in an interactive setting (a "guided tour") in this particular environment. This paper presents a pilot study that investigates how humans present a familiar environment to a mobile robot. The study is set up within our concept of Human Augmented Mapping, for which we assume an initial "guided tour" scenario to teach a robot its environment. Results from this pilot study are used to validate a proposed generic environment model for a service robot.

Service robots designed for domestic settings need to navigate in an environment that they have to share with their users. Thus, they have to be able to report their current state and whereabouts in a way that is comprehensible for the user. Pure metric maps do not usually correspond to the understanding of the environment a user would provide. Thus, the robotic map needs to be integrated with the human representation. This paper describes our framework of Human Augmented Mapping that allows us to achieve this integration. We propose further a method to specify and represent regions that relate to a user's view on the environment. We assume an interactive setup for the specification of regions and show the applicability of our method in terms of distinctiveness for space segmentation and in terms of localisation purposes.

In this paper, the design of an office-guide robot for social interaction studies is presented. We are interested in studying the impact of passage behaviours in casual encounters. While the system offers assistance in locating the appropriate office that a visitor wants to reach, it is expected to engage in a passing behaviour to allow free passage for other persons that it may encounter. Through use of such an approach it is possible to study the effect of social interaction in a situation that is much more natural than out-of-context user studies. The system has been tested in an early evaluation phase when it worked for almost 7 hours. A total of 64 interactions with people were registered and 13 passage behaviors were performed to conclude that this framework can be successfully used for the evaluation of passing behaviors in natural contexts of operation.

Casual encounters with mobile robots for non-experts can be a challenge due to lack of an interaction model. The present work is based on the rules from proxemics which are used to design a passing strategy. In narrow corridors the lateral distance of passage is a key parameter to consider. An implemented system has been used in a small study to verify the basic parametric design for such a system. In total 10 subjects evaluated variations in proxemics for encounters with a robot in a corridor setting. The user feedback indicates that entering the intimate sphere of people is less comfortable, however a too significant avoidance is also considered unnecessary. Adequate signaling of avoidance is a behaviour that must be carefully tuned.

Interacting with a domestic service robot implies the existence for a joint environment model for a user and a robot. We present a pilot study that investigates, how humans present a familiar environment to a mobile robot. Results from this study are used to evaluate a generic environment model for a service robot that can be personalised by interaction.

A teleoperation system for controlling a robot with fast dynamics over the Internet has been constructed. It employs a predictive control structure with an accurate dynamic model of the robot to overcome problems caused by varying delays. The operator interface uses a stereo virtual reality display of the robot cell, and a haptic device for force feed-back including virtual obstacle avoidance forces. Copyright 2006 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.QC 20111006

An Ontology of Robotics Science.- A Multi-Agent System Architecture for Modular Robotic Mobility Aids.- How to Construct Dense Objects with Self-Reconfigurable Robots.- In Situ Autonomous Biomechanical Characterization.- Incremental Learning of Task Sequences with Information- Theoretic Metrics.- Representation, Recognition and Generation of Actions in the context of Imitation Learning.- Reduction of Learning Time for Robots Using Automatic State Abstraction.- Efficient Failure Detection for Mobile Robots Using Mixed-Abstraction Particle Filters.- Optimization and Fail-Safety Analysis of Antagonistic Actuation for pHRI.- Bilateral Control of Different Order Teleoperators.- Navigation and planning in an unknown environment using vision and a cognitive map.- Exploiting Distinguishable Image Features in Robotic Mapping and Localization.- Image-Based Visual Servoing for Mobile Robots with Catadioptric Camera.- Multi-knowledge Approach for Mobile Robot Identification of a Changing Environment.- Robust Monte-Carlo Localization using Adaptive Likelihood Models.- Metric Localization with Scale-Invariant Visual Features using a Single Perspective Camera.