An Analysis Review: Optimal Trajectory for 6-DOF-based Intelligent Controller in Biomedical Application

With technological advancements and the development of robots have begun to be utilized in numerous sectors, including industrial, agricultural, and medical. Optimizing the path planning of robot manipulators is a fundamental aspect of robot research with promising future prospects. The precise robot manipulator tracks can enhance the efficacy of a variety of robot duties, such as workshop operations, crop harvesting, and medical procedures, among others. Trajectory planning for robot manipulators is one of the fundamental robot technologies, and manipulator trajectory accuracy can be enhanced by the design of their controllers. However, the majority of controllers devised up to this point were incapable of effectively resolving the nonlinearity and uncertainty issues of high-degree freedom manipulators in order to overcome these issues and enhance the track performance of high-degree freedom manipulators. Developing practical path-planning algorithms to efficiently complete robot functions in autonomous robotics is critical. In addition, designing a collision-free path in conjunction with the physical limitations of the robot is a very challenging challenge due to the complex environment surrounding the dynamics and kinetics of robots with different degrees of freedom (DoF) and/or multiple arms. The advantages and disadvantages of current robot motion planning methods, incompleteness, scalability, safety, stability, smoothness, accuracy, optimization, and efficiency are examined in this paper

Advances in humanoid control and perception

One day there will be humanoid robots among us doing our boring, time-consuming, or dangerous tasks. They might cook a delicious meal for us or do the groceries. For this to become reality, many advances need to be made to the artificial intelligence of humanoid robots. The ever-increasing available computational processing power opens new doors for such advances. In this thesis we develop novel algorithms for humanoid control and vision that harness this power. We apply these methods on an iCub humanoid upper-body with 41 degrees of freedom. For control, we develop Natural Gradient Inverse Kinematics (NGIK), a sampling-based optimiser that applies natural evolution strategies to perform inverse kinematics. The resulting algorithm makes very few assumptions and gives much more freedom in definable constraints than its Jacobian-based counterparts. A special graph-building procedure is introduced to build Task-Relevant Roadmaps (TRM) by iteratively applying NGIK and storing the results. TRMs form searchable graphs of kinematic configurations on which a wide range of task-relevant humanoid movements can be planned. Through coordinating several instances of NGIK, a fast parallelised version of the TRM building algorithm is developed. To contrast the offline TRM algorithms, we also develop Natural Gradient Control which directly uses the optimisation pass in NGIK as an online control signal. For vision, we develop dynamic vision algorithms that form cyclic information flows that affect their own processing. Deep Attention Selective Networks (dasNet) implement feedback in convolutional neural networks through a gating mechanism that is steered by a policy. Through this feedback, dasNet can focus on different features in the image in light of previously gathered information and improve classification, with state-of-the- art results at the time of publication. Then, we develop PyraMiD-LSTM, which processes 3D volumetric data by employing a novel convolutional Long Short-Term Memory network (C-LSTM) to compute pyramidal contexts for every voxel, and combine them to perform segmentation. This resulted in state-of-the-art performance on a segmentation benchmark. The work on control and vision is integrated into an application on the iCub robot. A Fast-Weight PyraMiD-LSTM is developed that dynamically generates weights for a C-LSTM layer given actions of the robot. An explorative policy using NGC generates a stream of data, which the Fast-Weight PyraMiD-LSTM has to predict. The resulting integrated system learns to model the effects of head and hand movements and their effects on future visual input. To our knowledge, this is the first effective visual prediction system on an iCub

Structured Prediction for CRiSP Inverse Kinematics Learning with Misspecified Robot Models

With the recent advances in machine learning, problems that traditionally would require accurate modeling to be solved analytically can now be successfully approached with data-driven strategies. Among these, computing the inverse kinematics of a redundant robot arm poses a significant challenge due to the non-linear structure of the robot, the hard joint constraints and the non-invertible kinematics map. Moreover, most learning algorithms consider a completely data-driven approach, while often useful information on the structure of the robot is available and should be positively exploited. In this work, we present a simple, yet effective, approach for learning the inverse kinematics. We introduce a structured prediction algorithm that combines a data-driven strategy with the model provided by a forward kinematics function -- even when this function is misspecified -- to accurately solve the problem. The proposed approach ensures that predicted joint configurations are well within the robot's constraints. We also provide statistical guarantees on the generalization properties of our estimator as well as an empirical evaluation of its performance on trajectory reconstruction tasks.Comment: Accepted for publication in IEEE Robotics and Automation Letters (2021) and presentation at IEEE International Conference on Robotics and Automation (2021) Updated funding informatio

Trajectory Generation for a Multibody Robotic System: Modern Methods Based on Product of Exponentials

This work presents several trajectory generation algorithms for multibody robotic systems based on the Product of Exponentials (PoE) formulation, also known as screw theory. A PoE formulation is first developed to model the kinematics and dynamics of a multibody robotic manipulator (Sawyer Robot) with 7 revolute joints and an end-effector. In the first method, an Inverse Kinematics (IK) algorithm based on the Newton-Raphson iterative method is applied to generate constrained joint-space trajectories corresponding to straight-line and curvilinear motions of the end effector in Cartesian space with finite jerk. The second approach describes Constant Screw Axis (CSA) trajectories which are generated using Machine Learning (ML) and Artificial Neural Networks (ANNs) techniques. The CSA method smooths the trajectory in the Special Euclidean (SE(3)) space. In the third approach, a multi-objective Swarm Intelligence (SI) trajectory generation algorithm is developed, where the IK problem is tackled using a combined SI-PoE ML technique resulting in a joint trajectory that avoids obstacles in the workspace, and satisfies the finite jerk constraint on end-effector while minimizing the torque profiles. The final method is a different approach to solving the IK problem using the Deep Q-Learning (DQN) Reinforcement Learning (RL) algorithm which can generate different joint space trajectories given the Cartesian end-effector path. For all methods above, the Newton-Euler recursive algorithm is implemented to compute the inverse dynamics, which generates the joint torques profiles. The simulated torque profiles are experimentally validated by feeding the generated joint trajectories to the Sawyer robotic arm through the developed Robot Operating System (ROS) - Python environment in the Software Development Kit (SDK) mode. The developed algorithms can be used to generate various trajectories for robotic arms (e.g. spacecraft servicing missions)

Industrial Robotics

This book covers a wide range of topics relating to advanced industrial robotics, sensors and automation technologies. Although being highly technical and complex in nature, the papers presented in this book represent some of the latest cutting edge technologies and advancements in industrial robotics technology. This book covers topics such as networking, properties of manipulators, forward and inverse robot arm kinematics, motion path-planning, machine vision and many other practical topics too numerous to list here. The authors and editor of this book wish to inspire people, especially young ones, to get involved with robotic and mechatronic engineering technology and to develop new and exciting practical applications, perhaps using the ideas and concepts presented herein

Proceedings of the NASA Conference on Space Telerobotics, volume 1

The theme of the Conference was man-machine collaboration in space. Topics addressed include: redundant manipulators; man-machine systems; telerobot architecture; remote sensing and planning; navigation; neural networks; fundamental AI research; and reasoning under uncertainty

Design, modeling and implementation of a soft robotic neck for humanoid robots

Mención Internacional en el título de doctorSoft humanoid robotics is an emerging field that combines the flexibility and safety of soft robotics with the form and functionality of humanoid robotics. This thesis explores the potential for collaboration between these two fields with a focus on the development of soft joints for the humanoid robot TEO. The aim is to improve the robot’s adaptability and movement, which are essential for an efficient interaction with its environment. The research described in this thesis involves the development of a simple and easily transportable soft robotic neck for the robot, based on a 2 Degree of Freedom (DOF) Cable Driven Parallel Mechanism (CDPM). For its final integration into TEO, the proposed design is later refined, resulting in an efficiently scaled prototype able to face significant payloads. The nonlinear behaviour of the joints, due mainly to the elastic nature of their soft links, makes their modeling a challenging issue, which is addressed in this thesis from two perspectives: first, the direct and inverse kinematic models of the soft joints are analytically studied, based on CDPM mathematical models; second, a data-driven system identification is performed based on machine learning techniques. Both approaches are deeply studied and compared, both in simulation and experimentally. In addition to the soft neck, this thesis also addresses the design and prototyping of a soft arm capable of handling external loads. The proposed design is also tendon-driven and has a morphology with two main bending configurations, which provides more versatility compared to the soft neck. In summary, this work contributes to the growing field of soft humanoid robotics through the development of soft joints and their application to the humanoid robot TEO, showcasing the potential of soft robotics to improve the adaptability, flexibility, and safety of humanoid robots. The development of these soft joints is a significant achievement and the research presented in this thesis paves the way for further exploration and development in this field.La robótica humanoide blanda es un campo emergente que combina la flexibilidad y seguridad de la robótica blanda con la forma y funcionalidad de la robótica humanoide. Esta tesis explora el potencial de colaboración entre estos dos campos centrándose en el desarrollo de una articulación blanda para el cuello del robot humanoide TEO. El objetivo es mejorar la adaptabilidad y el movimiento del robot, esenciales para una interacción eficaz con su entorno. La investigación descrita en esta tesis consiste en el desarrollo de un prototipo sencillo y fácilmente transportable de cuello blando para el robot, basado en un mecanismo paralelo actuado por cable de 2 grados de libertad. Para su integración final en TEO, el diseño propuesto es posteriormente refinado, resultando en un prototipo eficientemente escalado capaz de manejar cargas significativas. El comportamiemto no lineal de estas articulaciones, debido fundamentalmente a la naturaleza elástica de sus eslabones blandos, hacen de su modelado un gran reto, que en esta tesis se aborda desde dos perspectivas diferentes: primero, los modelos cinemáticos directo e inverso de las articulaciones blandas se estudian analíticamente, basándose en modelos matemáticos de mecanismos paralelos actuados por cable; segundo, se aborda el problema de la identificación del sistema mediante técnicas basadas en machine learning. Ambas propuestas se estudian y comparan en profundidad, tanto en simulación como experimentalmente. Además del cuello blando, esta tesis también aborda el diseño de un brazo robótico blando capaz de manejar cargas externas. El diseño propuesto está igualmente basado en accionamiento por tendones y tiene una morfología con dos configuraciones principales de flexión, lo que proporciona una mayor versatilidad en comparación con el cuello robótico blando. En resumen, este trabajo contribuye al creciente campo de la robótica humanoide blanda mediante el desarrollo de articulaciones blandas y su aplicación al robot humanoide TEO, mostrando el potencial de la robótica blanda para mejorar la adaptabilidad, flexibilidad y seguridad de los robots humanoides. El desarrollo de estas articulaciones es una contribución significativa y la investigación presentada en esta tesis allana el camino hacia nuevos desarrollos y retos en este campo.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidenta: Cecilia Elisabet García Cena.- Secretario: Dorin Sabin Copaci.- Vocal: Martin Fodstad Stole

Exploiting Prior Knowledge in Robot Motion Skills Learning

This thesis presents a new robot learning framework, its application to exploit prior knowledge by encoding movement primitives in the form of a novel motion library, and the transfer of such knowledge to other robotic platforms in the form of shared latent spaces. In robot learning, it is often desirable to have robots that learn and acquire new skills rapidly. However, existing methods are specific to a certain task defined by the user, as well as time consuming to train. This includes for instance end-to-end models that can require a substantial amount of time to learn a certain skill. Such methods often start with no prior knowledge or little, and move slowly from erratic movements to the specific required motion. This is very different from how animals and humans learn motion skills. For instance, zebras in the African Savannah can learn to walk in few minutes just after being born. This suggests that some kind of prior knowledge is encoded into them. Leveraging this information may help improve and accelerate the learning and generation of new skills. These observations raise questions such as: how would this prior knowledge be represented? And how much would it help the learning process? Additionally, once learned, these models often do not transfer well to other robotic platforms requiring to teach to each other robot the same skills. This significantly increases the total training time and render the demonstration phase a tedious process. Would it be possible instead to exploit this prior knowledge to accelerate the learning process of new skills by transferring it to other robots? These are some of the questions that we are interested to investigate in this thesis. However, before examining these questions, a practical tool that allows one to easily test ideas in robot learning is needed. This tool would have to be easy-to-use, intuitive, generic, modular, and would need to let the user easily implement different ideas and compare different models/algorithms. Once implemented, we would then be able to focus on our original questions