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

Inverse Kinematics Based on Fuzzy Logic and Neural Networks for the WAM-Titan II Teleoperation System

The inverse kinematic problem is crucial for robotics. In this paper, a solution algorithm is presented using artificial intelligence to improve the pseudo-inverse Jacobian calculation for the 7-DOF Whole Arm Manipulator (WAM) and 6-DOF Titan II teleoperation system. An investigation of the inverse kinematics based on fuzzy logic and artificial neural networks for the teleoperation system was undertaken. Various methods such as Adaptive Neural-Fuzzy Inference System (ANFIS), Genetic Algorithms (GA), Multilayer Perceptrons (MLP) Feedforward Networks, Radial Basis Function Networks (RBF) and Generalized Regression Neural Networks (GRNN) were tested and simulated using MATLAB. Each method for identification of the pseudo-inverse problem was tested, and the best method was selected from the simulation results and the error analysis. From the results, the Multilayer Perceptrons with Levenberg-Marquardt (MLP-LM) method had the smallest error and the fastest computation among the other methods. For the WAM-Titan II teleoperation system, the new inverse kinematics calculations for the Titan II were simulated and analyzed using MATLAB. Finally, extensive C code for the alternative algorithm was developed, and the inverse kinematics based on the artificial neural network with LM method is implemented in the real system. The maximum error of Cartesian position was 1.3 inches, and from several trajectories, 75 % of time implementation was achieved compared to the conventional method. Because fast performance of a real time system in the teleoperation is vital, these results show that the new inverse kinematics method based on the MLP-LM is very successful with the acceptable error

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

Constrained-Differential-Kinematics-Decomposition-Based NMPC for Online Manipulator Control with Low Computational Costs

Flexibility combined with the ability to consider external constraints comprises the main advantages of nonlinear model predictive control (NMPC). Applied as a motion controller, NMPC enables applications in varying and disturbed environments, but requires time-consuming computations. Hence, given the full nonlinear multi-DOF robot model, a delay-free execution providing short control horizons at appropriate prediction horizons for accurate motions is not applicable in common use. This contribution introduces an approach that analyzes and decomposes the differential kinematics similar to the inverse kinematics method to assign Cartesian boundary conditions to specific systems of equations during the model building, reducing the online computational costs. The resulting fully constrained NMPC realizes the translational obstacle avoidance during trajectory tracking using a reduced model considering both joint and Cartesian constraints coupled with a Jacobian transposed controller performing the end-effector’s orientation correction. Apart from a safe distance from the obstacles, the presented approach does not lead to any limitations of the reachable workspace, and all degrees of freedom (DOFs) of the robot are used. The simulative evaluation in Gazebo using the Stäubli TX2-90 commanded of ROS on a standard computer emphasizes the significantly lower online computational costs, accuracy analysis, and extended adaptability in obstacle avoidance, providing additional flexibility. An interpretation of the new concept is discussed for further use and extensions

Kinematic calibration of a 3-DOF spindle head using a double ball bar

This paper presents a simple and effective approach for kinematic calibration of a 3-DOF spindle head developed for high-speed machining. This approach is implemented in three steps, (i) error modeling that allows the geometric errors affecting the compensatable and uncompensatable pose accuracy to be classified; (ii) identification of the geometric errors using a set of distance measurements acquired by a double ball bar (DBB) with a single installation; (iii) design of a linearized error compensator for real-time error implementation. Experimental results on a prototype machine show that the compensatable pose accuracy can significantly be improved by the proposed approach

A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics Identification

In this paper, we propose a black-box model based on Gaussian process regression for the identification of the inverse dynamics of robotic manipulators. The proposed model relies on a novel multidimensional kernel, called \textit{Lagrangian Inspired Polynomial} (\kernelInitials{}) kernel. The \kernelInitials{} kernel is based on two main ideas. First, instead of directly modeling the inverse dynamics components, we model as GPs the kinetic and potential energy of the system. The GP prior on the inverse dynamics components is derived from those on the energies by applying the properties of GPs under linear operators. Second, as regards the energy prior definition, we prove a polynomial structure of the kinetic and potential energy, and we derive a polynomial kernel that encodes this property. As a consequence, the proposed model allows also to estimate the kinetic and potential energy without requiring any label on these quantities. Results on simulation and on two real robotic manipulators, namely a 7 DOF Franka Emika Panda and a 6 DOF MELFA RV4FL, show that the proposed model outperforms state-of-the-art black-box estimators based both on Gaussian Processes and Neural Networks in terms of accuracy, generality and data efficiency. The experiments on the MELFA robot also demonstrate that our approach achieves performance comparable to fine-tuned model-based estimators, despite requiring less prior information

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