Gait generation for a simulated hexapod robot : a nonlinear dynamical systems approach

The capacity of walking in a wide variety of terrains is one of the most important features of hexapod insects. In this paper we describe a bio-inspired controller able to generate locomotion and reproduce the different type of gaits for an hexapod robot. Motor patterns are generated by coupled Central Pattern Generators, formulated as nonlinear oscillators. In order to demonstrate the robustness of the controller we developed a simulation model of the real Chiara hexapod robot where are described the most important steps of its development. Results were performed in simulation using the developed model of the Chiara hexapod robot

Hexapod locomotion : a nonlinear dynamical systems approach

The ability of walking in a wide variety of terrains is one of the most important features of hexapod insects. In this paper we describe a bio-inspired controller able to generate locomotion and switch between different type of gaits for an hexapod robot. Motor patterns are generated by coupled Central Pattern Generators formulated as nonlinear oscillators. These patterns are modulated by a drive signal, proportionally changing the oscillators frequency, amplitude and the coupling parameters among the oscillators. Locomotion initiation, stopping and smooth gait switching is achieved by changing the drive signal. We also demonstrate a posture controller for hexapod robots using the dynamical systems approach. Results from simulation using a model of the Chiara hexapod robot demonstrate the capability of the controller both to locomotion generation and smooth gait transition. The postural controller is also tested in different situations in which the hexapod robot is expected to maintain balance. The presented results prove its reliability

Deep Learning: Our Miraculous Year 1990-1991

In 2020, we will celebrate that many of the basic ideas behind the deep learning revolution were published three decades ago within fewer than 12 months in our "Annus Mirabilis" or "Miraculous Year" 1990-1991 at TU Munich. Back then, few people were interested, but a quarter century later, neural networks based on these ideas were on over 3 billion devices such as smartphones, and used many billions of times per day, consuming a significant fraction of the world's compute.Comment: 37 pages, 188 references, based on work of 4 Oct 201

Learning Image-Conditioned Dynamics Models for Control of Under-actuated Legged Millirobots

Millirobots are a promising robotic platform for many applications due to their small size and low manufacturing costs. Legged millirobots, in particular, can provide increased mobility in complex environments and improved scaling of obstacles. However, controlling these small, highly dynamic, and underactuated legged systems is difficult. Hand-engineered controllers can sometimes control these legged millirobots, but they have difficulties with dynamic maneuvers and complex terrains. We present an approach for controlling a real-world legged millirobot that is based on learned neural network models. Using less than 17 minutes of data, our method can learn a predictive model of the robot's dynamics that can enable effective gaits to be synthesized on the fly for following user-specified waypoints on a given terrain. Furthermore, by leveraging expressive, high-capacity neural network models, our approach allows for these predictions to be directly conditioned on camera images, endowing the robot with the ability to predict how different terrains might affect its dynamics. This enables sample-efficient and effective learning for locomotion of a dynamic legged millirobot on various terrains, including gravel, turf, carpet, and styrofoam. Experiment videos can be found at https://sites.google.com/view/imageconddy

Software Engineering Department Master Thesis

Son zamanlarda yapay zeka (AI), sunduğu çözümler nedeniyle bilimsel araştırmaların tüm alanlarını işgal etti. Sağlık da bir istisna değil. Diyabet dünyadaki en yaygın hastalıklardan biridir. Komplikasyonlarından biri, hastanın görüşünü bulanıklaştırabilen veya bozabilen ve körlüğün ana nedenlerinden biri olan diyabetik retinopatidir. Diyabetik retinopatinin erken teşhisi tedaviye büyük ölçüde yardımcı olabilir. Yapay Zeka ve özellikle derin öğrenme alanındaki son gelişmeler, birçok hastalığı erken evrelerinde tahmin etmek, öngörmek ve teşhis etmek için kullanılabilecek iddialı çözümler sunmaktadır. Son yıl projemizde, retina görüntülerini analiz etmek için derin öğrenmenin potansiyelini araştırdık. Diyabetik retinopati seviyelerini otomatik olarak tespit etmemizi ve sınıflandırmamızı sağlayacak bir model oluşturmak için Derin Öğrenme (DL) kavramlarını bir konvolüsyonel sinir ağı (CNN) algoritması ile inceleyeceğiz. Göz ve diyabetik retinopati, ardından farklı diyabetik retinopati türleri, diyabetik retinopatinin nedenleri, önlenmesi, teşhisi ve uygun tedavisi hakkında bir sunum yapacağız. Modellerimizi eğitmek için herkesin erişebileceği bir platform olan Google Colab'ı kullanacağız