Dört bacaklı robotlar için önizlemeli kontrol ile sıfır moment noktası tabanlı yürüme yörüngesi sentezi

Bacakları üzerinde hareket eden robotların engel aşma konusunda önemli avantajları söz konusudur. Özellikle dört bacaklı robotların değişken arazi yapıları üzerinde birçok uygulamaları düşünülmektedir. Bu çalışmada, dört bacaklı bir robotun düz zemin üzerinde hızlı yol almasına yönelik tırıs türü ilerleme üzerinde durulmaktadır. Sıfır Moment Noktası (SMN) karalılık kriterine ve Doğrusal Ters Sarkaç Modeli’ne (DTSM) dayalı bir yürüme referansı sentez yöntemi sunulmaktadır. Tırıs ilerleme için bir SMN referans yörüngesi önerilmiş, bu yörüngeden, önizlemeli kontrol yaklaşımı ile Robot Ağırlık Merkezi (RAM) için bir referans yörünge elde edilmiştir. Oluşturulan ağırlık merkezi yörüngesi ters kinematik yöntemi ile bacak eklemlerinin konum referanslarının hesaplanmasında kullanılmıştır. Önerilen referans sentezi yöntemi, 16 serbestlik dereceli bir robot modeli ile üç boyutlu ve tam dinamikli bir simülasyon ortamında denenmiştir. Simülasyon sonuçları sunulan yaklaşımın başarılı olduğunu göstermektedir

Quadrupedal Robots with Stiff and Compliant Actuation

In the broader context of quadrupedal locomotion, this overview article introduces and compares two platforms that are similar in structure, size, and morphology, yet differ greatly in their concept of actuation. The first, ALoF, is a classically stiff actuated robot that is controlled kinematically, while the second, StarlETH, uses a soft actuation scheme based on Changedhighly compliant series elastic actuators. We show how this conceptual difference influences design and control of the robots, compare the hardware of the two systems, and show exemplary their advantages in different application

Legged Robots

International audienc

Inverse Dynamics Trajectory Optimization for Contact-Implicit Model Predictive Control

Robots must make and break contact to interact with the world and perform useful tasks. However, planning and control through contact remains a formidable challenge. In this work, we achieve real-time contact-implicit model predictive control with a surprisingly simple method: inverse dynamics trajectory optimization. While trajectory optimization with inverse dynamics is not new, we introduce a series of incremental innovations that collectively enable fast model predictive control on a variety of challenging manipulation and locomotion tasks. We implement these innovations in an open-source solver, and present a variety of simulation examples to support the effectiveness of the proposed approach. Additionally, we demonstrate contact-implicit model predictive control on hardware at over 100 Hz for a 20 degree-of-freedom bi-manual manipulation task

On the Biomimetic Design of Agile-Robot Legs

The development of functional legged robots has encountered its limits in human-made actuation technology. This paper describes research on the biomimetic design of legs for agile quadrupeds. A biomimetic leg concept that extracts key principles from horse legs which are responsible for the agile and powerful locomotion of these animals is presented. The proposed biomimetic leg model defines the effective leg length, leg kinematics, limb mass distribution, actuator power, and elastic energy recovery as determinants of agile locomotion, and values for these five key elements are given. The transfer of the extracted principles to technological instantiations is analyzed in detail, considering the availability of current materials, structures and actuators. A real leg prototype has been developed following the biomimetic leg concept proposed. The actuation system is based on the hybrid use of series elasticity and magneto-rheological dampers which provides variable compliance for natural motion. From the experimental evaluation of this prototype, conclusions on the current technological barriers to achieve real functional legged robots to walk dynamically in agile locomotion are presented

Multi-expert learning of adaptive legged locomotion

Achieving versatile robot locomotion requires motor skills which can adapt to previously unseen situations. We propose a Multi-Expert Learning Architecture (MELA) that learns to generate adaptive skills from a group of representative expert skills. During training, MELA is first initialised by a distinct set of pre-trained experts, each in a separate deep neural network (DNN). Then by learning the combination of these DNNs using a Gating Neural Network (GNN), MELA can acquire more specialised experts and transitional skills across various locomotion modes. During runtime, MELA constantly blends multiple DNNs and dynamically synthesises a new DNN to produce adaptive behaviours in response to changing situations. This approach leverages the advantages of trained expert skills and the fast online synthesis of adaptive policies to generate responsive motor skills during the changing tasks. Using a unified MELA framework, we demonstrated successful multi-skill locomotion on a real quadruped robot that performed coherent trotting, steering, and fall recovery autonomously, and showed the merit of multi-expert learning generating behaviours which can adapt to unseen scenarios