Microrobots for wafer scale microfactory: design fabrication integration and control.

Future assembly technologies will involve higher automation levels, in order to satisfy increased micro scale or nano scale precision requirements. Traditionally, assembly using a top-down robotic approach has been well-studied and applied to micro-electronics and MEMS industries, but less so in nanotechnology. With the bloom of nanotechnology ever since the 1990s, newly designed products with new materials, coatings and nanoparticles are gradually entering everyone’s life, while the industry has grown into a billion-dollar volume worldwide. Traditionally, nanotechnology products are assembled using bottom-up methods, such as self-assembly, rather than with top-down robotic assembly. This is due to considerations of volume handling of large quantities of components, and the high cost associated to top-down manipulation with the required precision. However, the bottom-up manufacturing methods have certain limitations, such as components need to have pre-define shapes and surface coatings, and the number of assembly components is limited to very few. For example, in the case of self-assembly of nano-cubes with origami design, post-assembly manipulation of cubes in large quantities and cost-efficiency is still challenging. In this thesis, we envision a new paradigm for nano scale assembly, realized with the help of a wafer-scale microfactory containing large numbers of MEMS microrobots. These robots will work together to enhance the throughput of the factory, while their cost will be reduced when compared to conventional nano positioners. To fulfill the microfactory vision, numerous challenges related to design, power, control and nanoscale task completion by these microrobots must be overcome. In this work, we study three types of microrobots for the microfactory: a world’s first laser-driven micrometer-size locomotor called ChevBot，a stationary millimeter-size robotic arm, called Solid Articulated Four Axes Microrobot (sAFAM), and a light-powered centimeter-size crawler microrobot called SolarPede. The ChevBot can perform autonomous navigation and positioning on a dry surface with the guidance of a laser beam. The sAFAM has been designed to perform nano positioning in four degrees of freedom, and nanoscale tasks such as indentation, and manipulation. And the SolarPede serves as a mobile workspace or transporter in the microfactory environment

Scaled Autonomy for Networked Humanoids

Humanoid robots have been developed with the intention of aiding in environments designed for humans. As such, the control of humanoid morphology and effectiveness of human robot interaction form the two principal research issues for deploying these robots in the real world. In this thesis work, the issue of humanoid control is coupled with human robot interaction under the framework of scaled autonomy, where the human and robot exchange levels of control depending on the environment and task at hand. This scaled autonomy is approached with control algorithms for reactive stabilization of human commands and planned trajectories that encode semantically meaningful motion preferences in a sequential convex optimization framework. The control and planning algorithms have been extensively tested in the field for robustness and system verification. The RoboCup competition provides a benchmark competition for autonomous agents that are trained with a human supervisor. The kid-sized and adult-sized humanoid robots coordinate over a noisy network in a known environment with adversarial opponents, and the software and routines in this work allowed for five consecutive championships. Furthermore, the motion planning and user interfaces developed in the work have been tested in the noisy network of the DARPA Robotics Challenge (DRC) Trials and Finals in an unknown environment. Overall, the ability to extend simplified locomotion models to aid in semi-autonomous manipulation allows untrained humans to operate complex, high dimensional robots. This represents another step in the path to deploying humanoids in the real world, based on the low dimensional motion abstractions and proven performance in real world tasks like RoboCup and the DRC

Modeling, Control and Estimation of Reconfigurable Cable Driven Parallel Robots

The motivation for this thesis was to develop a cable-driven parallel robot (CDPR) as part of a two-part robotic device for concrete 3D printing. This research addresses specific research questions in this domain, chiefly, to present advantages offered by the addition of kinematic redundancies to CDPRs. Due to the natural actuation redundancy present in a fully constrained CDPR, the addition of internal mobility offers complex challenges in modeling and control that are not often encountered in literature. This work presents a systematic analysis of modeling such kinematic redundancies through the application of reciprocal screw theory (RST) and Lie algebra while further introducing specific challenges and drawbacks presented by cable driven actuators. It further re-contextualizes well-known performance indices such as manipulability, wrench closure quality, and the available wrench set for application with reconfigurable CDPRs. The existence of both internal redundancy and static redundancy in the joint space offers a large subspace of valid solutions that can be condensed through the selection of appropriate objective priorities, constraints or cost functions. Traditional approaches to such redundancy resolution necessitate computationally expensive numerical optimization. The control of both kinematic and actuation redundancies requires cascaded control frameworks that cannot easily be applied towards real-time control. The selected cost functions for numerical optimization of rCDPRs can be globally (and sometimes locally) non-convex. In this work we present two applied examples of redundancy resolution control that are unique to rCDPRs. In the first example, we maximize the directional wrench ability at the end-effector while minimizing the joint torque requirement by utilizing the fitness of the available wrench set as a constraint over wrench feasibility. The second example focuses on directional stiffness maximization at the end-effector through a variable stiffness module (VSM) that partially decouples the tension and stiffness. The VSM introduces an additional degrees of freedom to the system in order to manipulate both reconfigurability and cable stiffness independently. The controllers in the above examples were designed with kinematic models, but most CDPRs are highly dynamic systems which can require challenging feedback control frameworks. An approach to real-time dynamic control was implemented in this thesis by incorporating a learning-based frameworks through deep reinforcement learning. Three approaches to rCDPR training were attempted utilizing model-free TD3 networks. Robustness and safety are critical features for robot development. One of the main causes of robot failure in CDPRs is due to cable breakage. This not only causes dangerous dynamic oscillations in the workspace, but also leads to total robot failure if the controllability (due to lack of cables) is lost. Fortunately, rCDPRs can be utilized towards failure tolerant control for task recovery. The kinematically redundant joints can be utilized to help recover the lost degrees of freedom due to cable failure. This work applies a Multi-Model Adaptive Estimation (MMAE) framework to enable online and automatic objective reprioritization and actuator retasking. The likelihood of cable failure(s) from the estimator informs the mixing of the control inputs from a bank of feedforward controllers. In traditional rigid body robots, safety procedures generally involve a standard emergency stop procedure such as actuator locking. Due to the flexibility of cable links, the dynamic oscillations of the end-effector due to cable failure must be actively dampened. This work incorporates a Linear Quadratic Regulator (LQR) based feedback stabilizer into the failure tolerant control framework that works to stabilize the non-linear system and dampen out these oscillations. This research contributes to a growing, but hitherto niche body of work in reconfigurable cable driven parallel manipulators. Some outcomes of the multiple engineering design, control and estimation challenges addressed in this research warrant further exploration and study that are beyond the scope of this thesis. This thesis concludes with a thorough discussion of the advantages and limitations of the presented work and avenues for further research that may be of interest to continuing scholars in the community

Shared Control Policies and Task Learning for Hydraulic Earth-Moving Machinery

This thesis develops a shared control design framework for improving operator efficiency and performance on hydraulic excavation tasks. The framework is based on blended shared control (BSC), a technique whereby the operator’s command input is continually augmented by an assistive controller. Designing a BSC control scheme is subdivided here into four key components. Task learning utilizes nonparametric inverse reinforcement learning to identify the underlying goal structure of a task as a sequence of subgoals directly from the demonstration data of an experienced operator. These subgoals may be distinct points in the actuator space or distributions overthe space, from which the operator draws a subgoal location during the task. The remaining three steps are executed on-line during each update of the BSC controller. In real-time, the subgoal prediction step involves utilizing the subgoal decomposition from the learning process in order to predict the current subgoal of the operator. Novel deterministic and probabilistic prediction methods are developed and evaluated for their ease of implementation and performance against manually labeled trial data. The control generation component involves computing polynomial trajectories to the predicted subgoal location or mean of the subgoal distribution, and computing a control input which tracks those trajectories. Finally, the blending law synthesizes both inputs through a weighted averaging of the human and control input, using a blending parameter which can be static or dynamic. In the latter case, mapping probabilistic quantities such as the maximum a posteriori probability or statistical entropy to the value of the dynamic blending parameter may yield a more intelligent control assistance, scaling the intervention according to the confidence of the prediction. A reduced-scale (1/12) fully hydraulic excavator model was instrumented for BSC experimentation, equipped with absolute position feedback of each hydraulic actuator. Experiments were conducted using a standard operator control interface and a common earthmoving task: loading a truck from a pile. Under BSC, operators experienced an 18% improvement in mean digging efficiency, defined as mass of material moved per cycle time. Effects of BSC vary with regard to pure cycle time, although most operators experienced a reduced mean cycle time

Hybrid optical and magnetic manipulation of microrobots

Microrobotic systems have the potential to provide precise manipulation on cellular level for diagnostics, drug delivery and surgical interventions. These systems vary from tethered to untethered microrobots with sizes below a micrometer to a few microns. However, their main disadvantage is that they do not have the same capabilities in terms of degrees-of-freedom, sensing and control as macroscale robotic systems. In particular, their lack of on-board sensing for pose or force feedback, their control methods and interface for automated or manual user control are limited as well as their geometry has few degrees-of-freedom making three-dimensional manipulation more challenging. This PhD project is on the development of a micromanipulation framework that can be used for single cell analysis using the Optical Tweezers as well as a combination of optical trapping and magnetic actuation for recon gurable microassembly. The focus is on untethered microrobots with sizes up to a few tens of microns that can be used in enclosed environments for ex vivo and in vitro medical applications. The work presented investigates the following aspects of microrobots for single cell analysis: i) The microfabrication procedure and design considerations that are taken into account in order to fabricate components for three-dimensional micromanipulation and microassembly, ii) vision-based methods to provide 6-degree-offreedom position and orientation feedback which is essential for closed-loop control, iii) manual and shared control manipulation methodologies that take into account the user input for multiple microrobot or three-dimensional microstructure manipulation and iv) a methodology for recon gurable microassembly combining the Optical Tweezers with magnetic actuation into a hybrid method of actuation for microassembly.Open Acces

Parallel Manipulators

In recent years, parallel kinematics mechanisms have attracted a lot of attention from the academic and industrial communities due to potential applications not only as robot manipulators but also as machine tools. Generally, the criteria used to compare the performance of traditional serial robots and parallel robots are the workspace, the ratio between the payload and the robot mass, accuracy, and dynamic behaviour. In addition to the reduced coupling effect between joints, parallel robots bring the benefits of much higher payload-robot mass ratios, superior accuracy and greater stiffness; qualities which lead to better dynamic performance. The main drawback with parallel robots is the relatively small workspace. A great deal of research on parallel robots has been carried out worldwide, and a large number of parallel mechanism systems have been built for various applications, such as remote handling, machine tools, medical robots, simulators, micro-robots, and humanoid robots. This book opens a window to exceptional research and development work on parallel mechanisms contributed by authors from around the world. Through this window the reader can get a good view of current parallel robot research and applications

Generative and predictive models for robust manipulation

Probabilistic modelling of manipulation skills, perception and uncertainty pose many challenges at different stages of a typical robot manipulation pipeline. This thesis is about devising algorithms and strategies for improving robustness in object manipulation skills acquired from demonstration and derived from learnt physical models in non-prehensile tasks such as pushing. Manipulation skills can be made robust in different ways: first by improving time performance for grasp synthesis, second by employing active perceptual strategies that exploit generated grasp action hypothesis to more efficiently gather task-relevant information for grasp generation, and finally via exploiting predictive uncertainty in learnt physical models. Hence, robust manipulation skills emerge from the interplay of a triad of capabilities: generative modelling for action synthesis, active perception, and finally learning and exploiting uncertainty in physical interactions. This thesis addresses these problems by • Showing how parametric models for approximating multimodal distributions can be used as a computationally faster method for generative grasp synthesis. • Exploiting generative methods for dexterous grasp synthesis and investigating how active vision strategies can be applied to improve grasp execution safety, success rate, and utilise fewer camera views of an object for grasp generation. • Outlining methods to model and exploit predictive uncertainty from learnt forward models to achieve robust, uncertainty-averse non-prehensile manipulation, such as push manipulation. In particular, the thesis: (i) presents a framework for generative grasp synthesis with applications for real-time grasp synthesis suitable for multi-fingered robot hands; (ii) describes a sensorisation method for under-actuated hands, such as the Pisa/IIT SoftHand, which allows us to deploy the aforementioned grasp synthesis framework to this type of robotic hand; (iii) provides an active vision approach for view selection that makes use of generative grasp synthesis methods to perform perceptual predictions in order to leverage grasp performance, taking into account grasp execution safety and contact information; and (iv) finally, going beyond prehensile skills, provides an approach to model and exploit predictive uncertainty from learnt physics applied to push manipulation. Experimental results are presented in simulation and on real robot platforms to validate the proposed methods

Actuation, Sensing And Control For Micro Bio Robots

The continuing trend in miniaturization of technology, advancements in micro and nanofabrication and improvements in high-resolution imaging has enabled micro- and meso-scale robots that have many applications. They can be used for micro-assembly, directed drug delivery, microsurgery and high-resolution measurement. In order to create microrobots, microscopic sensors, actuators and controllers are needed. Unique challenges arise when building microscale robots. For inspiration, we look toward highly capable biological organisms, which excel at these length scales. In this dissertation we develop technologies that combine biological components and synthetic components to create actuation, sensing and assembly onboard microrobots. For actuation, we study the dynamics of synthetic micro structures that have been integrated with single-cell biological organisms to provide un-tethered onboard propulsion to the microrobot. For sensing, we integrate synthetically engineered sensor cells to enable a system capable of detecting a change in the local environment, then storing and reporting the information. Furthermore, we develop a bottom-up fabrication method using a macroscopic magnetic robot to direct the assembly of inorganic engineered micro structures. We showcase the capability of this assembly method by demonstrating highly-specified, predictable assembly of microscale building blocks in a semi-autonomous experiment. These magnetic robots can be used to program the assembly of passive building blocks, with the building blocks themselves having the potential to be arbitrarily complex. We extend the magnetic robot actuation work to consider control algorithms for multiple robots by exploiting spatial gradients of magnetic fields. This thesis makes contributions toward actuation, sensing and control of autonomous micro systems and provides technologies that will lead to the development of swarms of microrobots with a suite of manipulation and sensing capabilities working together to sense and modify the environment