Machine Learning-based Framework for Optimally Solving the Analytical Inverse Kinematics for Redundant Manipulators

Solving the analytical inverse kinematics (IK) of redundant manipulators in real time is a difficult problem in robotics since its solution for a given target pose is not unique. Moreover, choosing the optimal IK solution with respect to application-specific demands helps to improve the robustness and to increase the success rate when driving the manipulator from its current configuration towards a desired pose. This is necessary, especially in high-dynamic tasks like catching objects in mid-flights. To compute a suitable target configuration in the joint space for a given target pose in the trajectory planning context, various factors such as travel time or manipulability must be considered. However, these factors increase the complexity of the overall problem which impedes real-time implementation. In this paper, a real-time framework to compute the analytical inverse kinematics of a redundant robot is presented. To this end, the analytical IK of the redundant manipulator is parameterized by so-called redundancy parameters, which are combined with a target pose to yield a unique IK solution. Most existing works in the literature either try to approximate the direct mapping from the desired pose of the manipulator to the solution of the IK or cluster the entire workspace to find IK solutions. In contrast, the proposed framework directly learns these redundancy parameters by using a neural network (NN) that provides the optimal IK solution with respect to the manipulability and the closeness to the current robot configuration. Monte Carlo simulations show the effectiveness of the proposed approach which is accurate and real-time capable ($\approx$ \SI{32}{\micro\second}) on the KUKA LBR iiwa 14 R820

Two-Step Online Trajectory Planning of a Quadcopter in Indoor Environments with Obstacles

This paper presents a two-step algorithm for online trajectory planning in indoor environments with unknown obstacles. In the first step, sampling-based path planning techniques such as the optimal Rapidly exploring Random Tree (RRT*) algorithm and the Line-of-Sight (LOS) algorithm are employed to generate a collision-free path consisting of multiple waypoints. Then, in the second step, constrained quadratic programming is utilized to compute a smooth trajectory that passes through all computed waypoints. The main contribution of this work is the development of a flexible trajectory planning framework that can detect changes in the environment, such as new obstacles, and compute alternative trajectories in real time. The proposed algorithm actively considers all changes in the environment and performs the replanning process only on waypoints that are occupied by new obstacles. This helps to reduce the computation time and realize the proposed approach in real time. The feasibility of the proposed algorithm is evaluated using the Intel Aero Ready-to-Fly (RTF) quadcopter in simulation and in a real-world experiment

An EKF observer to estimate semi-autogenous grinding mill hold-ups

A non-linear observer model of a semi-autogenous grinding mill is developed. The observer model distinguishes between the volumetric hold-up of water, solids, and the grinding media in the mill. Solids refer to all ore small enough to discharge through the end-discharge grate, and grinding media refers to the rocks and steel balls. The rocks are all ore too large to discharge from the mill. The observer model uses the accumulation rate of solids and the mill’s discharge rate as parameters. It is shown that with mill discharge flow-rate, discharge density, and volumetric hold-up measurements, the model states and parameters are linearly observable. Although instrumentation at the mill discharge is not yet included in industrial circuits because of space restrictions, this study motivates the benefits to be gained from including such instrumentation. An extended Kalman filter is applied in simulation to estimate the model states and parameters from data generated by a semi-autogenous mill simulation model from literature. Results indicate that if sufficiently accurate measurements are available, especially at the discharge of the mill, it is possible to reliably estimate grinding media, solids and water hold-ups within the mill. Such an observer can be used as part of an advanced process control strategy.The first author gratefully appreciates support from the University of Pretoria postgraduate study abroad bursary programme. The second author gratefully acknowledges financial support provided by the Austrian Academy of Sciences in the form of an APART-fellowship at the Automation and Control Institute of Vienna University of Technology. The third author gratefully acknowledges financial support provided by the Austrian Federal Ministry of Science, Research and Economy, and the National Foundation for Research, Technology and Development. The fourth author would like to acknowledge the support of the National Research Foundation of South Africa (Grant No.90533).http://www.elsevier.com/locate/jprocont2018-03-31hb2017Electrical, Electronic and Computer Engineerin

Automatic Control of Mechatronic Systems

This contribution deals with different concepts of nonlinear control for mechatronic systems. Since most physical systems are nonlinear in nature, it is quite obvious that an improvement in the performance of the closed loop can often be achieved only by means of control techniques that take the essential nonlinearities into consideration. Nevertheless, it can be observed that industry often hesitates to implement these nonlinear controllers, despite all advantages existing from the theoretical point of view. On the basis of three different applications, a PWM-controlled dc-to-dc converter, namely the Cuk-converter, the problem of hydraulic gap control in steel rolling, and the design of smart structures with piezolelectric sensor and actuator layers, we will demonstrate how one can overcome these problems by exploiting the physical structure of the mathematical models of the considered plants

Reset-control-based current tracking for a solenoid with unknown parameters

International audienceWe propose a hybrid controller for reference tracking for the output current of a solenoid. The solenoid can be modeled as a first-order linear plant with unknown parameters. The proposed hybrid controller comprises 1) a feedforward action, based on an estimate of the unknown plant parameters exploiting a hybrid formulation of the recursive least-squares method, and 2) a feedback action, exploiting a reset control scheme based on a first-order reset element. We prove stability properties for the closed-loop system and we show through simulations that the current output converges to the reference for sufficiently exciting reference signals