A survey of robot manipulation in contact

In this survey, we present the current status on robots performing manipulation tasks that require varying contact with the environment, such that the robot must either implicitly or explicitly control the contact force with the environment to complete the task. Robots can perform more and more manipulation tasks that are still done by humans, and there is a growing number of publications on the topics of (1) performing tasks that always require contact and (2) mitigating uncertainty by leveraging the environment in tasks that, under perfect information, could be performed without contact. The recent trends have seen robots perform tasks earlier left for humans, such as massage, and in the classical tasks, such as peg-in-hole, there is a more efficient generalization to other similar tasks, better error tolerance, and faster planning or learning of the tasks. Thus, in this survey we cover the current stage of robots performing such tasks, starting from surveying all the different in-contact tasks robots can perform, observing how these tasks are controlled and represented, and finally presenting the learning and planning of the skills required to complete these tasks

Master of Science

thesisIn performing microsurgeries, the procedure of vascular anastomosis is performed frequently. When executing this procedure, the most widely used method is hand suturing the vessels back together. This process, however, is extremely time consuming (depending on the size and location of the vessel and the experience of the surgeon) and is subject to human error. The vascular coupling device and its accompanying installation tools in this work have been designed and tested to reduce human error and significantly decrease the amount of time required to perform the anastomosis. Tests that were performed on the vascular coupling device include a pressure leak test (both open-end and sealed-end), a tensile test, and the time required to complete the anastomosis. The coupler was also installed on the carotid artery of a cadaver swine. The coupling device had significantly less leakage than hand sutured anastomoses (p values of approximately 0.05 or lower), was able to withstand an average tensile force of 5.52 ± 2.34 N (n=5) before failure, and was installed in an average of 7 min and 34 sec (n=3)

Exploiting the Asymmetric Energy Barrier in Multi-Stable Origami to Enable Mechanical Diode Behavior

Recently, multi-stable origami have drawn many attentions for their potential applications in multi-functional structures and material systems. Especially, origami folding is essentially a three-dimensional mechanism, which induces unorthodox properties that distinguish this mechanism from its traditional counterparts. This study proposes a multi-stable origami cellular structure that can exhibit mechanical diode behavior in compression. Furthermore, with a small variation in the unit cell of the proposed structure, a extension diode can be achieved. Such structures consist of many stacked Miura-ori sheets, and can be divided into unit cells that pose two different stable configurations. To understand and elucidate the underlying mechanisms, two adjacent unit cells were considered as the most fundamental constituents of the cellular structures that display the desired diode behavior. This study examines how folding can impose a kinematic constraint onto the deformation of these two dual cell chains via estimating the elastic potential energy landscapes of two dual assemblies. For the compression diode, this folding-induced constraint increase the energy barrier for compressing from a certain stable state to another, however, the same constraint does not increase the energy barrier of the opposite extension. Thus, one should apply a large force to compress the chain, but a small force to extend it. As a result, a compression mechanical diode is achieved. This constraint acts the opposite way in extension diode. Then, four prototypes were fabricated to experimentally validate the analytical results. The results of this study can open new avenues towards multi-functional structure and materials systems capable of motion rectifying, wave propagation control, and even mechanical computation

On the Statics, Dynamics, and Stability of Continuum Robots: Model Formulations and Efficient Computational Schemes

This dissertation presents advances in continuum-robotic mathematical-modeling techniques. Specifically, problems of statics, dynamics, and stability are studied for robots with slender elastic links. The general procedure within each topic is to develop a continuous theory describing robot behavior, develop a discretization strategy to enable simulation and control, and to validate simulation predictions against experimental results.Chapter 1 introduces the basic concept of continuum robotics and reviews progress in the field. It also introduces the mathematical modeling used to describe continuum robots and explains some notation used throughout the dissertation.The derivation of Cosserat rod statics, the coupling of rods to form a parallel continuum robot (PCR), and solution of the kinematics problem are reviewed in Chapter 2. With this foundation, soft real-time teleoperation of a PCR is demonstrated and a miniature prototype robot with a grasper is controlled.Chapter 3 reviews the derivation of Cosserat rod dynamics and presents a discretization strategy having several desirable features, such as generality, accuracy, and potential for good computational efficiency. The discretized rod model is validated experimentally using high speed camera footage of a cantilevered rod. The discretization strategy is then applied to simulate continuum robot dynamics for several classes of robot, including PCRs, tendon-driven robots, fluidic actuators, and concentric tube robots.In Chapter 4, the stability of a PCR is analyzed using optimal control theory. Conditions of stability are gradually developed starting from a single planar rod and finally arriving at a stability test for parallel continuum robots. The approach is experimentally validated using a camera tracking system.Chapter 5 provides closing discussion and proposes potential future work

Generative Models for Learning Robot Manipulation Skills from Humans

A long standing goal in artificial intelligence is to make robots seamlessly interact with humans in performing everyday manipulation skills. Learning from demonstrations or imitation learning provides a promising route to bridge this gap. In contrast to direct trajectory learning from demonstrations, many problems arise in interactive robotic applications that require higher contextual level understanding of the environment. This requires learning invariant mappings in the demonstrations that can generalize across different environmental situations such as size, position, orientation of objects, viewpoint of the observer, etc. In this thesis, we address this challenge by encapsulating invariant patterns in the demonstrations using probabilistic learning models for acquiring dexterous manipulation skills. We learn the joint probability density function of the demonstrations with a hidden semi-Markov model, and smoothly follow the generated sequence of states with a linear quadratic tracking controller. The model exploits the invariant segments (also termed as sub-goals, options or actions) in the demonstrations and adapts the movement in accordance with the external environmental situations such as size, position and orientation of the objects in the environment using a task-parameterized formulation. We incorporate high-dimensional sensory data for skill acquisition by parsimoniously representing the demonstrations using statistical subspace clustering methods and exploit the coordination patterns in latent space. To adapt the models on the fly and/or teach new manipulation skills online with the streaming data, we formulate a non-parametric scalable online sequence clustering algorithm with Bayesian non-parametric mixture models to avoid the model selection problem while ensuring tractability under small variance asymptotics. We exploit the developed generative models to perform manipulation skills with remotely operated vehicles over satellite communication in the presence of communication delays and limited bandwidth. A set of task-parameterized generative models are learned from the demonstrations of different manipulation skills provided by the teleoperator. The model captures the intention of teleoperator on one hand and provides assistance in performing remote manipulation tasks on the other hand under varying environmental situations. The assistance is formulated under time-independent shared control, where the model continuously corrects the remote arm movement based on the current state of the teleoperator; and/or time-dependent autonomous control, where the model synthesizes the movement of the remote arm for autonomous skill execution. Using the proposed methodology with the two-armed Baxter robot as a mock-up for semi-autonomous teleoperation, we are able to learn manipulation skills such as opening a valve, pick-and-place an object by obstacle avoidance, hot-stabbing (a specialized underwater task akin to peg-in-a-hole task), screw-driver target snapping, and tracking a carabiner in as few as 4 - 8 demonstrations. Our study shows that the proposed manipulation assistance formulations improve the performance of the teleoperator by reducing the task errors and the execution time, while catering for the environmental differences in performing remote manipulation tasks with limited bandwidth and communication delays

Structural Systems Inspired by the Architecture of Skeletal Muscle

Modern engineering applications call for structural and material systems that exhibit advanced performance. To achieve this performance, researchers often look to nature for inspiration. Skeletal muscle is a multifunctional system with remarkable versatility and robustness, offering a great example on how to effectively store, convert, and release energy for force generation and shape change. To date, most efforts seeking to emulate muscle have focused on its bulk characteristics. However, it has recently been shown that many of muscle’s advantageous properties arise from the assembly and geometry of its microscale constituents. This dissertation will aim to develop new concepts for structural and material systems inspired by a fundamental understanding of the assembly of muscle’s constituent elements into contractile units. This is achieved by exploiting two key ingredients expressed by these constituents: metastability, which is the existence of multiple stable conformations for a prescribed global geometry, and ¬¬local conformation changes to switch between these stable topologies. Rather than faithfully emulating or seeking to explain the complex chemo-mechanical processes that govern muscle contraction, the major contributions of this thesis arise from the exploitation of the aforementioned key features within the context of engineered structures and materials systems. First, a fundamental metastable unit is studied under harmonic excitation. Experimental, numerical, and analytical investigations uncover the coexistence of multiple response regimes with significantly different amplitudes. These distinct regimes are exploited to achieve highly adaptable energy dissipation characteristics that vary by up to two orders of magnitude among them, even as excitation parameters are held constant. On the other hand, introducing asymmetry by varying a static bias parameter allows for smooth, finer variation of energy dissipation performance. Then, inspired by the ability of the myofibril lattice in skeletal muscle to trap strain energy that can be released on-demand, this thesis explores structural systems that leverage asymmetric multistability for energy capture and storage. The initial kinetic energy from impulsive excitation is shown to trigger state transitions that result in the capture of recoverable strain energy in higher-potential states. Reverse transitions to lower-energy states exploit this stored energy to facilitate efficient deployment and length change in the structure. Lastly, the effect of myofibril lattice spacing in skeletal muscle, and shear-like motions of adjacent filaments during contraction, serves as inspiration for the development of an architected modular material system that uses transverse confinements in conjunction with oblique, shear-like motions to give rise to sudden state transitions. Numerical results provide insight into the experimentally-observed behaviors, revealing that these energy-releasing transitions correspond to discrete changes in reaction force magnitude and direction Mechanical response properties can be tailored by strategic variation of transverse confinement and system geometry. Analytical tools using relatively simple models are developed to offer meaningful prediction of the above features. The overall outcomes of this thesis reveal great potential to develop high-performance, versatile, and adaptable structural and material systems by exploiting fundamental features of skeletal muscle architecture.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145893/1/kidambi_1.pd

Stability Analysis of Plates and Shells

This special publication contains the papers presented at the special sessions honoring Dr. Manuel Stein during the 38th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference held in Kissimmee, Florida, Apdl 7-10, 1997. This volume, and the SDM special sessions, are dedicated to the memory of Dr. Manuel Stein, a major pioneer in structural mechanics, plate and shell buckling, and composite structures. Many of the papers presented are the work of Manny's colleagues and co-workers and are a result, directly or indirectly, of his influence. Dr. Stein earned his Ph.D. in Engineering Mechanics from Virginia Polytechnic Institute and State University in 1958. He worked in the Structural Mechanics Branch at the NASA Langley Research Center from 1943 until 1989. Following his retirement, Dr. Stein continued his involvement with NASA as a Distinguished Research Associate