Dynamic Bat-Control of a Redundant Ball Playing Robot

This thesis shows a control algorithm for coping with a ball batting task for an entertainment robot. The robot is a three jointed robot with a redundant degree of freedom and its name is Doggy . Doggy because of its dog-like costume. Design, mechanics and electronics were developed by us. DC-motors control the tooth belt driven joints, resulting in elasticities between the motor and link. Redundancy and elasticity have to be taken into account by our developed controller and are demanding control tasks. In this thesis we show the structure of the ball playing robot and how this structure can be described as a model. We distinguish two models: One model that includes a flexible bearing, the other does not. Both models are calibrated using the toolkit Sparse Least Squares on Manifolds (SLOM) - i.e. the parameters for the model are determined. Both calibrated models are compared to measurements of the real system. The model with the flexible bearing is used to implement a state estimator - based on a Kalman filter - on a microcontroller. This ensures real time estimation of the robot states. The estimated states are also compared with the measurements and are assessed. The estimated states represent the measurements well. In the core of this work we develop a Task Level Optimal Controller (TLOC), a model-predictive optimal controller based on the principles of a Linear Quadratic Regulator (LQR). We aim to play a ball back to an opponent precisely. We show how this task of playing a ball at a desired time with a desired velocity at a desired position can be embedded into the LQR principle. We use cost functions for the task description. In simulations, we show the functionality of the control concept, which consists of a linear part (on a microcontroller) and a nonlinear part (PC software). The linear part uses feedback gains which are calculated by the nonlinear part. The concept of the ball batting controller with precalculated feedback gains is evaluated on the robot. This shows successful batting motions. The entertainment aspect has been tested on the Open Campus Day at the University of Bremen and is summarized here shortly. Likewise, a jointly developed audience interaction by recognition of distinctive sounds is summarized herein. In this thesis we answer the question, if it is possible to define a rebound task for our robot within a controller and show the necessary steps for this

High-Order Robotic Joint Sensing with Multiple Accelerometer and Gyroscope Systems

In recent years work into larger humanoid robotic systems and other highly dynamic legged robots has driven a need to increase control system performance and parameter estimation capability. This in turn has seen an increase in the use of higher order joint space derivative terms such as acceleration and jerk being introduced into the control systems and estimators. Although it is evident that the inclusion of these terms can increase the performance of the estimators and control systems, there is a distinct lack of high quality sensors or systems capable of providing this information. Instead it is apparent that those researchers aiming to employ the acceleration and jerk terms are having to resort to other poor quality methods of acquiring the information, which in turn limits the capability of the systems. The works examined suggest that in particular, access to higher quality sources of joint space acceleration measurement or estimation can lead to increases in the performance of control systems and estimators employing these terms. The aim of this work is to investigate the feasibility and capability of a new joint space sensor based on positional encoders and MEMs accelerometers that can estimate angular joint position, velocity and acceleration. The system proposed employs the accelerometer only IMU (AO-IMU) concept to estimate link angular acceleration and velocity in an inertial frame. This concept is then extended to obtain these angular components relative to the previous link. Sensor fusion techniques are then tasked with estimating the velocity states of the AO-IMU and ensuring consistency across the relative states. Two calibration schemes are proposed and demonstrated to correct for the bias, gain and cross axis effects present in the inertial sensors and to correct for the non-ideal placement of the sensors on the body frame. The performance of the system is compared to three online methods common in the literature with significant increases in performance being shown across all states, particularly in the acceleration and velocity states. The base sensor system is then augmented to explore alternate inertial sensor arrangements and structures. In this the effects of adding MEMs gyroscopes to the sensor system are studied and shown to have a small positive effect on the relative velocity state. The addition of multiple relative accelerometers are then studied to examine whether the initial system design choices could be improved upon, with this study showing greater increases in the relative acceleration and velocity states performance. Taking inspiration from the positive results of the multiple relative accelerometer study, an alternate sensor system structure is proposed whereby the robot is instrumented with AO-IMUs and the relative accelerometers omitted. This augmented structure may prove more useful in larger robotic systems. This study initially showed poor results with the low angular velocities experienced by the upper link AO-IMU introducing bias errors. This was corrected for by the inclusion of gyroscopes with the resulting system exhibiting good performance. The findings within this work show that with some modification, the AO-IMU is capable of directly measuring the relative angular acceleration and velocity of a robotic link. When combined with positional sensors this system can be extended to obtain high quality measurements of a joint’s angular position, velocity and acceleration.Thesis (MPhil) -- University of Adelaide, School of Mechanical Engineering, 201

ROBOTIC INTERACTION AND COOPERATION. Industrial and rehabilitative applications

The main goal of the thesis is the development of human-robotic interaction control strategies, which enable close collaboration between human and robot. In this framework we studied two di erent aspects, with applications respectively in industrial and rehabilitation domains. In the rst part safety issues are examined on a scenario in which a robot manipulator and a human perform the same task and in the same workspace. During the task execution the human should be able to get into contact with the robot and in this case an estimation algorithm of both interaction forces and contact point is proposed in order to guarantee safety conditions. At the same time, all the unintended contacts have to be avoided, and a suitable post collision strategy has been studied to move away the robot from the collision area or to reduce the impact e orts. However, the second part of the thesis focus on the cooperation between an orthesis and a patient. Indeed, in order to support a rehabilitation process, gait parameters, such as hip and knee angles or the beginning of a gait phase, have been estimated. For this purpose a sensor system, consisting of accelerometers and gyroscopes, and algorithms, developed in order to avoid the error accumulation due to the gyroscopes drift and the vibrations related to the beginning of the stance phase due to the accelerometers, have been proposed.The main goal of the thesis is the development of human-robotic interaction control strategies, which enable close collaboration between human and robot. In this framework we studied two di erent aspects, with applications respectively in industrial and rehabilitation domains. In the rst part safety issues are examined on a scenario in which a robot manipulator and a human perform the same task and in the same workspace. During the task execution the human should be able to get into contact with the robot and in this case an estimation algorithm of both interaction forces and contact point is proposed in order to guarantee safety conditions. At the same time, all the unintended contacts have to be avoided, and a suitable post collision strategy has been studied to move away the robot from the collision area or to reduce the impact e orts. However, the second part of the thesis focus on the cooperation between an orthesis and a patient. Indeed, in order to support a rehabilitation process, gait parameters, such as hip and knee angles or the beginning of a gait phase, have been estimated. For this purpose a sensor system, consisting of accelerometers and gyroscopes, and algorithms, developed in order to avoid the error accumulation due to the gyroscopes drift and the vibrations related to the beginning of the stance phase due to the accelerometers, have been proposed

Free-Floating Robot Inertial Parameter Identification Towards in Orbit Servicing

This master thesis work focuses on the inertial parameter identification of a space robot by exploiting an object of known inertial properties placed at the end-effector of the robotic arm and angular momentum conservation. On-orbit servicing tasks are becoming every day more crucial due to the exponential growth experienced by the space sector in the recent years. The accurate knowledge of the inertial parameters of a servicing platform is fundamental to accomplish complex missions which require high precision. In this context the method developed in this research work, which was carried out at the DLR's Institute of Robotics and Mechatronics in Oberpfaffenhofen (Germany), will extend the already well-covered topic of space manipulators in-orbit identification with algorithms tailored for platforms that do not have reaction wheels on board (e.g. ISS Astrobees). Besides validating the method with offline simulations, tests were performed for a freefloating robot with a 7 degrees of freedom arm on DLR's OOS-SIM experimental facility, providing an onground validation in a close to representative environment. The identification results show that the full dynamic model of the free-floating robot can be identified with the known load at its end-effector, giving comparable results to those in the literature, ready to be used in a model-based control framework

Virtual Model Building for Multi-Axis Machine Tools Using Field Data

Accurate machine dynamic models are the foundation of many advanced machining technologies such as virtual process planning and machine condition monitoring. Viewing recent designs of modern high-performance machine tools, to enhance the machine versatility and productivity, the machine axis configuration is becoming more complex and diversified, and direct drive motors are more commonly used. Due to the above trends, coupled and nonlinear multibody dynamics in machine tools are gaining more attention. Also, vibration due to limited structural rigidity is an important issue that must be considered simultaneously. Hence, this research aims at building high-fidelity machine dynamic models that are capable of predicting the dynamic responses, such as the tracking error and motor current signals, considering a wide range of dynamic effects such as structural flexibility, inter-axis coupling, and posture-dependency. Building machine dynamic models via conventional bottom-up approaches may require extensive investigation on every single component. Such approaches are time-consuming or sometimes infeasible for the machine end-users. Alternatively, as the recent trend of Industry 4.0, utilizing data via Computer Numerical Controls (CNCs) and/or non-intrusive sensors to build the machine model is rather favorable for industrial implementation. Thus, the methods proposed in this thesis are top-down model building approaches, utilizing available data from CNCs and/or other auxiliary sensors. The achieved contributions and results of this thesis are summarized below. As the first contribution, a new modeling and identification technique targeting a closed-loop control system of coupled rigid multi-axis feed drives has been developed. A multi-axis closed-loop control system, including the controller and the electromechanical plant, is described by a multiple-input multiple-output (MIMO) linear time-invariant (LTI) system, coupled with a generalized disturbance input that represents all the nonlinear dynamics. Then, the parameters of the open-loop and closed-loop dynamic models are respectively identified by a strategy that combines linear Least Squares (LS) and constrained global optimization. This strategy strikes a balance between model accuracy and computational efficiency. This proposed method was validated using an industrial 5-axis laser drilling machine and an experimental feed drive, achieving 2.38% and 5.26% root mean square (RMS) prediction error, respectively. Inter-axis coupling effects, i.e., the motion of one axis causing the dynamic responses of another axis, are correctly predicted. Also, the tracking error induced by motor ripple and nonlinear friction is correctly predicted as well. As the second contribution, the above proposed methodology is extended to also consider structural flexibility, which is a crucial behavior of large-sized industrial 5-axis machine tools. More importantly, structural vibration is nonlinear and posture-dependent due to the nature of a multibody system. In this thesis, prominent cases of flexibility-induced vibrations in a linear feed drive are studied and modeled by lumped mass-spring-damper system. Then, a flexible linear drive coupled with a rotary drive is systematically analyzed. It is found that the case with internal structural vibration between the linear and rotary drives requires an additional motion sensor for the proposed model identification method. This particular case is studied with an experimental setup. The thesis presents a method to reconstruct such missing internal structural vibration using the data from the embedded encoders as well as a low-cost micro-electromechanical system (MEMS) inertial measurement unit (IMU) mounted on the machine table. It is achieved by first synchronizing the data, aligning inertial frames, and calibrating mounting misalignments. Finally, the unknown internal vibration is reconstructed by comparing the rigid and flexible machine kinematic models. Due to the measurement limitation of MEMS IMUs and geometric assembly error, the reconstructed angle is unfortunately inaccurate. Nevertheless, the vibratory angular velocity and acceleration are consistently reconstructed, which is sufficient for the identification with reasonable model simplification. Finally, the reconstructed internal vibration along with the gathered servo data are used to identify the proposed machine dynamic model. Due to the separation of linear and nonlinear dynamics, the vibratory dynamics can be simply considered by adding complex pole pairs into the MIMO LTI system. Experimental validation shows that the identified model is able to predict the dynamic responses of the tracking error and motor force/torque to the input command trajectory and external disturbances, with 2% ~ 6% RMS error. Especially, the vibratory inter-axis coupling effect and posture-dependent effect are accurately depicted. Overall, this thesis presents a dynamic model-building approach for multi-axis feed drive assemblies. The proposed model is general and can be configured according to the kinematic configuration. The model-building approach only requires the data from the servo system or auxiliary motion sensors, e.g., an IMU, which is non-intrusive and in favor of industrial implementation. Future research includes further investigation of the IMU measurement, geometric error identification, validation using more complicated feed drive system, and applications to the planning and monitoring of 5-axis machining process

Vibration Control in Cable Robots Using a Multi-Axis Reaction System

The primary motivation of this thesis is to develop a control strategy for eliminating persistent vibrations in all six spatial directions of the end effector of a planar cable-driven parallel robotic manipulator. By analysing the controllability of a cable-driven robot dynamic model, the uncontrollable modes of the robot are identified. For such uncontrollable modes, a new multi-axis reaction system (MARS) is developed. The new MARS that is attached to the end effector is made of two identical pendulums driven by two servo motors. A decoupled PD controller strategy is developed for regulating controllable modes and a hierarchical sliding mode controller is developed for controlling the remaining modes of the cable robot using MARS. The performance of both controllers is studied and shown to be effective in simulation. The controllers are then implemented on an experimental test setup of a planar cable-driven manipulator. Both controllers are shown to completely eliminate the end effector vibrations

Theory, Design, and Implementation of Landmark Promotion Cooperative Simultaneous Localization and Mapping

Simultaneous Localization and Mapping (SLAM) is a challenging problem in practice, the use of multiple robots and inexpensive sensors poses even more demands on the designer. Cooperative SLAM poses specific challenges in the areas of computational efficiency, software/network performance, and robustness to errors. New methods in image processing, recursive filtering, and SLAM have been developed to implement practical algorithms for cooperative SLAM on a set of inexpensive robots. The Consolidated Unscented Mixed Recursive Filter (CUMRF) is designed to handle non-linear systems with non-Gaussian noise. This is accomplished using the Unscented Transform combined with Gaussian Mixture Models. The Robust Kalman Filter is an extension of the Kalman Filter algorithm that improves the ability to remove erroneous observations using Principal Component Analysis (PCA) and the X84 outlier rejection rule. Forgetful SLAM is a local SLAM technique that runs in nearly constant time relative to the number of visible landmarks and improves poor performing sensors through sensor fusion and outlier rejection. Forgetful SLAM correlates all measured observations, but stops the state from growing over time. Hierarchical Active Ripple SLAM (HAR-SLAM) is a new SLAM architecture that breaks the traditional state space of SLAM into a chain of smaller state spaces, allowing multiple robots, multiple sensors, and multiple updates to occur in linear time with linear storage with respect to the number of robots, landmarks, and robots poses. This dissertation presents explicit methods for closing-the-loop, joining multiple robots, and active updates. Landmark Promotion SLAM is a hierarchy of new SLAM methods, using the Robust Kalman Filter, Forgetful SLAM, and HAR-SLAM. Practical aspects of SLAM are a focus of this dissertation. LK-SURF is a new image processing technique that combines Lucas-Kanade feature tracking with Speeded-Up Robust Features to perform spatial and temporal tracking. Typical stereo correspondence techniques fail at providing descriptors for features, or fail at temporal tracking. Several calibration and modeling techniques are also covered, including calibrating stereo cameras, aligning stereo cameras to an inertial system, and making neural net system models. These methods are important to improve the quality of the data and images acquired for the SLAM process

Fusion of low-cost and light-weight sensor system for mobile flexible manipulator

There is a need for non-industrial robots such as in homecare and eldercare. Light-weight mobile robots preferred as compared to conventional fixed based robots as the former is safe, portable, convenient and economical to implement. Sensor system for light-weight mobile flexible manipulator is studied in this research. A mobile flexible link manipulator (MFLM) contributes to high amount of vibrations at the tip, giving rise to inaccurate position estimations. In a control system, there inevitably exists a lag between the sensor feedback and the controller. Consequently, it contributed to instable control of the MFLM. Hence, there it is a need to predict the tip trajectory of the MFLM. Fusion of low cost sensors is studied to enhance prediction accuracy at the MFLM’s tip. A digital camera and an accelerometer are used predict tip of the MFLM. The main disadvantage of camera is the delayed feedback due to the slow data rate and long processing time, while accelerometer composes cumulative errors. Wheel encoder and webcam are used for position estimation of the mobile platform. The strengths and limitations of each sensor were compared. To solve the above problem, model based predictive sensor systems have been investigated for used on the mobile flexible link manipulator using the selected sensors. Mathematical models were being developed for modeling the reaction of the mobile platform and flexible manipulator when subjected to a series of input voltages and loads. The model-based Kalman filter fusion prediction algorithm was developed, which gave reasonability good predictions of the vibrations of the tip of flexible manipulator on the mobile platform. To facilitate evaluation of the novel predictive system, a mobile platform was fabricated, where the flexible manipulator and the sensors are mounted onto the platform. Straight path motions were performed for the experimental tests. The results showed that predictive algorithm with modelled input to the Extended Kalman filter have best prediction to the tip vibration of the MFLM

Adaptive Localization and Mapping for Planetary Rovers

Future rovers will be equipped with substantial onboard autonomy as space agencies and industry proceed with missions studies and technology development in preparation for the next planetary exploration missions. Simultaneous Localization and Mapping (SLAM) is a fundamental part of autonomous capabilities and has close connections to robot perception, planning and control. SLAM positively affects rover operations and mission success. The SLAM community has made great progress in the last decade by enabling real world solutions in terrestrial applications and is nowadays addressing important challenges in robust performance, scalability, high-level understanding, resources awareness and domain adaptation. In this thesis, an adaptive SLAM system is proposed in order to improve rover navigation performance and demand. This research presents a novel localization and mapping solution following a bottom-up approach. It starts with an Attitude and Heading Reference System (AHRS), continues with a 3D odometry dead reckoning solution and builds up to a full graph optimization scheme which uses visual odometry and takes into account rover traction performance, bringing scalability to modern SLAM solutions. A design procedure is presented in order to incorporate inertial sensors into the AHRS. The procedure follows three steps: error characterization, model derivation and filter design. A complete kinematics model of the rover locomotion subsystem is developed in order to improve the wheel odometry solution. Consequently, the parametric model predicts delta poses by solving a system of equations with weighed least squares. In addition, an odometry error model is learned using Gaussian processes (GPs) in order to predict non-systematic errors induced by poor traction of the rover with the terrain. The odometry error model complements the parametric solution by adding an estimation of the error. The gained information serves to adapt the localization and mapping solution to the current navigation demands (domain adaptation). The adaptivity strategy is designed to adjust the visual odometry computational load (active perception) and to influence the optimization back-end by including highly informative keyframes in the graph (adaptive information gain). Following this strategy, the solution is adapted to the navigation demands, providing an adaptive SLAM system driven by the navigation performance and conditions of the interaction with the terrain. The proposed methodology is experimentally verified on a representative planetary rover under realistic field test scenarios. This thesis introduces a modern SLAM system which adapts the estimated pose and map to the predicted error. The system maintains accuracy with fewer nodes, taking the best of both wheel and visual methods in a consistent graph-based smoothing approach