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We identify 10 exciting robotics developments and technologies, ranging from original research that may change the future of robotics to commercial products that enable basic science and drive industrial and medical innovations.

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.

In this paper we present the first planner for the problem of Navigation Among Movable Obstacles (NAMO) on a real robot that can handle environments with under-specified object dynamics. This result makes use of recent progress from two threads of the Reinforcement Learning literature. The first is a hierarchical Markov-Decision Process formulation of the NAMO problem designed to handle dynamics uncertainty. The second is a physics-based Reinforcement Learning framework which offers a way to ground this uncertainty in a compact model space that can be efficiently updated from data received by the robot online. Our results demonstrate the ability of a robot to adapt to unexpected object behavior in a real office scenario.

In this paper, we present an efficient assembly sequence generation method for robot programming, which can reduce the complexity of assembly sequence generation for products within a product family. Today, principle markets for robot assembly automation (e.g., automobile and consumer electronics) have a relatively short product run rate, ranging from six to twelve months. To accommodate this rapidly changing production demand, in most cases, manufacturers design a series of new products within a product family (i.e., a group of products derived from a common product platform) so that they have similar assembly sequences. Seemingly robot programming for a new product within a product family does not require extensive programming adjustments, but it is still complex and manufacturers spend large amount time and money in programming robots for each new product. To solve this problem, we present a new assembly sequence generation method. With a case-based planner, we generate feasible and similar-to-real assembly sequence candidates for a new product from assembly sequences of old products within a product family. Then, we use geometric information extracted from its CAD model to evaluate those candidate sequences. Unlike those used by previous studies, our method can generate an assembly sequence that is more suitable for the concept of a reconfigurable assembly system for general-purpose robots (assembly sequence generation for robot programming); more similar to assembly sequences used in real assembly systems (case-based planning); and more properly evaluated for robot assembly (a CAD model). We apply the proposed method to a toy airplane assembly scenario and demonstrate its feasibility.

Existing Simultaneous Localization and Mapping systems require an extensive manual pre-calibration process. Non-manual calibration procedures use manipulators to create known patterns in order to estimate the unknown calibration. Calibration is often time-consuming and involves humans performing repetitive tasks such as aligning a known calibration target at different poses with respect to the sensor. We propose an algorithm that plans a trajectory which actively reduces the uncertainty of the robot's calibration given a rough initial calibration estimate. Calibration is performed autonomously in a previously unknown environment by maintaining the belief over landmarks, poses, and the calibration parameters. We present experimental results to demonstrate the approach's ability to autonomously calibrate the exteroceptive sensor in simulated and real environments. We show that even a greedy approach can reduce the effort needed to perform calibration every time the robot is reconfigured for autonomous tasks and mitigates the possibility of human error added into the calibration.

This work introduces an affordance characterization employing mechanical wrenches as a metric for predicting and planning with workspace affordances. Although affordances are a commonly used high-level paradigm for robotic task-level planning and learning, the literature has been sparse regarding how to characterize the agent in this object-agent-environment framework. In this work, we propose decomposing a behavior into a vocabulary of characteristic requirements and capabilities that are suitable to predict the affordances of various parts of the workspace. Specifically, we investigate mechanical wrenches as a viable representation of these affordance requirements and capabilities. We then use this vocabulary in a planning system to compose complex motions from simple behavior types in continuous space. The utility of the framework for complex planning is demonstrated on example scenarios both in simulation and with real-world industrial manipulators.

In the United States, there has been a diverse set of robotics programs funded by different agencies 2007. Formulation of a road map for robotics in the United States to establish the basis for a coordinated strategy has been discussed. Four different workshops have been arranged: (1) manufacturing and logistics, (2) health-care and medical robotics, (3) service robotics, and (4) emerging technologies.

In this paper we present a design study and technical specifications of a high performance robotic manipulator to be used for ball catching experiments using commercial off-the-shelf (COTS) components. Early evaluation shows that very good performance can be achieved using standardized PowerCube actuator modules from Amtec and a standard workstation using CAN bus communication. Implementation issues of low-level control and software platform are also described, as well as early experimental evaluation of the system.

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.

Autonomous navigation in urban environments inevitably leads to having to switch between various, sometimes conflicting control tasks. Sting Racing, a collaboration between Georgia Tech and SAIC, has developed a modular control architecture for this purpose and thispaperdescribestheoperationanddefinitionofthisarchitecturethroughso-callednested hybrid automata. We show how to map the requirements associated with the DARPA Urban GrandChallengeontothesenestedautomataandillustratetheiroperationthroughanumber of experimental results.

Application of SLAM outdoors is challenged by complexity, handling of non-linearities and flexible integration of a diverse set of features. A graphical approach to SLAM is introduced that enables ...

We present a system in which a flexible team of robots coordinates to assemble large, complex, and diverse structures autonomously. Our system operates across a wide range of spatial scales and tolerances, using a hierarchical perception architecture. For the successful execution of very precise assembly operations under initial uncertainty, our system starts with high-field of view but low accuracy sensors, and gradually uses low field-of-view but high accuracy sensors. Our system also uses a failure detection and recovery system, integrated with this hierarchical perception architecture: upon losing track of a feature, our system retracts to using high-field of view systems to re-localize. Additionally, we contribute manipulation skills and tools necessary to assemble large structures with high precision. First, the team of robots coordinates to transport large assembly parts which are too heavy for a single robot to carry. Second, we develop a new tool which is capable of co-localizing holes and fasteners for robust insertion and fastening. We present real robot experiments where we measure the contribution of the hierarchical perception and failure recovery approach to the robustness of our system. We also present an extensive set of experiments where our robots successfully insert all 80 of the attempted fastener insertion operations.

© The Author(s) 2015. In this paper we present algorithms and experiments for multi-scale assembly of complex structures by multi-robot teams. We also focus on tasks where successful completion requires multiple types of assembly operations with a range of precision requirements. We develop a hierarchical planning approach to multi-scale perception in support of multi-scale manipulation, in which the resolution of the perception operation is matched with the required resolution for the manipulation operation. We demonstrate these techniques in the context of a multi-step task where robots assemble large box-like objects, inspired by the assembly of an airplane wing. The robots begin by transporting a wing panel, a coarse manipulation operation that requires a wide field of view, and gradually shifts to a narrower field of view but with more accurate sensors for part alignment and fastener insertion. Within this framework we also provide for failure detection and recovery: upon losing track of a feature, the robots retract to using wider field of view systems to re-localize. Finally, we contribute collaborative manipulation algorithms for assembling complex large objects. First, the team of robots coordinates to transport large assembly parts which are too heavy for a single robot to carry. Second, the fasteners and parts are co-localized for robust insertion and fastening. We implement these ideas using four KUKA youBot robots and present experiments where our robots successfully complete all 80 of the attempted fastener insertion operations.

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.

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.

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...

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

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 the idea that by using AI planning in concert with formal task modeling, the overhead associated with plan creation for complex tasks can be reduced. The proposed approach uses a SysML taxonomy to model the system capabilities and the process specification, and the PDDL planning language to determine acceptable objective solutions. This idea is applied to the manufacturing domain, and examples are shown modeling a multi-robot system in an automobile manufacturing environment. A discussion is given regarding the merits of the demonstrated approach.

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.

Robot systems are increasing in complexity. Trying to diagnose a robot that is non-functional or exhibiting suboptimal performance can be a major challenge. A framework for plug-n-play addition of diagnostics to modules in an object oriented software framework is presented. The methods for modeling of system modules, their transition to a Bayesian model and final implementation are described. The methodology is exemplified for a mobile manipulation system and experimental results are presented.

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.

In this paper we describe a method for bridging internet time delays in a teleoperation scenario. In the scenario, the sizes of the time delays is not only stochastic, but it is also large compared to the task execution time. The method proposed uses minimum jerk motion models to predict the input from the user a time into the future that is equivalent to, he one-way communication delay. We present results from a teleoperated ball-catching experiment with real internet delays, where we show that the proposed method makes a significant improvement over traditional methods for teleoperation over intercontinental distances. Copyright 2009 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 20110509

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.

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. With our framework for Human Augmented Mapping we aim to deal with this issue and assume a guided tour as basis for an initial mapping process. During such a tour the robotic system needs to be able to detect significant changes in its environment representation - structural ambiguities - to be able to invoke a clarification discourse with the user. In this paper we present our approach to the detection of such ambiguities, that is independent from prior specification and training of particular spatial categories. We evaluate our method on data sets obtained during several runs in indoor environments in the context of a guided tour scenario.

Mobile manipulation in many respects represents the next generation of robot applications. An important part of design of such systems is the integration of techniques for navigation, recognition, control, and planning to achieve a robust solution. To study this problem three different approaches to mobile manipulation have been designed and implemented. A prototypical application that requires navigation and manipulation has been chosen as a target for the systems. In this paper we present the basic design of the three systems and draw some general lessons on design and implementation.

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.

The military have a considerable amount of experience from using robots for mine clearing and bomb removal. As new technology emerges it is necessary to investigate the possibly to expand robot use. This study has investigated an Army company, specialized in urban operations, while fulfilling their tasks with the support of a PackBot Scout. The robot was integrated and deployed as an ordinary component of the company and included modifying and retraining a number of standard behaviors to include the robot. This paper reports on the following issues: evaluation of missions where the platform can be deployed, what technical improvements are the most desired, and what are the new risks introduced by use of robots? Information was gathered through observation, interviews, and a questionnaire. The results indicate the robot to be useful for reconnaissance and mapping. The users also anticipated that the robot could be used to decrease the risks of IEDs by either triggering or by neutralising them with a disruptor. The robot was further considered to be useful for direct combat if armed, and for placing explosive loads against, for example, a door. Autonomous rendering of maps, acquiring images, two-way audio, and improved sensing such as IR were considered important improvements. The robot slowing down the pace of the unit was considered to be the main risk when used in urban operations.

Robots have been successfully deployed within bomb squads all over the world for decades. Recent technical improvements are increasing the prospects to achieve the same benefits also for other high risk professions. As the number of applications increase issues of collaboration and coordination come into question. Can several groups deploy the same type of robot? Can they deploy the same methods? Can resources be shared? What characterizes the different applications? What are the similarities and differences between different groups? This paper reports on a study of four areas in which robots are already, or are about to be deployed: Military Operations in Urban Terrain (MOUT), Military and Police Explosive Ordnance Disposal (EOD), Military Chemical Biological Radiological Nuclear contamination control (CBRN), and Fire Fighting (FF). The aim of the study has been to achieve a general overview across the four areas to survey and compare their similarities and differences. It has also been investigated to what extent it is possible for the them to deploy the same type of robot. It was found that the groups share many requirements, but, that they also have a few individual hard constrains. A comparison across the groups showed the demands of man-portability, ability to access narrow premises, and ability to handle objects of different weight to be decisive; two or three different sizes of robots will be needed to satisfy the need of the four areas.

In this paper we present a design study and technical specifications of a high performance robotic manipulator to be used for ball catching experiments using commercial off-the-shelf (COTS) components. Early evaluation shows that very good performance can be achieved using standardized PowerCube actuator modules from Amtec and a standard workstation using CAN bus communication. Implementation issues of low-level control and software platform are also described, as well as early experimental evaluation of the system.

The present paper examines minimum jerk models for human kinematics as a tool to predict user input in teleoperation with significant dynamics. Predictions of user input can be a powerful tool to bridge time-delays and to trigger autonomous sub-sequences. In this paper an example implementation is presented, along with the results of a pilot experiment in which a virtual reality simulation of a teleoperated ball-catching scenario is used to test the predictive power of the model. The results show that delays up to 100 ms can potentially be bridged with this approach. Copyright 2007 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 20110908

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.

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

This paper presents a multiple target tracking approach for following and passing persons in the context of human-robot interaction. The general purpose for the approach is the use in human augmented mapping. This concept is presented and it is described how navigation and person following are subsumed under it. Results from experiments under test conditions and from data collected during a user study are also provided.

Road-following for ground vehicles has been widely studied and the solutions normally tracks line markings or a salient road edge. Most of the presented approaches are thus based on geometric or spectral models and does a fit of image data to the model to ensure robustness. For operation on unmarked roads under highly varying weather and light conditions a strategy is presented which is based on a probabilistic generative model. The method is evaluated on a number of test scenes and compared to well known methods from the literature.

This paper presents a combination of a 3D laser sensor and a line-base SLAM algorithm which together produce 2D line maps of highly cluttered indoor environments. The key of the described method is the replacement of commonly used 2D laser range sensors by 3D perception. A straightforward algorithm extracts a virtual 2D scan that also contains partially occluded walls. These virtual scans are used as input for SLAM using line segments as features. The paper presents the used algorithms and experimental results that were made in a former industrial bakery. The focus lies on scenes that are known to be problematic for pure 2D systems. The results demonstrate that mapping indoor environments can be made robust with respect to both, poor odometry and clutter.

A robotic manipulation of objects typically involves object detection/recognition, servoing to the object, alignment and grasping. To perform fine alignment and final grasping, it is usually necessary to estimate the position and orientation (pose) of the object. In this paper we present a model based tracking system used to estimate and continuously update the pose of the object to be manipulated. Here, a wire-frame model is used to find and track features in the consequent images. One of the important parts of the system is the ability to automatically initiate the tracking process. The strength of the system is the ability to operate in an domestic environment (living room) with changing lighting and background conditions.

Behaviour coordination is a notorious problem in mobile robotics. Behaviours are either in competition or collaborating to achieve the goals of a system, which leads to requirements for arbitration and/or fusion of control signals. In most systems the arbitration is specified in terms of "events" that denote positions or sensory input. The detection of these events allows discrete switching between groups of behaviours. In contrast, the fusion of behaviours is often achieved using potential fields, fuzzy rules, or superposition. In most cases, the underlying theoretical foundation is rather weak and the behaviour switching results in discrete changes in the overall system dynamics. In this paper, we present a scheme for behaviour coordination that is grounded in the dynamical systems approach. The methodology provides a solid theoretical basis for analysis and design of a behaviour coordination framework. This framework is demonstrated in the context of a domestic robot for fetch-and-carry type tasks. It is here shown that behaviour coordination can be analyzed as an integral part of the design to facilitate smooth transition and fusion between behaviours.

We present a scheme for mobile manipulation by introducing a mobile manipulation control architecture (MMCA). This architecture is motivated by a need for a systematic control structure for robotic manipulation within a behavior based framework. The control structure enables integration of the manipulator into a behavior based control structure for the platform. Furthermore, our suggested MMCA is designed in such a way that it supports design and performance analysis from both a manipulator dynamics and a hybrid automata perspective.

Ultrasonic sensors are still one of the most widely used sensors in mobile robotics. A notorious problem in the use of sonar data is the lack of good spatial resolution, which typically results in a high uncertainty in the resulting map of the environment. In the paper a triangulation technique is used for filtering of data so as to obtain an improved grid map of the environment. The basic technique is described and it is outlined how it can be used for identification of natural landmarks.

We address scaling of the "dynamic systems" approach for robot planning to multi-agent cooperation. To accommodate this extension it is necessary to carefully consider how individual behaviors contribute to the vector field. To avoid spurious minima and related problems a competition dynamics is introduced and its stability is analyzed. A system of two cooperating agents is designed, and examples are presented to illustrate the utility of this approach.

Robust and safe traversal of doors is an important task for autonomous mobile robots. This paper presents a robust and inexpensive approach for recognizing and localizing doors based on monocular grey-level images and minimalistic models. The traversal is achieved by servoing the robot with respect to the door-hypothesis. Robustness against pose errors, scene complexity, and sensing conditions is obtained by combining indexing on significant aggregated features with a multi-view approach that employs consistency over time: pose-hypotheses are filtered via the motion of the robot to reject incorrect ones. The traversal is implemented as a behavior and was evaluated in 150 experiments at different doors under different conditions.

In the early 1980s, robotics was entering a new era in its development following the earlier successes in industrial applications. With much energy and excitement, the pioneers of robotics launched a number of initiatives, which came to shape what became our field. In 1982, Mike Brady, Richard Paul, and their colleagues established the first journal entirely devoted to robotics, International Journal of Robotics Research (IJRR), which was followed by the organization in 1983, of the first International Symposium of Robotics Research (ISRR). The intent was to foster the notion of robotics as a distinct scientific field and to create a broad community of robotics. It was in the pursuit of these aims that the International Foundation of Robotics Research (IFRR) was later formally founded in 1986. The year 1984 brought a host of other major developments in robotics with the establishment of IEEE Robotics & Automation Council (RAC), led by George Saridis, the organization of the first IEEE Conference on Robotics (John Jarvis and Richard Paul), the launching of the IEEE Journal of Robotics and Automation (George Bekey), and RAC Newsletter (Wesley Snyder). The transformation of the council into our IEEE Robotics & Automation Society (RAS) was formally accomplished in 1989. In the pursuit of a broad community of robotics, the same pioneering researchers who established IFRR were also part of the effort for the creation of the RAS. Devoted to the robotics community, IFRR members have continued in the years since to serve in many capacities in the activities of both organizations, as conference chairs, transactions editors, Administrative Committee (AdCom), and Technical Committee (TC) members, Society presidents, or technical contributors.

A major challenge in mobile robotics is integration of methods into operational autonomous systems. Construction of such systems requires use of methods from perception, control engineering, software engineering, mathematical modelling, and artificial intelligence. In this paper it is described how such a variety of methods have been integrated to provide an autonomous service robot system that can carry out fetch-and-carry type missions. The system integrates sensory information from sonars, laser scanner, and computer vision to allow navigation and human-computer interaction through the use of a hybrid deliberative architecture. The paper presents the underlying objectives, the underlying architecture, and the needed behaviours. Throughout the paper, examples from real-world evaluation of the system are presented.