Active Object Search Exploiting Probabilistic Object–Object Relations

\u3cp\u3eThis paper proposes a probabilistic object-object relation based approach for an active object search. An important role of mobile robots will be to perform object-related tasks and active object search strategies deal with the non-trivial task of finding an object in unstructured and dynamically changing environments. This work builds further upon an existing approach exploiting probabilistic object-room relations for selecting the room in which an object is expected to be. Learnt object-object relations allow to search for objects inside a room via a chain of intermediate objects. Simulations have been performed to investigate the effect of the camera quality on path length and failure rate. Furthermore, a comparison is made with a benchmark algorithm based the same prior knowledge but without using a chain of intermediate objects. An experiment shows the potential of the proposed approach on the AMIGO robot.\u3c/p\u3

Using monocular SLAM for position-based visual control

Most of the existing visual control algorithms make use of pairwise geometry constraints to define the relation between the control input of the robot and the dynamics of tracked features in an image. The assumption is that feature correspondences will be available between the current image and the goal image, which do not always hold. For example, if a non-holonomic robot has to turn a large angle to reach a goal image, it most surely will lose track of this goal image. As another limitation, the use of pairwise geometry needs to change its underlying model depending on the geometric configuration of the current pair of frames –usually, from fundamental to homography matrix. In order to cope with these two limitations, this paper proposes the use of geometric maps from SLAM (Simultaneous Localization and Mapping) for visual control. A SLAM map summarizes feature tracks by registering them, along with the camera position, in a 3D common reference frame. Even when a feature goes out of sight and the track is lost; it remains registered in the 3D scene and hence usable for the control. Using a map also makes the control independent of the geometric configuration of two particular frames. As a proof of concept, we present two experiments: In the first one, a low-cost robot (build with Lego NXT and equipped with a 320x240 black-and-white camera) navigates around an object only relying on monocular information and even when the object comes out of view in the first frames of the input image sequence. In the second one, the robot is able to go back to an initial position without presenting degeneracies

Set-point variation in learning schemes with applications to wafer scanners

This paper presents a finite impulse response strategy to deal with set-point variation in learning schemes. On the basis of converged learning forces obtained with learning control at a specific acceleration set-point profile, a finite impulse response mapping is derived to generalize the learned forces at a specific set-point toward arbitrary set-point profiles, thus relaxing the need for further learning. The above strategy is applied to the motion control systems of a wafer scanner in a multi-input multi-output feed-forward setting, where a variety of set-point profiles is used. Industrial potential is demonstrated via robustness to set-point variation and the improvements obtained in settling-time reduction

Werkwijze en inrichting voor het met behulp van een elektrisch veld uit een materiaal vervaardigen en sturen van een vezel, en voorwerp aldus vervaardigd

Geen samenvatting beschikbaa

A method and a device for forming a fibre from a material and directing said fibre, using an electric field, and an object thus formed

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Face recognition : implementation of face recognition on AMIGO

In this (traineeship)report two possible methods of face recognition were presented. The first method describes how to detect and recognize faces by using the SURF algorithm. This algorithm finally was not used for recognizing faces, with the reason that the Eigenface algorithm was an already tested and proven method for facial recognition, which did not hold for the SURF method. This does not mean that SURF would not provide good results for face recognition. From the test data it was clear that SURF had some good results, but as mentioned before these results did not always have the same quality. The second method was the Eigenface method. Because this method proved to be capable of recognizing faces, it was implemented on AMIGO. The algorithm was used during the RoboCup@home challenges in Germany. It was capable of acquiring training faces, which was the first important step. After this acquiring step, due to a combination of an error in the world model and face detector, the algorithm ended up in an endless loop. Further results of the actual implantation on AMIGO are therefore still unknown

Design and implementation of a generic intercept strategy for soccer robots

The Robocup MSL(Middle Size League) is a robot soccer league. In the Robocup MSL a team consists of 5 robots playing soccer autonomously. A very important aspect during a soccer match is intercepting the ball. Intercepting the ball occurs in many different situations. For example when the opponent is in ball possession or when the ball moves free over the field. The traineeship assignment is to design and implement a more successful strategy that is situation dependent, easy to extend and easy to change. The existing software of a soccer robot is created in Matlab Simulink with C code. An important subject during the traineeship is to do research on how and with which program to implement the intercept strategy. The first step in the design of the intercept strategy, is analyzing the many different situations that can occur during a MSL soccer march. With the situations defined the intercept strategy is designed in flowchart form. There are many different programs to implement the intercept strategy in flowchart form, in code. After comparing the pros and cons of these programs, the intercept strategy in flowchart form is implemented in ROS(Robot Operating System)