Deep Neural Network Based Subspace Learning of Robotic Manipulator Workspace Mapping

The manipulator workspace mapping is an important problem in robotics and has attracted significant attention in the community. However, most of the pre-existing algorithms have expensive time complexity due to the reliance on sophisticated kinematic equations. To solve this problem, this paper introduces subspace learning (SL), a variant of subspace embedding, where a set of robot and scope parameters is mapped to the corresponding workspace by a deep neural network (DNN). Trained on a large dataset of around $\mathbf{6\times 10^4}$ samples obtained from a MATLAB$^\circledR$ implementation of a classical method and sampling of designed uniform distributions, the experiments demonstrate that the embedding significantly reduces run-time from $\mathbf{5.23 \times 10^3}$ s of traditional discretization method to $\mathbf{0.224}$ s, with high accuracies (average F-measure is $\mathbf{0.9665}$ with batch gradient descent and resilient backpropagation).Comment: 12 pages, 12 figures, accepted for presentation at ICCAIRO 201

Design, analysis and kinematic control of highly redundant serial robotic arms

The use of robotic manipulators in industry has grown in the last decades to improve and speed up industrial processes. Industrial manipulators started to be investigated for machining tasks since they can cover larger workspaces, increasing the range of achievable operations and improving flexibility. The company Nimbl’Bot developed a new mechanism, or module, to build stiffer flexible serial modular robots for machining applications. This manipulator is a kinematic redundant robot with 21 degrees of freedom. This thesis thoroughly analysis the Nimbl’Bot robot features and is divided into three main topics. The first topic regards using a task priority kinematic redundancy resolution algorithm for the Nimbl’Bot robot tracking trajectory while optimizing its kinetostatic performances. The second topic is the kinematic redundant robot design optimization with respect to a desired application and its kinetostatic performance. For the third topic, a new workspace determination algorithm is proposed for kinematic redundant manipulators. Several simulation tests are proposed and tested on some Nimbl’Bot robot designs for each subjects

Geometric soft robotics: a finite element approach

Enabling remote semi-autonomous operations in hazardous environments is a challenging technological problem, given the difficulty to access in confined and constrained spaces using classical robotic systems. Inspired by biological trunks and tentacles, soft continuum robots constitute a possible solution to this problem, for their ability to traverse confined spaces, manipulate objects in complex environments, and conform their shape to nonlinear curvilinear paths. The need of reaching difficult-to-access industrial sites for maintenance and inspection procedures or anatomical sites for less invasive robotic surgery mainly motivates the current research. Despite the recent advances in the design and fabrication of soft robots, the community still suffers for the lack of a consolidate modeling framework for simulating their mechanical behavior. Such a modeling framework is the necessary condition for developing new physical design and control strategies, as well as path planning algorithms. Indeed, despite their appreciable features, soft robots usually generate undesired vibrations during normal procedures. This is one of the main reasons which still limits their potentially wide use in real scenario. Realistic modeling frameworks might leverage the development of model-based predictive controllers to compensate for the undesired vibrations, as well as design concepts and optimized trajectories to avoid the excitation of the vibration modes of the mechanical structure. The main objective of the thesis is to develop a unified mathematical framework for simulating the mechanical behavior of soft continuum robotic manipulators, which can also accommodate the dynamic simulation of classical rigid robots. The computer implementation of this theoretical framework leads to the development of SimSOFT, a physics engine for soft robots. The formulation has been validated through literature benchmark and some applications are presented. One of the major strengths of the framework is that it can accommodate the realistic simulation of kinematic trees or loops constituted either by rigid or soft arms connected by rigid or flexible joints.The simulation of hybrid mechanisms, composed by classical rigid kinematic chains and soft continuum manipulators, which can be used to have larger dexterity in smaller workspaces, as they are easily to miniaturize, is thus possible. To the best of the author's knowledge, the mathematical models developed in the thesis constitute the first attempt in the robotics community towards a unified framework for the dynamics of soft continuum multibody systems

A memetic approach to the inverse kinematics problem for robotic applications

The inverse kinematics problem of an articulated robot system refers to computing the joint configuration that places the end-effector at a given position and orientation. To overcome the numerical instability of the Jacobian-based algorithms around singular joint configurations, the inverse kinematics is formulated as a constrained minimization problem in the configuration space of the robot. In previous works this problem has been solved for redundant and non-redundant robots using evolutionary-based algorithms. However, despite the flexibility and accuracy of the direct search approach of evolutionary algorithms, these algorithms are not suitable for most robot applications given their low convergence speed rate and the high computational cost of their population-based approach. In this thesis, we propose a memetic variant of the Differential Evolution (DE) algorithm to increase its convergence speed on the kinematics inversion problem of articulated robot systems. With the aim to yield an efficient trade-off between exploration and exploitation of the search space, the memetic approach combines the global search scheme of the standard DE with an independent local search mechanisms, called discarding. The proposed scheme is tested on a simulation environment for different benchmark serial robot manipulators and anthropomorphic robot hands. Results show that the memetic differential evolution is able to find solutions with high accuracy in less generations than the original DE. -----------------------------------------------------------La cinemática inversa de los robots manipuladores se refiere al problema de calcular las coordenadas articulares del robot a partir de coordenadas conocidas de posición y orientación de su extremo libre. Para evitar la inestabilidad numérica de los métodos basados en la inversa de la matriz Jacobiana en la vecindad de configuraciones singulares, el problema de cinemática inversa es definido en el espacio de configuraciones del robot manipulador como un problema de optimización con restricciones. Este problema de optimización ha sido previamente resuelto con métodos evolutivos para robots manipuladores, redundantes y no redundantes, obteniéndose buenos resultados; sin embargo, estos métodos exhiben una baja velocidad de convergencia no adecuada para aplicaciones robóticas. Para incrementar la velocidad de convergencia de estos algoritmos, se propone un método memético de evolución differencial. El enfoque de búsqueda directa propuesto combina el esquema estándar de evolución diferencial con un mecanismo independiente de refinamiento local, llamado discarding o descarte. El desempeño del método propuesto es evaluado en un entorno de simulación para diferentes robot manipuladores y manos robóticas antropomórficas. Los resultados obtenidos muestran una importante mejora en precisión y velocidad de convergencia en comparación del método DE original.Programa en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Pedro M. Urbano de Almeida Lima; Vocal: Cecilia Elisabet García Cena; Secretario: Mohamed Abderrahim Fichouch

Geometric Algebra for Optimal Control with Applications in Manipulation Tasks

Many problems in robotics are fundamentally problems of geometry, which lead to an increased research effort in geometric methods for robotics in recent years. The results were algorithms using the various frameworks of screw theory, Lie algebra and dual quaternions. A unification and generalization of these popular formalisms can be found in geometric algebra. The aim of this paper is to showcase the capabilities of geometric algebra when applied to robot manipulation tasks. In particular the modelling of cost functions for optimal control can be done uniformly across different geometric primitives leading to a low symbolic complexity of the resulting expressions and a geometric intuitiveness. We demonstrate the usefulness, simplicity and computational efficiency of geometric algebra in several experiments using a Franka Emika robot. The presented algorithms were implemented in c++20 and resulted in the publicly available library \textit{gafro}. The benchmark shows faster computation of the kinematics than state-of-the-art robotics libraries.Comment: 16 pages, 13 figures

Motion Control of the Hybrid Wheeled-Legged Quadruped Robot Centauro

Emerging applications will demand robots to deal with a complex environment, which lacks the structure and predictability of the industrial workspace. Complex scenarios will require robot complexity to increase as well, as compared to classical topologies such as fixed-base manipulators, wheeled mobile platforms, tracked vehicles, and their combinations. Legged robots, such as humanoids and quadrupeds, promise to provide platforms which are flexible enough to handle real world scenarios; however, the improved flexibility comes at the cost of way higher control complexity. As a trade-off, hybrid wheeled-legged robots have been proposed, resulting in the mitigation of control complexity whenever the ground surface is suitable for driving. Following this idea, a new hybrid robot called Centauro has been developed inside the Humanoid and Human Centered Mechatronics lab at Istituto Italiano di Tecnologia (IIT). Centauro is a wheeled-legged quadruped with a humanoid bi-manual upper-body. Differently from other platform of similar concept, Centauro employs customized actuation units, which provide high torque outputs, moderately fast motions, and the possibility to control the exerted torque. Moreover, with more than forty motors moving its limbs, Centauro is a very redundant platform, with the potential to execute many different tasks at the same time. This thesis deals with the design and development of a software architecture, and a control system, tailored to such a robot; both wheeled and legged locomotion strategies have been studied, as well as prioritized, whole-body and interaction controllers exploiting the robot torque control capabilities, and capable to handle the system redundancy. A novel software architecture, made of (i) a real-time robotic middleware, and (ii) a framework for online, prioritized Cartesian controller, forms the basis of the entire work

Modelling, Control and Optimization of Modular Reconfigurable Robots

Modular reconfigurable robots are robotic systems offering new opportunities to rapidly create fit-to-task flexible automation lines. The recent trends of increasingly varying market needs in low-volume high-mix manufacturing demands for highly adaptable robotic systems like this. In this context, methods for quickly and automatically generating a modular robot model and controller should be developed. Moreover, modularity and reconfigurabilty open up new opportunities for on-demand robot morphology optimization for varying tasks. Therefore a method to optimize the robot design for a certain criterion should be provided in order to exploit the full potential of reconfigurable robots. In this thesis, a complete hard- and software architecture for a modular reconfigurable EtherCAT-based robot is presented. This novel approach allows to automatically reconstruct the topology of different robot structures, composed of a set of body modules, each of which represents an EtherCAT slave. This approach enables to obtain in an automatic way the kinematic and dynamic model of the robot and store it in URDF format as soon as the physical robot is assembled or reconfigured. The method also automatically reshapes a generic optimization-based controller to be instantly used after reconfiguration. Finally, a study and analysis on how to find the best suited reconfigurable robot morphology for a given task are presented, starting from a fixed set of joint and link modules. In particular, is shown how exploiting multi-arm robotic systems and modifying the relative and absolute positions of their bases, can expand the solution space for a given task. Results obtained in simulations for different tasks, are verified with real-world experiments using a in-house developed reconfigurable robot prototype

Precision Grasp Planning for Integrated Arm-Hand Systems

The demographic shift has caused labor shortages across the world, and it seems inevitable to rely on robots more than ever to fill the widening gap in the workforce. The robotic replacement of human workers necessitates the ability of autonomous grasping as the most natural but rather a vital part of almost all activities. Among different types of grasping, fingertip grasping attracts much attention because of its superior performance for dexterous manipulation. This thesis contributes to autonomous fingertip grasping in four areas including hand-eye calibration, grasp quality evaluation, inverse kinematics (IK) solution of robotic arm-hand systems, and simultaneous achievement of grasp planning and IK solution. To initiate autonomous grasping, object perception is the first needed step. Stereo cameras are well-embraced for obtaining an object\u27s 3D model. However, the data acquired through a camera is expressed in the camera frame while robots only accept the commands encoded in the robot frame. This dilemma necessitates the calibration between the robot (hand) and the camera (eye) with the main goal is of estimating the camera\u27s relative pose to the robot end-effector so that the camera-acquired measurements can be converted into the robot frame. We first study the hand-eye calibration problem and achieve accurate results through a point set matching formulation. With the object\u27s 3D measurements expressed in the robot frame, the next step is finding an appropriate grasp configuration (contact points + contact normals) on the object\u27s surface. To this end, we present an efficient grasp quality evaluation method to calculate a popular wrench-based quality metric which measures the minimum distance between the wrench space origin ($\vec{0}_{6\times 1}$) to the boundary of grasp wrench space (GWS). The proposed method mathematically expresses the exact boundary of GWS, which allows to evaluate the quality of the grasp with the speed that is desirable in most robotic applications. Having obtained a suitable grasp configuration, an accurate IK solution of the arm-hand system is required to perform the planned grasp. Conventionally, the IK of the robotic hand and arm are solved sequentially, which often affects the efficiency and accuracy of the IK solutions. To overcome this problem, we kinematically integrate the robotic arm and hand and propose a human-inspired Thumb-First strategy to narrow down the search space of the IK solution. Based on the Thumb-First strategy, we propose two IK solutions. Our first solution follows a hierarchical IK strategy, while our second solution formulates the arm-hand system as a hybrid parallel-serial system to achieve a higher success rate. Using these results, we propose an approach to integrate the process of grasp planning and IK solution by following a special-designed coarse-to-fine strategy to improve the overall efficiency of our approach

Contact aware robust semi-autonomous teleoperation of mobile manipulators

In the context of human-robot collaboration, cooperation and teaming, the use of mobile manipulators is widespread on applications involving unpredictable or hazardous environments for humans operators, like space operations, waste management and search and rescue on disaster scenarios. Applications where the manipulator's motion is controlled remotely by specialized operators. Teleoperation of manipulators is not a straightforward task, and in many practical cases represent a common source of failures. Common issues during the remote control of manipulators are: increasing control complexity with respect the mechanical degrees of freedom; inadequate or incomplete feedback to the user (i.e. limited visualization or knowledge of the environment); predefined motion directives may be incompatible with constraints or obstacles imposed by the environment. In the latter case, part of the manipulator may get trapped or blocked by some obstacle in the environment, failure that cannot be easily detected, isolated nor counteracted remotely. While control complexity can be reduced by the introduction of motion directives or by abstraction of the robot motion, the real-time constraint of the teleoperation task requires the transfer of the least possible amount of data over the system's network, thus limiting the number of physical sensors that can be used to model the environment. Therefore, it is of fundamental to define alternative perceptive strategies to accurately characterize different interaction with the environment without relying on specific sensory technologies. In this work, we present a novel approach for safe teleoperation, that takes advantage of model based proprioceptive measurement of the robot dynamics to robustly identify unexpected collisions or contact events with the environment. Each identified collision is translated on-the-fly into a set of local motion constraints, allowing the exploitation of the system redundancies for the computation of intelligent control laws for automatic reaction, without requiring human intervention and minimizing the disturbance of the task execution (or, equivalently, the operator efforts). More precisely, the described system consist in two different building blocks. The first, for detecting unexpected interactions with the environment (perceptive block). The second, for intelligent and autonomous reaction after the stimulus (control block). The perceptive block is responsible of the contact event identification. In short, the approach is based on the claim that a sensorless collision detection method for robot manipulators can be extended to the field of mobile manipulators, by embedding it within a statistical learning framework. The control deals with the intelligent and autonomous reaction after the contact or impact with the environment occurs, and consist on an motion abstraction controller with a prioritized set of constrains, where the highest priority correspond to the robot reconfiguration after a collision is detected; when all related dynamical effects have been compensated, the controller switch again to the basic control mode