An overview of artificial intelligence and robotics. Volume 2: Robotics

This report provides an overview of the rapidly changing field of robotics. The report incorporates definitions of the various types of robots, a summary of the basic concepts, utilized in each of the many technical areas, review of the state of the art and statistics of robot manufacture and usage. Particular attention is paid to the status of robot development, the organizations involved, their activities, and their funding

A LOW-COST ROBOT CONTROLLER AND ITS SOFTWARE PROBLEMS

In recent years the need for advanced robot control algorithms for industrial robots has grown. The deyelopment of a low-cost robot controller to support the development, implementation and testing of those algorithms which require high computational power was targeted. This paper deals wiith the requirements of an experimental controller that can be connected to a NOKIA PUMA 560 robot arm. It explains the IBM PC compatible host and the TEXAS Digital Signal Processor (DSP) based hardware. On the host computer the UNIX-like QXX real-time operating system is used. In the current phase of development the robot controller works with the classical decentralised joint control based strategy. The Advanced Robot Pogramming System (ARPS) explicit robot programming, language is implementedl

Evaluating Robustness of Visual Representations for Object Assembly Task Requiring Spatio-Geometrical Reasoning

This paper primarily focuses on evaluating and benchmarking the robustness of visual representations in the context of object assembly tasks. Specifically, it investigates the alignment and insertion of objects with geometrical extrusions and intrusions, commonly referred to as a peg-in-hole task. The accuracy required to detect and orient the peg and the hole geometry in SE(3) space for successful assembly poses significant challenges. Addressing this, we employ a general framework in visuomotor policy learning that utilizes visual pretraining models as vision encoders. Our study investigates the robustness of this framework when applied to a dual-arm manipulation setup, specifically to the grasp variations. Our quantitative analysis shows that existing pretrained models fail to capture the essential visual features necessary for this task. However, a visual encoder trained from scratch consistently outperforms the frozen pretrained models. Moreover, we discuss rotation representations and associated loss functions that substantially improve policy learning. We present a novel task scenario designed to evaluate the progress in visuomotor policy learning, with a specific focus on improving the robustness of intricate assembly tasks that require both geometrical and spatial reasoning. Videos, additional experiments, dataset, and code are available at https://bit.ly/geometric-peg-in-hole

Smart distance measurement module for football robot

Diplomová práce se zabývá vývojem dálkoměrného modulu určeného pro rozšíření senzorické výbavy fotbalového robotu kategorie MiroSot. Tento modul na vstupu přijímá data ze senzorické jednotky vyvinuté na Ústavu automatizace a měřicí techniky a z těchto dat extrahuje polohu míčku. Je srovnáno využití neuronové sítě a zjednodušené Houghovy transformace pro získání polohy těžiště míčku. V práci je popsána pomocná implementace funkcionality v prostředích MATLAB a C#.NET i hlavní implementace pro signálový mikrokontrolér Freescale MC56F8013. Výsledný modul splňuje nároky zadání a je plně funkční.The master's thesis concerns with the design of a distance measurement module destined for a MiroSot-category soccer robot. The module accepts data outputted by a sensor unit developed on Department of Control and Instrumentation and uses it to determine the ball position. Utilization of a neural network and a simplified Hough transform for ball finding is discussed. The thesis describes auxiliary implementations in MATLAB and C#.NET environments as well as the main implementation for digital signal controller Freescale MC56F8013. The resulting module meets requirements of the submission and is fully functional.

Neural Task Programming: Learning to Generalize Across Hierarchical Tasks

In this work, we propose a novel robot learning framework called Neural Task Programming (NTP), which bridges the idea of few-shot learning from demonstration and neural program induction. NTP takes as input a task specification (e.g., video demonstration of a task) and recursively decomposes it into finer sub-task specifications. These specifications are fed to a hierarchical neural program, where bottom-level programs are callable subroutines that interact with the environment. We validate our method in three robot manipulation tasks. NTP achieves strong generalization across sequential tasks that exhibit hierarchal and compositional structures. The experimental results show that NTP learns to generalize well to- wards unseen tasks with increasing lengths, variable topologies, and changing objectives.Comment: ICRA 201

The PSEIKI Report—Version 2. Evidence Accumulation and Flow of Control in a Hierarchical Spatial Reasoning System

A fundamental goal of computer vision is the development of systems capable of carrying out scene interpretation while taking into account all the available knowledge. In this report, we have focused on how the interpretation task may be aided by expected-scene information which, in most cases, would not be in registration with the perceived scene. In this report, we describe PSEIKI, a framework for expectation-driven interpretation of image data. PSEIKI builds abstraction hierarchies in image data using, for cues, supplied abstraction hierarchies in a scene expectation map. Hypothesized abstractions in the image data are geometrically compared with the known abstractions in the expected scene; the metrics used for these comparisons translate into belief values. The Dempster-Shafer formalism is used to accumulate beliefs for the synthesized abstractions in the image data. For accumulating belief values, a computationally efficient variation of Dempster’s rule of combination is developed to enable the system to deal with the overwhelming amount of information present in most images. This variation of Dempster’s rule allows the reasoning process to be embedded into the abstraction hierarchy by allowing for the propagation of belief values between elements at different levels of abstraction. The system has been implemented as a 2- panel, 5-level blackboard in OPS 83. This report also discusses the control aspects of the blackboard, achieved via a distributed monitor using the OPS83 demons and a scheduler. Various knowledge sources for forming groupings in the image data and for labeling such groupings with abstractions from the scene expectation map are also discussed

A STUDY OF MACHINE VISION IN THE AUTOMOTIVE INDUSTRY

With the growth of industrial automation, it has become increasingly important to validate the quality of every manufactured part during production. Until now, human visual inspection aided with hard tooling or machines have been the primary means to this end, but the speed of today's production lines, the complexity of production equipment and the highest standards of quality to which parts must adhere frequently, make the traditional methods of industrial inspection and control impractical, if not impossible. Subsequently, new solutions have been developed for the monitoring and control of industrial processes, in real­time. One such technology is the area of machine vision. After many years of research and development, computerised vision systems are now leaving the laboratory and are being used successfully in the factory environment. They are both robust and competitively priced as a sensing technique which has now opened up a whole new sector for automation. Machine vision systems are becoming an important integral part of the automotive manufacturing process, with applications ranging from inspection, classification, robot guidance, assembly verification through to process monitoring and control. Although the number of systems in current use is still relatively small, there can be no doubt, given the issues at stake, that the automotive industry will once again lead the way with the implementation of machine vision just as it has done robotic technology. The thesis considered the issue of machine vision and in particular, its deployment within the automotive industry. The thesis has presented work on machine vision for the prospective end-user and not the designer of such systems. It will provide sufficient background about the subject, to separate machine vision promises from reality and permit intelligent decisions regarding machine vision applications to be made. The initial part of the dissertation focussed on the strategic issues affecting the selection of machine vision at the planning stage, such as a listing of the factors to justify investment, the capability of the technology and type of problems that are associated with this relatively new but complex science. Though it is widely accepted that no two industrial machine vision systems are identical, knowledge of the basic fundamentals which underpin the structure of the technology in its application is presented. This work covered a structured description detailing typical hardware components such as camera technology, lighting systems, etc... which form an integral part of an industrial system and discussions regarding the criteria for selection are presented. To complement this work, a further section is specifically devoted to the bewildering array of vision software analysis techniques which are currently available today. A detailed description of the various techniques that are applied to images in order to make use of and understand the data contained within them are discussed and explored. Applications for machine vision fall into two main categories namely robotic guidance and inspection. Obviously within each category there are many further sub­groups. Within this context the latter part of the thesis reviews with a well structured description of several industrial case studies derived from the automotive industry, which illustrate that machine vision is capable of providing real time solutions to manufacturing based problems. In conclusion, despite the limited availability of industrially based machine vision systems, the success of implementation is not always guaranteed, as the technology imposes both technical limitations and introduce new human engineering considerations. By understanding the application and the implications of the technical requirements on both the "staging" and the "image-processing" power required of the machine vision system. The thesis has shown that the most significant elements of a successful application are indeed the lighting, optics, component design, etc... - the "Staging". From the case studies investigated, optimised "staging" has resulted in the need for less computing power in the machine vision system. Inevitably, greater computing power not only requires more time but is generally more expensive. The experience gained from the this project, has demonstrated that machine vision technology is a realistic alternative means of capturing data in real-time. Since the current limitations of the technology are well suited to the delivery process of the quality function within the manufacturing process

Semi-dense SLAM on an FPGA SoC

Deploying advanced Simultaneous Localisation and Mapping, or SLAM, algorithms in autonomous low-power robotics will enable emerging new applications which require an accurate and information rich reconstruction of the environment. This has not been achieved so far because accuracy and dense 3D reconstruction come with a high computational complexity. This paper discusses custom hardware design on a novel platform for embedded SLAM, an FPGA-SoC, combining an embedded CPU and programmable logic on the same chip. The use of programmable logic, tightly integrated with an efficient multicore embedded CPU stands to provide an effective solution to this problem. In this work an average framerate of more than 4 frames/second for a resolution of 320×240 has been achieved with an estimated power of less than 1 Watt for the custom hardware. In comparison to the software-only version, running on a dual-core ARM processor, an acceleration of 2× has been achieved for LSD-SLAM, without any compromise in the quality of the result

Concepts of automatic pattern recognition in computer vision

Call number: LD2668 .R4 CMSC 1987 N54Master of ScienceComputing and Information Science