Abstract — Recently, Rao-Blackwellized particle filters have been introduced as an effective means to solve the simultaneous localization and mapping problem. This approach uses a particle filter in which each particle carries an individual map of the environment. Accordingly, a key question is how to reduce the number of particles. In this paper, we present adaptive techniques for reducing this number in a Rao-Blackwellized particle filter for learning grid maps. We propose an approach to compute an accurate proposal distribution taking into account not only the movement of the robot but also the most recent observation. This drastically decreases the uncertainty about the robot’s pose in the prediction step of the filter. Furthermore, we present an approach to selectively carry out resampling operations which seriously reduces the problem of particle depletion. Experimental results carried out with real mobile robots in large-scale indoor as well as in outdoor environments illustrate the advantages of our methods over previous approaches. Index Terms — SLAM, Rao-Blackwellized particle filter, adaptive resampling, motion-model, improved proposal

Player/Stage has become a de facto standard in the open source robotics community. We describe recent work on restructuring the Player robot device server into a system that more closely aligns with the idea of a robot framework. The general requirements for a robot framework are also discussed. Player 2.0 is a major improvement in two basic areas, simplicity and flexibility. The driver API has been vastly simplified with many more parts of the communication being hidden from the user. The flexibility of the new system is realised in the library division that allows for different transport layers (or no transport at all), and in the new device address structure that allows for inter-server device subscriptions. 1

The Partially Observable Markov Decision Process has long been recognized as a rich framework for real-world planning and control problems, especially in robotics. However exact solutions in this framework are typically computationally intractable for all but the smallest problems. A well-known technique for speeding up POMDP solving involves performing value backups at specific belief points, rather than over the entire belief simplex. The efficiency of this approach, however, depends greatly on the selection of points. This paper presents a set of novel techniques for selecting informative belief points which work well in practice. The point selection procedure is combined with point-based value backups to form an effective anytime POMDP algorithm called Point-Based Value Iteration (PBVI). The first aim of this paper is to introduce this algorithm and present a theoretical analysis justifying the choice of belief selection technique. The second aim of this paper is to provide a thorough empirical comparison between PBVI and other state-of-the-art POMDP methods, in particular the Perseus algorithm, in an effort to highlight their similarities and differences. Evaluation is performed using both standard POMDP domains and realistic robotic tasks.

Abstract — This paper describes two initiatives aiming at improving code reusability for programming mobile robots: RobotFlow/FlowDesigner, a data-flow programming environment; MARIE (Mobile and Autonomous Robotics Integration Environment), a programming environment allowing multiple applications, programs and tools, to operate on one or multiple machines/OS and work together on a mobile robot implementation. RobotFlow/FlowDesigner’s objective is to provide a modular, graphical programming environment that will help visualize and understand what is really happening in the robot’s control loops, sensors, actuators, by using graphical probes. MARIE aims at avoiding making an exclusive choice on particular programming tools, making it possible to reuse code and applications. I.

Robotic Development Environments (RDEs) have come to play an increasingly important role in robotics research in general, and for the development of architectures for mobile robots in particular. Yet, no systematic evaluation of available RDEs has been performed; establishing a comprehensive list of evaluation criteria targeted at robotics applications is desirable that can subsequently be used to compare their strengths and weaknesses. Moreover, there are no practical evaluations of the usability and impact of a large selection of RDEs that provides researchers with the information necessary to select an RDE most suited to their needs, nor identifies trends in RDE research that suggest directions for future RDE development. This survey addresses the above by selecting and describing nine open source, freely available RDEs for mobile robots, evaluating and comparing them from various points of view. First, based on previous work concerning agent systems, a conceptual framework of four broad categories is established, encompassing the characteristics and capabilities that an RDE supports. Then, a practical evaluation of RDE usability in designing, implementing, and executing robot architectures is presented. Finally, the impact of specific RDEs on the field of robotics is addressed by providing a list of published applications and research projects that give concrete examples of areas in which systems have been used. The comprehensive evaluation and comparison of the nine RDEs concludes with suggestions of how to use the results of this survey and a brief discussion of future trends in RDE design. 1

This article presents GraphSLAM, a unifying algorithm for the offline SLAM problem. GraphSLAM is closely related to a recent sequence of research papers on applying optimization techniques to SLAM problems. It transforms the SLAM posterior into a graphical network, representing the log-likelihood of the data. It then reduces this graph using variable elimination techniques, arriving at a lowerdimensional problems that is then solved using conventional optimization techniques. As a result, GraphSLAM can generate maps with 10 8 or more features. The paper discusses a greedy algorithm for data association, and presents results for SLAM in urban environments with occasional GPS measurements.

Acquiring models of the environment belongs to the fundamental tasks of mobile robots. In the last few years several researchers have focused on the problem of simultaneous localization and mapping (SLAM). Classic SLAM approaches are passive in the sense that they only process the perceived sensor data and do not influence the motion of the mobile robot. In this paper we present a novel and integrated approach that combines autonomous exploration with simultaneous localization and mapping. Our method uses a grid-based version of the FastSLAM algorithm and at each point in time considers actions to actively close loops during exploration. By re-entering already visited areas the robot reduces its localization error and this way learns more accurate maps. Experimental results presented in this paper illustrate the advantage of our method over pervious approaches lacking the ability to actively close loops.

Abstract — Designing robots that are capable of interacting with humans in real life settings is a challenging task. One key issue is the integration of multiple modalities (e.g., mobility, physical structure, navigation, vision, audition, dialogue, reasoning) into a coherent framework. Taking the AAAI Mobile Robot Challenge (making a robot attend the National conference on Artificial Intelligence) as the experimental context, we are currently addressing hardware, software and computation integration issues involved in designing a robot capable of sophisticated interaction with humans. This paper reports on our design solutions and the current status of the work, along with the potential impacts this design will have on human-robot interaction research. Index Terms — Socially interactive mobile robot, Embodied interaction and communication, Multi-modal communication.

We present a computational approach to Learning By Observation (LBO) that allows users to program mobile robots by demonstrating a task. Unlike previous approaches, our system incorporates statistical-learning techniques and concepts from control theory to reduce the amount of domain knowledge needed to infer the intent of the user. To improve the generalization ability of the system, the user can demonstrate the task multiple times. We extract task subgoals from these demonstrations and automatically associate them with objects in the environment. As these objects move, the subgoals are updated accordingly. This gives our system the ability to learn from demonstrations performed in different environments. In this paper, we present the concepts used in our LBO system as well as experimental laboratory results in learning motor-skill tasks.

POMDPs provide a rich framework for planning and control in partially observable domains. Recent new algorithms have greatly improved the scalability of POMDPs, to the point where they can be used in robot applications. In this paper, we describe how approximate POMDP solving can be further improved by the use of a new theoretically-motivated algorithm for selecting salient information states. We present the algorithm, called PEMA, demonstrate competitive performance on a range of navigation tasks, and show how this approach is robust to mismatches between the robot’s physical environment and the model used for planning. 1