Artificial Intelligence as a Substitute for Human Creativity

Creativity has always been perceived as a human trait, even though the exact neural mechanisms remain unknown, it has been the subject of research and debate for a long time. The recent development of AI technologies and increased interest in AI has led to many projects capable of performing tasks that have been previously regarded as impossible without human creativity. Music composition, visual arts, literature, and science represent areas in which these technologies have started to both help and replace the creative human, with the question of whether AI can be creative and capable of creation more realistic than ever. This review aims to provide an extensive perspective over several state-of-the art technologies and applications based on AI which are currently being implemented into areas of interest closely correlated to human creativity, as well as the economic impact the development of such technologies might have on those domains

Compliant multi-fingered adaptive robotic gripper

Passively compliant underactuated mechanisms are one way to obtain the finger which could accommodate to any irregular shaped and sensitive grasping object. The purpose of the underactuation is to use the power of one actuator to drive the open and close motion of the gripper. The fully compliant mechanism has multiple degrees of freedom and can be considered as an underactuated mechanism. This paper presents design of the adaptive underactuated compliant multi-fingered gripper with distributed compliance. The optimal topology of the finger structure was obtained by an iterative optimization procedure. It was proven that for real robotic applications multi-fingered grippers with three or more fingers were more suitable for stable and safe grasping.Passiven nachgiebigen unteraktuierten Mechanismen bieten die Möglichkeit solche Robotergreifer zu entwerfen die anpassfähig an irgendeine unregelmäßig geformte und empfindliche Greifobjekt sind. Der Zweck der Unteraktuierung ist nur ein Aktor für die Greifer-öffnen/schließen-Funktionen zu benutzen. Völlig nachgiebige Mechanismus hat grosse Anzahl der Freiheitsgrade und man kann als unteraktuierte Mechanismus betrachtet werden. In diesem Beitrag wird ein adaptive unteraktuierte nachgiebige Multifinger-Greifer mit verteilten Nachgiebigkeit entworfen. Die optimale Topologie der Fingerstruktur wurde durch ein iteratives Optimisationverfahren bekommen. Es wurde bewährt dass für die realen Anwendungen der Multifinger-Greifer mit drei und mehr Fingern für stabile und sichere Greifen besser geeignet ist

Integrating an autonomous robot on a dance and new technologies festival

This paper presents the results of a project to integrate an autonomous mobile robot into a modern dance performance at a dance and new technologies festival. The main goal is to integrate a simple low cost mobile robot into the dance performance, in order to study the possibilities that this kind of platforms can offer to the artists. First, this work explains the process and design to embed the robotic platform into the choreography theme. Another contribution described in this work is the system architecture proposed and built to make the robot behaviours match the artists requirements: precise, synchronized and robust robot movements. Finally, we discuss the main issues and lessons learned for this kind of robotics and arts applications and summarize the results obtained, including the successful final live performance results

Human Assisted Humanoid Robot Painter

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2012Thesis (M.Sc.) -- İstanbul Technical University, Institute of Science and Technology, 2012Günümüzde, insansı robotlar üzerindeki araştırmalar en heyecan verici ve sıcak konulardan biridir. İnsansı robotlar üzerinde insanlarla bir arada ve işbirliği içinde çalışabilen ve insanların yapamadığı görevleri yapabilen hareketli robot yaratma gibi birçok araştırma yapılmıştır. Bir diğer ilgi çekici araştırma alanı ise sanat icra edebilen robotlar yaratmaktır. Bu araştırmada, NAO H25 insan destekli insansı robot ressam anlatılmaktadır. Bu çalışmanın amacı, bir insan tarafından resim yapılırken yerine getirilen işlemlerin tamamının, görü sistemi olan insansı bir robot tarafından bir insan yardımıyla gerçekleştirebilmesidir. Bir ressam robot yaratmak konusunda, bazı önemli sorunlar için çözümler üretilmiştir. Görü ile ilgili çözülmesi gereken önemli sorunlar, robot tarafından resmi yapılacak nesnelerin bulunduğu ortamın görüntüsünün elde edilmesi, iki boyutlu nesne bölütlemesinin yapılması, nesnelerin yeraldığı ortam görüntüsünde nesneler ile arka planın birbirinden ayrılması, nesnelerin sahip olduğu renk sayısının robot tarafından kullanılabilecek en uygun renk sayısına indirgenmesi, renklerin robot tarafından algılanabilmesi, fırça darbelerinin yönünün belirlenmesi ve kullanılacak doğru rengin robot tarafından tespit edilebilmesidir. NAO insansı robotu ile ilgili çözülmesi gereken önemli sorunlar, insansı robotun resmin çizileceği yatay resim masası önünde ayakta durması, boya fırçasını kavraması, boya fırçasını başlangıç noktasında aşağı doğru indirmesi, fırça ile bir çizgi çizilmesi ve bitiş noktasında yukarı doğru kaldırmasıdır. Resim yapma işlemi üç bölüme ayrılmıştır: 1) Resmi yapılacak nesnelerin bulunduğu ortamın resminin robot tarafından elde edilmesi, 2) Resmi yapılacak nesneler ile arka planın birbirinden ayrılması, 3) Asistanı ile etkileşime geçerek robot tarafından nesnelerin resminin yapılması (örneğin robot yardımcısına “Bana mavi boyayı verebilir misin?” diyerek etkileşimde bulunabilir.) NAO H25 insansı robotun başının üzerinde bir tanesi alın, diğeri ise ağız hizasında olmak üzere iki tane dijital kamerası vardır. Robot, nesnelerin bulunduğu ortamın resmini alın hizasındaki kamerası aracılığıyla elde eder. Bu işlem neticesinde birinci bölüm tamamlanmış olur. Ortam görüntüsü elde edildikten sonra nesne ile arka planın birbirinden ayrılması maksadıyla nesneye ait görüntü elemanları ile arka plana ait görüntü elemanlarının bölütlenmesi gerekmektedir. Bu amaçla çizge tabanlı nesne bölütleme algoritması kullanılmaktadır. Bu algoritma neticesinde nesnelerin sahip olduğu renkler belirlenmiş olur ve bu renkler robot tarafından algılanır. Robotun, resmini yapacağı nesneler çok farklı renklere sahip olabilirler. Buna karşın robotun elinde var olan renk sayısı ise sınırlıdır. Bu nedenle robotun uygun renk bölütlerini belirleyebilmesi için nesnenin renk dağılımını analiz etmesi gerekmektedir. Bu konu renk azaltma ile ilgilidir. Bu çalışmada renk azaltma işlemi için K-means bölütleme algoritması kullanılmıştır. Nesne ile arka planın birbirinden ayrılması, renklerin algılanması ve renk azaltma işlemlerinin tamamlanmasından sonra, robotun fırça darbelerini tuval üzerine nasıl uygulayacağına karar vermesi gerekmektedir. Bu maksatla fırça darbelerinin yönü belirlenmelidir. Fırça darbelerinin yönünün belirlenmesi için ortam resmindeki görüntü elemanlarının yön bilgileri kullanılmıştır. Fırçanın yönünün belirlenmesinden sonra NAO her bir renk bölütünde boyayabileceği uygun bir alan bulmaya çalışır. Böyle bir alan bulduğunda, yardımcısını çağırır ve kendisinden boyama yapacağı rengi vermesini ister. Yardımcı renk kâsesini NAO’ya verir. NAO, boya kâsesini kontrol eder ve doğru boya olup olmadığını tespit eder. Eğer doğru boya ise yardımcısına teşekkür eder, doğru boya değilse o zaman yardımcısından doğru boyayı kendisine vermesini ister. NAO doğru boyayı elde ettiğinde resim yapmaya başlar. Öncelikle kolunu başlangıç noktasına götürür ve boya fırçasını aşağı indirir. Daha sonra kolunu bitiş noktasına götürür ve fırçayı kaldırır. NAO bulduğu ilk uygun alanı boyadıktan sonra bir başka uygun alan arar. İlk seviyedeki bütün renkler boyandıktan sonra, bir sonraki seviyeyi daha ince fırça ile boyamaya başlar. Resim yapma işleminde kullanılan seviye sayısı, boyama için kullanılan fırça sayısı ile ilgilidir. Kullanılacak fırça sayısı yapılacak olan resmin tamamlanacağı seviye sayısını belirler. Bu sistemde boyama yaparken fırçayı dengeli bir yönde hareket ettirmek önemlidir. Aksi taktirde fırça, robotun elinden kayabilir veya çizgiler yanlış bir şekilde çizilebilir. Boya fırçası, robota yakın olan taraftan uzak olan tarafa doğru hareket ederse kavrama dengeli bir hale gelir. Bununla birlikte, boya fırçası soldan sağa doğru ve uzak taraftan yakın tarafa doğru hareket ederse o zaman kavrama dengesiz bir hale gelir. Webots, hareketli robotların modellenebilmesi, programlanabilmesi ve benzetiminin yapılabilmesi için kullanılan bir geliştirme ortamıdır. Resim yapma işlemi, Webots benzetim programında NAO H25 insansı robotu kullanılarak yaratılan jenerik bir ortamda gerçekleştirildi. Bu ortama bir adet sandalye, sandalyenin üzerine bir adet küçük teneke kutu, sandalyenin önüne ise bir tanesi küçük bir tanesi büyük olmak üzere iki adet farklı renkte top yerleştirildi. Insansı robot bu nesnelerin resimlerini jenerik ortamdaki tuval üzerine yaptı. Benzetim ortamında robot kolunu ve ellerini oynatırken, aynı zamanda resim de yavaş yavaş renklerle doldu ve resim ortaya çıktı. Gerçek ortamda gerçekleştirilen çalışmalarda yatay bir masa, masa üzerine yerleştirilmiş tuval olarak kullanılan bir kağıt, resmini yapacağımız bir saksı ve guaj boya kullanılmıştır. NAO, kollarını x-y düzlemi üzerinde bir çizici gibi kullanarak ve üst gövdesini koluyla birlikte hareket ettirerek masa üzerindeki farklı noktalara erişebilmektedir. Sistem gerçek robot üzerine taşındıktan sonra, özellikle de NAO’nun eklemli kollarını sabit bir yüzey üzerinde resim yapması için hareket ettirmesi gereksiniminden kaynaklanan bazı teknik zorluklar ortaya çıkmıştır. Çünkü NAO’nun eklemli kollarının hareket kısıtlaması vardır. Sabit yüzey üzerinde istediğibir noktaya istediği bir şekilde hareket edememektedir. Uzak noktalara erişebilmesi için kollarıyla birlikte üst gövdesini de hareket ettirmesi gerekmektedir. Üst gövdesini hareket ettirirken aynı zamanda ayakta olması nedeniyle kendi dengesini de sağlaması gerekmektedir. NAO’nun kolları ile ilgili kısıtlamalarının yanında elleri ile ilgili de bazı kısıtlamaları vardır. NAO’nun her bir elinde üç parmağı vardır. Parmaklarından hiçbirisini tek başına hareket ettirememekte, sadece parmaklarını açıp kapayabilmektedir. Bununla birlikte parmaklarının üzerinde herhangi bir güç veya dokunma sensörü bulunmamaktadır. Bu kısıtlamadan dolayı insansı robot, fırçayı kendi imkânları ile insana benzer bir şekilde hissederek kavrayamamaktadır. Elini açtığı anda fırça elinin içine sabitlenmekte ve daha sonra elini kapatmak suretiyle fırçayı kavramaktadır.Currently, research on humanoid robots is one of the most exciting and hot topics. Many researches have been studied on humanoid robots like creating a mobile robot that is able to coexist and collaborate with humans and to perform tasks that humans cannot. Another attractive research area is creating artist robots that are able to perform arts. In this research, NAO H25 human assisted humanoid robot painter is described. The aim of this study to reproduce the whole painting process by a humanoid robot with a vision system with the assistance of a human. To create a robot painter, solutions for some key problems are generated. The key problems regarding vision are obtaining the image of the environment in which the objects that the robot paints, 2D object segmentation, extracting the objects from environment, color perception, reducing the number of color to the optimum set of colors that can be used by the robot and determining orientation for brush strokes. The key problems regarding NAO humanoid robot are standing in front of the painting table, grasping the paintbrush, moving down the paintbrush to the start point, drawing a line and moving up the paintbrush from end point. Painting process are divided into three phases: obtaining the image of the environment that the objects are in, extracting the objects to paint and painting by a robot to be in interaction with its assistant (i.e. Robot says to human: “Could you give me the blue color?” ). NAO H25 humanoid robot has two CMOS-Complementary Metal Oxide Semiconductor digital cameras on its head. It acquires the image of the environment via its camera. After taking the image of the environment, the foreground and background pixels should be segmented to extract objects from the environment. Graph-based object segmentation is used for this purpose. Once this algorithm applied to the image, all colors are determined and can be percept by the robot. The robot must analyze color distribution of the object to determine the appropriate color segments for the optimum set of colors that can be used by the robot. This issue is related with the color reduction. K-means clustering method is used for color reduction. When the color reduction is finished, the orientation used to guide brush strokes should be computed in order to imitate human painting style. The gradient information is used to calculate orientation. After computing orientation, NAO tries to find an appropriate area in each color segment derived from the second phase (extracting objects from the environment). If the robot finds such an area, it calls its assistant and asks for the color that it will paint. Its assistant gives the color case to it. NAO controls the color and if it is true, it thanks to child. If the color is incorrect, the robot asks for the right color. When the robot gets the right color, it starts drawing. NAO moves its arm to the start position and pushes the paintbrush down. Then it moves its arm to the end position and pulls the brush up. In this system, it is important to paint in a stable direction. The grasping becomes unstable when a brush moves from near side to far side. However, it is stable when a brush moves from left to right and from far side to near side. For the next time, the robot looks for a new area to paint. All of the color in one level is painted; the next level starts with a thinner brush. The number of level is related with the brush number used for painting. Webots is a development environment used to model, program and simulate mobile robots. This painting art is simulated in a generic environment in Webots with NAO. A chair, a can on the chair and two balls of different sizes in front of the chair are located. The humanoid robot paints these objects on a canvas. In the simulator, the robot moves its arm and its hand, and the simulated picture is filled with colors, as the robot’s hand is moved. In real environment, a table, a cardboard used as a canvas placed on a table, a pot the robot paints and gouache paints are used. Currently, the robot paints on a table top, using its arm like a plotter on an x-y plane and moves its upper torso with the arm to reach different locations on the table. Once the system is moved on the real robot, there are some technical challenges, primarily due to the requirement that the NAO uses its articulated arms to paint on a fixed surface (canvas). It has some limitations on its articulated arms. NAO H25 humanoid robot does not have any force and tactile sensors on its fingers. Because of this constraint, the paintbrush is fixed in the robot’s hand to manipulate it while painting.Yüksek LisansM.Sc

Humanoid robot painter: Visual perception and high-level planning

Abstract -This paper presents visual perception discovered in high-level manipulator planning for a robot to reproduce the procedure involved in human painting. First, we apply a technique of 2D object segmentation that considers region similarity as an objective function and edge as a constraint with artificial intelligent used as a criterion function. The system can segment images more effectively than most of existing methods, even if the foreground is very similar to the background. Second, we propose a novel color perception model that shows similarity to human perception. The method outperforms many existing color reduction algorithms. Third, we propose a novel global orientation map perception using a radial basis function. Finally, we use the derived model along with the brush's position-and force-sensing to produce a visual feedback drawing. Experiments show that our system can generate good paintings including portraits

Painting Robot with Multi-Fingered Hands and Stereo Vision

Abstract — In this paper, we describe a painting robot with multi-fingered hands and stereo vision. The goal of this study is for the robot to reproduce the whole procedure involved in human painting. A painting action is divided into three phases: obtaining a 3D model, composing a picture model, and painting by a robot. In this system, various feedback techniques including computer vision and force sensors are used. As experiments, an apple and a human silhouette are painted on a canvas using this system. I

Design and Development of Sensor Integrated Robotic Hand

Most of the automated systems using robots as agents do use few sensors according to the need. However, there are situations where the tasks carried out by the end-effector, or for that matter by the robot hand needs multiple sensors. The hand, to make the best use of these sensors, and behave autonomously, requires a set of appropriate types of sensors which could be integrated in proper manners. The present research work aims at developing a sensor integrated robot hand that can collect information related to the assigned tasks, assimilate there correctly and then do task action as appropriate. The process of development involves selection of sensors of right types and of right specification, locating then at proper places in the hand, checking their functionality individually and calibrating them for the envisaged process. Since the sensors need to be integrated so that they perform in the desired manner collectively, an integration platform is created using NI PXIe-1082. A set of algorithm is developed for achieving the integrated model. The entire process is first modelled and simulated off line for possible modification in order to ensure that all the sensors do contribute towards the autonomy of the hand for desired activity. This work also involves design of a two-fingered gripper. The design is made in such a way that it is capable of carrying out the desired tasks and can accommodate all the sensors within its fold. The developed sensor integrated hand has been put to work and its performance test has been carried out. This hand can be very useful for part assembly work in industries for any shape of part with a limit on the size of the part in mind. The broad aim is to design, model simulate and develop an advanced robotic hand. Sensors for pick up contacts pressure, force, torque, position, surface profile shape using suitable sensing elements in a robot hand are to be introduced. The hand is a complex structure with large number of degrees of freedom and has multiple sensing capabilities apart from the associated sensing assistance from other organs. The present work is envisaged to add multiple sensors to a two-fingered robotic hand having motion capabilities and constraints similar to the human hand. There has been a good amount of research and development in this field during the last two decades a lot remains to be explored and achieved. The objective of the proposed work is to design, simulate and develop a sensor integrated robotic hand. Its potential applications can be proposed for industrial environments and in healthcare field. The industrial applications include electronic assembly tasks, lighter inspection tasks, etc. Application in healthcare could be in the areas of rehabilitation and assistive techniques. The work also aims to establish the requirement of the robotic hand for the target application areas, to identify the suitable kinds and model of sensors that can be integrated on hand control system. Functioning of motors in the robotic hand and integration of appropriate sensors for the desired motion is explained for the control of the various elements of the hand. Additional sensors, capable of collecting external information and information about the object for manipulation is explored. Processes are designed using various software and hardware tools such as mathematical computation MATLAB, OpenCV library and LabVIEW 2013 DAQ system as applicable, validated theoretically and finally implemented to develop an intelligent robotic hand. The multiple smart sensors are installed on a standard six degree-of-freedom industrial robot KAWASAKI RS06L articulated manipulator, with the two-finger pneumatic SHUNK robotic hand or designed prototype and robot control programs are integrated in such a manner that allows easy application of grasping in an industrial pick-and-place operation where the characteristics of the object can vary or are unknown. The effectiveness of the actual recommended structure is usually proven simply by experiments using calibration involving sensors and manipulator. The dissertation concludes with a summary of the contribution and the scope of further work

Cooperative Manipulation using a Magnetically Navigated Microrobot and a Micromanipulator

The cooperative manipulation of a common object using two or more manipulators is a popular research field in both industry and institutions. Different types of manipulators are used in cooperative manipulation for carrying heavy loads and delicate operations. Their applications range from macro to micro. In this thesis, we are interested in the development of a novel cooperative manipulator for manipulation tasks in a small workspace. The resultant cooperative manipulation system consists of a magnetically navigated microrobot (MNM) and a motorized micromanipulator (MM). The MNM is a small cylinder permanent magnet with 10mm diameter and 10mm height. The MM model is MP-285 which is a commercialized product. Here, the MNM is remotely controlled by an external magnetic field. The property of non-contact manipulation makes it a suitable choice for manipulation in a confined space. The cooperative manipulation system in this thesis used a master/slave mechanism as the central control strategy. The MM is the master side. The MNM is the slave side. During the manipulation process, the master manipulator MM is always position controlled, and it leads the object translation according to the kinematic constraints of the cooperative manipulation task. The MNM is position controlled at the beginning of the manipulation. In the translation stage, the MNM is switched to force control to maintain a successful holding of the object, and at the same time to prevent damaging the object by large holding force. Under the force control mode, the motion command to the MNM is calculated from a position-based impedance controller that enforces a relationship between the position of the MNM and the force. In this research, the accurate motion control of both manipulators are firstly studied before the cooperative manipulation is conducted. For the magnetic navigation system, the magnetic field in its workspace is modeled using an experimental measurement data-driven technique. The developed model is then used to develop a motion controller for navigating of a small cylindrical permanent magnet. The accuracy of motion control is reached at 20 µm in three degrees of freedom. For the motorized micromanipulator, a standard PID controller is designed to control its motion stage. The accuracy of the MM navigation is 0.8 µm. Since the MNM is remotely manipulated by an external magnetic field in a small space, it is challenging to install an on-board force sensor to measure the contact force between the MNM and the object. Therefore, a dual-axial o_-board force determination mechanism is proposed. The force is determined according to the linear relation between the minimum magnetic potential energy point and the real position of the MNM in the workspace. For convenience, the minimum magnetic potential energy point is defined as the Bmax in the literature. In this thesis, the dual-axial Bmax position is determined by measuring the magnetic ux density passing through the workspace using four Hall-effect sensors installed at the bottom of an iron pole-piece. The force model is experimentally validated in a horizontal plane with an accuracy of 2 µN in the x- and y- direction of horizontal planes. The proposed cooperative manipulator is then used to translate a hard-shell small object in two directions of a vertical plane, while one direction is constrained with a desired holding force. During the manipulation process, a digital camera is used to capture the real-time position of the MNM, the MM end-effector, and the manipulated object. To improve the performance of force control on the MNM, the proposed dual-axial force model is used to examine the compliant force control of the MNM while it is navigated to contact with uncertain environments. Here, uncertain refers to unknown environmental stiffness. An adaptive position-based impedance controller is implemented to estimate the stiffness of the environment and the contact force. The controller is examined by navigating the MNM to push a thin aluminum beam whose stiffness is unknown. The studied cooperative manipulation system has potential applications in biomedical microsurgery and microinjection. It should be clarified that the current system setup with 10mm ×10 mm MNM is not proper for this micromanipulation. In order to conduct research on microinjection, the size of the MNM and the end-effector of the MNM should be down-scaled to micrometers. In addition, the navigation accuracy of the MNM should also be improved to adopt the micromanipulation tasks