Enrico Pagello

Image-Based Monte-Carlo Localisation with Omnidirectional Images

Monte Carlo localisation generally requires a metrical map of the environment to calculate a robots position from the posterior probability density of a set of weighted samples. Image-based localisation, which matches a robots current view of the environment with reference views, fails in environments with perceptual aliasing. The method we present in this paper is experimentally demonstrated to overcome these disadvantages in a large indoor environment by combining Monte Carlo and image-based localisation. It exploits the properties of the Fourier transform of omnidirectional images, while weighting the samples according to the similarity among images. We also introduce a novel strategy for solving the “kidnapped robot problem”.

Cooperative behaviors in multi-robot systems through implicit communication

Abstract We illustrate the Cooperation through Implicit Communication behavior-based approach used for developing the PaSo-Team (The University of Pa dua Simulated So ccer Robot Team ), a multi-robot software system for soccer robot competitions promoted by the RoboCup Simulation League. The configuration of the environment, namely the robots’ relative positions depending on both the global task and the game dynamics, provides a source of implicit information about the robots’ intention to be involved in collective actions, making them able to cooperate implicitly. The soccer team performance can be tuned by triggering the arbitration module of any single robot to generate, as many as possible, suitable situations which hint to the team the action of scoring the goal. Some macroscopic parameters can be usefully introduced to evaluate the evolution of the whole multi-robot software system.

Multirobot motion coordination in space and time

This paper describes a solution to the multirobot motion planning problem based on a decoupled analysis in the space domain and in the time domain. It investigates the practical use of the notion of motion plan quality and of the motion plan robustness measures for computing safe motions. The use of anytime algorithms allows one to evaluate the opportunity of looking for alternative solution paths by generating small variations of robot motions affecting both its geometrical path and its scheduled velocity. By using the concept of plan robustness, several alternative paths are generated and evaluated through various performance indices and impact factors, using heuristic rules. These indices allow one to know how much a variation affects a given plan. Finally, some recent experiments are outlined.

Range-only SLAM with a mobile robot and a Wireless Sensor Networks

This paper presents the localization of a mobile robot while simultaneously mapping the position of the nodes of aWireless Sensor Network using only range measurements. The robot can estimate the distance to nearby nodes of the Wireless Sensor Network by measuring the Received Signal Strength Indicator (RSSI) of the received radio messages. The RSSI measure is very noisy, especially in an indoor environment due to interference and reflections of the radio signals. We adopted an Extended Kalman Filter SLAM algorithm to integrate RSSI measurements from the different nodes over time, while the robot moves in the environment. A simple pre-processing filter helps in reducing the RSSI variations due to interference and reflections. Successful experiments are reported in which an average localization error less than 1 m is obtained when the SLAM algorithm has no a priori knowledge on the wireless node positions, while a localization error less than 0.5 m can be achieved when the position of the node is initialized close to the their actual position. These results are obtained using a generic path loss model for the trasmission channel. Moreover, no internode communication is necessary in the WSN. This can save energy and enables to apply the proposed system also to fully disconnected networks

Omnidirectional vision scan matching for robot localization in dynamic environments

The localization problem for an autonomous robot moving in a known environment is a well-studied problem which has seen many elegant solutions. Robot localization in a dynamic environment populated by several moving obstacles, however, is still a challenge for research. In this paper, we use an omnidirectional camera mounted on a mobile robot to perform a sort of scan matching. The omnidirectional vision system finds the distances of the closest color transitions in the environment, mimicking the way laser rangefinders detect the closest obstacles. The similarity of our sensor with classical rangefinders allows the use of practically unmodified Monte Carlo algorithms, with the additional advantage of being able to easily detect occlusions caused by moving obstacles. The proposed system was initially implemented in the RoboCup Middle-Size domain, but the experiments we present in this paper prove it to be valid in a general indoor environment with natural color transitions. We present localization experiments both in the RoboCup environment and in an unmodified office environment. In addition, we assessed the robustness of the system to sensor occlusions caused by other moving robots. The localization system runs in real-time on low-cost hardware.

Overview of RoboCup-99

RoboCup-97, The First Robot World Cup Soccer Games and Conferences, was held at the Fifteenth International Joint Conference on Artificial Intelligence. There were two leagues: real robot and simulation. 10 teams participated in the real robot league and 29 teams did in the simulation league. The World Champions are CMUnited (CMU, U.S.A.) for the Small-Size league, Dreamteam (Univ. of Southern California, U.S.A.) and Trackies (Osaka Univ., Japan) for the Middle-Size league, and At-Humboldt (Humboldt Univ., Germany) for the Simulation league. The Scientific Challenge Award was given to Sean Luke (Univ. of Maryland, U.S.A.) for his genetic programming based simulation team and the Engineering Challenge Awards was given to UttoriUnited (Utsunomiya Univ., Toyo Univ. and RIKEN, JAPAN) and RMIT (Royal Melbourne Institute of Technology, Australia) for designing novel omni-directional driving mechanisms. RoboCup-98, the Second Robot World Cup Soccer Games and Conferences, will be held at the Third International Conference on Multi-Agent Systems at Paris, France in July 1998.

A visual odometry framework robust to motion blur

Motion blur is a severe problem in images grabbed by legged robots and, in particular, by small humanoid robots. Standard feature extraction and tracking approaches typically fail when applied to sequences of images strongly affected by motion blur. In this paper, we propose a new feature detection and tracking scheme that is robust even to non-uniform motion blur. Furthermore, we developed a framework for visual odometry based on features extracted out of and matched in monocular image sequences. To reliably extract and track the features, we estimate the point spread function (PSF) of the motion blur individually for image patches obtained via a clustering technique and only consider highly distinctive features during matching. We present experiments performed on standard datasets corrupted with motion blur and on images taken by a camera mounted on walking small humanoid robots to show the effectiveness of our approach. The experiments demonstrate that our technique is able to reliably extract and match features and that it is furthermore able to generate a correct visual odometry, even in presence of strong motion blur effects and without the aid of any inertial measurement sensor.

A solid modelling system for robot action planning

The characteristics of several solid representation schemes are discussed with respect to their possible use in robot action planning systems. The World Modeler (WM), a solid modeler developed by combining the generalized cylinder approach with constructive solid geometry, is presented. An efficient algorithm for computing collision among convex polyhedrons, that utilizes the internal geometric data structure of the World Modeler is explained.<<ETX>>

Cooperation Issues and Distributed Sensing for Multirobot Systems

This paper considers the properties a multirobot system should exhibit to perform an assigned task cooperatively. Our experiments regard specifically the domain of RoboCup middle-size league (MSL) competitions. But the illustrated techniques can be usefully applied also to other service robotics fields like, for example, videosurveillance. Two issues are addressed in the paper. The former refers to the problem of dynamic role assignment in a team of robots. The latter concerns the problem of sharing the sensory information to cooperatively track moving objects. Both these problems have been extensively investigated over the past years by the MSL robot teams. In our paper, each individual robot has been designed to become reactively aware of the environment configuration. In addition, a dynamic role assignment policy among teammates is activated, based on the knowledge about the best behavior that the team is able to acquire through the shared sensorial information. We present the successful performance of the Artisti Veneti robot team at the MSL Challenge competitions of RoboCup-2003 to show the effectiveness of our proposed hybrid architecture, as well as some tests run in laboratory to validate the omnidirectional distributed vision system which allows us to share the information gathered by the omnidirectional cameras of our robots

Designing an Omnidirectional Vision System for a Goalkeeper Robot

The aim of this paper is to provide a guideline for the design of an omnidirectional vision system for the Robocup domain. We report the design steps undertaken, with a detailed description of the design of an omnidirectional mirror with a custom profile. In the second part, we present the software written to exploit the properties of the designed mirror. The application for which the vision system is designed is the Middle-Size Robocup Championship. The task performed by the vision system is to localise the robot and to locate the ball and other robots in the field of play. Several practical tricks and suggestions are provided.