Microsoft Robotics Studio - Robots Programming

Import 29/09/2010V posledných rokoch sa robotika stala dostupnou širšiemu okruhu užívateľov, čím bol umožnený jej prudký rozvoj. Sprístupnenie robotiky napríklad žiakom základných škôl bolo umožnené vývojom technológií, ktoré dovoľujú konštrukciu a programovanie robotov bez znalosti pokročilých programovacích techník. Zámerom tejto práce je ukážka spolupráce LEGO Mindstorms NXT a Microsoft Robotics Studio. Okrem toho sa práca zameriava na určovanie polohy robotov, pričom využíva knižnicu Emgu CV určenú pre prácu s grafikou. Súčasťou práce je popis problémov, ktoré boli pri vývoji nájdené a popis ich možného riešenia.Robotics has become accesible to wider audience of users in last few years, what allowed to its rapid development. Making robotics accesible for example to students of primary schools was caused by development of new technologies, which allow construction and programming of robots without the requirement of advanced programming knowledge. The goal of this thesis is to show interaction between LEGO Mindstorms NXT and Microsoft Robotics Studio. In addition the thesis focuses on how to identify position of robots, using the Emgu CV library focused on image processing. The thesis also contains description of problems that were found in development and their possible solutions.Prezenční456 - Katedra informatikydobř

A development approach to industrial robots programming

This paper proposes a development approach to industrial robot programming, that includes: a truly high level and declarative language; an easy-to-use frontend; an intermediate representation; an automatic generator of the robot code generators. So, we introduce a new paradigm to program industrial robots, that focus on the modeling of the system, rather than on the robot. It will improve the programming and maintenance tasks, allowing the reuse of source code, because this source code will be machine independent

Approximate Discrete Probability Distribution Representation using a Multi-ResolutionBinary Tree

Computing and storing probabilities is a hard problem as soon as one has to deal with complex distributions over multiples random variables. The problem of efficient representation of probability distributions is central in term of computational efficiency in the field of probabilistic reasoning. The main problem arises when dealing with joint probability distributions over a set of random variables: they are always represented using huge probability arrays. In this paper, a new method based on a binary-tree representation is introduced in order to store efficiently very large joint distributions. Our approach approximates any multidimensional joint distributions using an adaptive discretization of the space. We make the assumption that the lower is the probability mass of a particular region of feature space, the larger is the discretization step. This assumption leads to a very optimized representation in term of time and memory. The other advantages of our approach are the ability to refine dynamically the distribution every time it is needed leading to a more accurate representation of the probability distribution and to an anytime representation of the distribution

Bayesian Robot Programming

International audienceWe propose a new method to program robots based on Bayesian inference and learning. It is called BRP for Bayesian Robot Programming. The capacities of this programming method are demonstrated through a succession of increasingly complex experiments. Starting from the learning of simple reactive behaviors, we present instances of behavior combinations, sensor fusion, hierarchical behavior composition, situation recognition and temporal sequencing. This series of experiments comprises the steps in the incremental development of a complex robot program. The advantages and drawbacks of BRP are discussed along with these different experiments and summed up as a conclusion. These different robotics programs may be seen as an illustration of probabilistic programming applicable whenever one must deal with problems based on uncertain or incomplete knowledge. The scope of possible applications is obviously much broader than robotics

Expressing Bayesian Fusion as a Product of Distributions: Application to Randomized Hough Transform

Data fusion is a common issue of mobile robotics, computer assisted medical diagnosis or behavioral control of simulated character for instance. However data sources are often noisy, opinion for experts are not known with absolute precision, and motor commands do not act in the same exact manner on the environment. In these cases, classic logic fails to manage efficiently the fusion process. Confronting different knowledge in an uncertain environment can therefore be adequately formalized in the bayesian framework. Besides, bayesian fusion can be expensive in terms of memory usage and processing time. This paper precisely aims at expressing any bayesian fusion process as a product of probability distributions in order to reduce its complexity. We first study both direct and inverse fusion schemes. We show that contrary to direct models, inverse local models need a specific prior in order to allow the fusion to be computed as a product. We therefore propose to add a consistency variable to each local model and we show that these additional variables allow the use of a product of the local distributions in order to compute the global probability distribution over the fused variable. Finally, we take the example of the Randomized Hough Transform. We rewrite it in the bayesian framework, considering that it is a fusion process to extract lines from couples of dots in a picture. As expected, we can find back the expression of the Randomized Hough Transform from the literature with the appropriate assumptions

Bayesian Programming Multi-Target Tracking: an Automotive Application

A prerequisite to the design of future Advanced Driver Assistance Systems for cars is a sensing system providing all the information required for high-level driving assistance tasks. In particular, target tracking is still challenging in urban trafc situations, because of the large number of rapidly maneuvering targets. The goal of this paper is to present an original way to perform target position and velocity, based on the occupancy grid framework. The main interest of this method is to avoid the decision problem of classical multi-target tracking algorithms. Obtained occupancy grids are combined with danger estimation to perform an elementary task of obstacle avoidance with an electric car

Sustaining interaction in a mathematical community of practice

This paper focuses on an activity in which students explore sequences through a game, using ToonTalk programming and a web-based collaboration system. Our analytical framework combines theory of communities of practice with domain epistemology. We note three factors which influence the length and quality of interactions: facilitation, reciprocation and audience-awareness

Current possibilities and trends for programming of industrial robots

Bakalářská práce je zaměřená na popis aktuálních programovacích metod průmyslových robotů. Porovnává hlavní typy metod a seznamuje s novými trendy v programování robotů jako jsou silomomentové řízení, či spolupráce mezi člověkem a robotem. Výsledkem práce je poukázat na výhody a nevýhody těchto typů a specifikovat jejich vhodné využití v průmyslové sféře.Bachelor thesis focuses on describing of actual methods of industrial robots' programming. It compares main types of methods and informs about new trends in robots' programming as are force/torque control and human-robot interaction. Product of this thesis is to point out pros and cons of this types and to specify their proper use in industrial sphere.

Using Bayesian Programming for Multisensor Multi-Target Tracking in Automative Applications

A prerequisite to the design of future Advanced Driver Assistance Systems for cars is a sensing system providing all the information required for high-level driving assistance tasks. Carsense is a European project whose purpose is to develop such a new sensing system. It will combine different sensors (laser, radar and video) and will rely on the fusion of the information coming from these sensors in order to achieve better accuracy, robustness and an increase of the information content. This paper demonstrates the interest of using probabilistic reasoning techniques to address this challenging multi-sensor data fusion problem. The approach used is called Bayesian Programming. It is a general approach based on an implementation of the Bayesian theory. It was introduced rst to design robot control programs but its scope of application is much broader and it can be used whenever one has to deal with problems involving uncertain or incomplete knowledge

Learning by observation through system identification

In our previous works, we present a new method to program mobile robots —“code identification by demonstration”— based on algorithmically transferring human behaviours to robot control code using transparent mathematical functions. Our approach has three stages: i) first extracting the trajectory of the desired behaviour by observing the human, ii) making the robot follow the human trajectory blindly to log the robot’s own perception perceived along that trajectory, and finally iii) linking the robot’s perception to the desired behaviour to obtain a generalised, sensor-based model. So far we used an external, camera based motion tracking system to log the trajectory of the human demonstrator during his initial demonstration of the desired motion. Because such tracking systems are complicated to set up and expensive, we propose an alternative method to obtain trajectory information, using the robot’s own sensor perception. In this method, we train a mathematical polynomial using the NARMAX system identification methodology which maps the position of the “red jacket” worn by the demonstrator in the image captured by the robot’s camera, to the relative position of the demonstrator in the real world according to the robot. We demonstrate the viability of this approach by teaching a Scitos G5 mobile robot to achieve door traversal behaviour