3D visual sensing of human hand for remote operation of a robotic hand

New low cost sensors and open free libraries for 3D image processing are making important advances in robot vision applications possible, such as three-dimensional object recognition, semantic mapping, navigation and localization of robots, human detection and/or gesture recognition for human-machine interaction. In this paper, a novel method for recognizing and tracking the fingers of a human hand is presented. This method is based on point clouds from range images captured by a RGBD sensor. It works in real time and it does not require visual marks, camera calibration or previous knowledge of the environment. Moreover, it works successfully even when multiple objects appear in the scene or when the ambient light is changed. Furthermore, this method was designed to develop a human interface to control domestic or industrial devices, remotely. In this paper, the method was tested by operating a robotic hand. Firstly, the human hand was recognized and the fingers were detected. Secondly, the movement of the fingers was analysed and mapped to be imitated by a robotic hand.

Control framework for dexterous manipulation using dynamic visual servoing and tactile sensors’ feedback

Tactile sensors play an important role in robotics manipulation to perform dexterous and complex tasks. This paper presents a novel control framework to perform dexterous manipulation with multi-fingered robotic hands using feedback data from tactile and visual sensors. This control framework permits the definition of new visual controllers which allow the path tracking of the object motion taking into account both the dynamics model of the robot hand and the grasping force of the fingertips under a hybrid control scheme. In addition, the proposed general method employs optimal control to obtain the desired behaviour in the joint space of the fingers based on an indicated cost function which determines how the control effort is distributed over the joints of the robotic hand. Finally, authors show experimental verifications on a real robotic manipulation system for some of the controllers derived from the control framework.

μ-MAR: Multiplane 3D Marker based Registration for depth-sensing cameras

Many applications including object reconstruction, robot guidance, and. scene mapping require the registration of multiple views from a scene to generate a complete geometric and appearance model of it. In real situations, transformations between views are unknown and it is necessary to apply expert inference to estimate them. In the last few years, the emergence of low-cost depth-sensing cameras has strengthened the research on this topic, motivating a plethora of new applications. Although they have enough resolution and accuracy for many applications, some situations may not be solved with general state-of-the-art registration methods due to the signal-to-noise ratio (SNR) and the resolution of the data provided. The problem of working with low SNR data, in general terms, may appear in any 3D system, then it is necessary to propose novel solutions in this aspect. In this paper, we propose a method, μ-MAR, able to both coarse and fine register sets of 3D points provided by low-cost depth-sensing cameras, despite it is not restricted to these sensors, into a common coordinate system. The method is able to overcome the noisy data problem by means of using a model-based solution of multiplane registration. Specifically, it iteratively registers 3D markers composed by multiple planes extracted from points of multiple views of the scene. As the markers and the object of interest are static in the scenario, the transformations obtained for the markers are applied to the object in order to reconstruct it. Experiments have been performed using synthetic and real data. The synthetic data allows a qualitative and quantitative evaluation by means of visual inspection and Hausdorff distance respectively. The real data experiments show the performance of the proposal using data acquired by a Primesense Carmine RGB-D sensor. The method has been compared to several state-of-the-art methods. The results show the good performance of the μ-MAR to register objects with high accuracy in presence of noisy data outperforming the existing methods.

Visual perception for the 3D recognition of geometric pieces in robotic manipulation

During grasping and intelligent robotic manipulation tasks, the camera position relative to the scene changes dramatically because the robot is moving to adapt its path and correctly grasp objects. This is because the camera is mounted at the robot effector. For this reason, in this type of environment, a visual recognition system must be implemented to recognize and “automatically and autonomously” obtain the positions of objects in the scene. Furthermore, in industrial environments, all objects that are manipulated by robots are made of the same material and cannot be differentiated by features such as texture or color. In this work, first, a study and analysis of 3D recognition descriptors has been completed for application in these environments. Second, a visual recognition system designed from specific distributed client-server architecture has been proposed to be applied in the recognition process of industrial objects without these appearance features. Our system has been implemented to overcome problems of recognition when the objects can only be recognized by geometric shape and the simplicity of shapes could create ambiguity. Finally, some real tests are performed and illustrated to verify the satisfactory performance of the proposed system.

A micro-opto-electro-mechanical system (MOEMS) for microstructure manipulation

Microstructure manipulation is a fundamental process to the study of biology and medicine, as well as to advance micro- and nano-system applications. Manipulation of microstructures has been achieved through various microgripper devices developed recently, which lead to advances in micromachine assembly, and single cell manipulation, among others. Only two kinds of integrated feedback have been demonstrated so far, force sensing and optical binary feedback. As a result, the physical, mechanical, optical, and chemical information about the microstructure under study must be extracted from macroscopic instrumentation, such as confocal fluorescence microscopy and Raman spectroscopy. In this research work, novel Micro-Opto-Electro-Mechanical-System (MOEMS) microgrippers are presented. These devices utilize flexible optical waveguides as gripping arms, which provide the physical means for grasping a microobject, while simultaneously enabling light to be delivered and collected. This unique capability allows extensive optical characterization of the structure being held such as transmission, reflection, or fluorescence. The microgrippers require external actuation which was accomplished by two methods: initially with a micrometer screw, and later with a piezoelectric actuator. Thanks to a novel actuation mechanism, the "fishbone", the gripping facets remain parallel within 1 degree. The design, simulation, fabrication, and characterization are systematically presented. The devices mechanical operation was verified by means of 3D finite element analysis simulations. Also, the optical performance and losses were simulated by the 3D-to-2D effective index (finite difference time domain FDTD) method as well as 3D Beam Propagation Method (3D-BPM). The microgrippers were designed to manipulate structures from submicron dimensions up to approximately 100 μm. The devices were implemented in SU-8 due to its suitable optical and mechanical properties. This work demonstrates two practical applications: the manipulation of single SKOV-3 human ovarian carcinoma cells, and the detection and identification of microparts tagged with a fluorescent "barcode" implemented with quantum dots. The novel devices presented open up new possibilities in the field of micromanipulation at the microscale, scalable to the nano-domain.

Sparse Sample Detection Using Magnetic Bead Manipulation on a Digital Microfluidic Device

This thesis demonstrates a new way to achieve sparse biological sample detection, which uses magnetic bead manipulation on a digital microfluidic device. Sparse sample detection was made possible through two steps: sparse sample capture and fluorescent signal detection. For the first step, the immunological reaction between antibody and antigen enables the binding between target cells and antibody-­‐‑ coated magnetic beads, hence achieving sample capture. For the second step, fluorescent detection is achieved via fluorescent signal measurement and magnetic bead manipulation. In those two steps, a total of three functions need to work together, namely magnetic beads manipulation, fluorescent signal measurement and immunological binding. The first function is magnetic bead manipulation, and it uses the structure of current-­‐‑carrying wires embedded in the actuation electrode of an electrowetting-­‐‑on-­‐‑dielectric (EWD) device. The current wire structure serves as a microelectromagnet, which is capable of segregating and separating magnetic beads. The device can achieve high segregation efficiency when the wire spacing is 50µμm, and it is also capable of separating two kinds of magnetic beads within a 65µμm distance. The device ensures that the magnetic bead manipulation and the EWD function can be operated simultaneously without introducing additional steps in the fabrication process. Half circle shaped current wires were designed in later devices to concentrate magnetic beads in order to increase the SNR of sample detection. The second function is immunological binding. Immunological reaction kits were selected in order to ensure the compatibility of target cells, magnetic bead function and EWD function. The magnetic bead choice ensures the binding efficiency and survivability of target cells. The magnetic bead selection and binding mechanism used in this work can be applied to a wide variety of samples with a simple switch of the type of antibody. The last function is fluorescent measurement. Fluorescent measurement of sparse samples is made possible of using fluorescent stains and a method to increase SNR. The improved SNR is achieved by target cell concentration and reduced sensing area. Theoretical limitations of the entire sparse sample detection system is as low as 1 Colony Forming Unit/mL (CFU/mL).

A Micro-Opto-Electro-Mechanical System (MOEMS) for Microstructure Manipulation

Microstructure manipulation is a fundamental process to the study of biology and medicine, as well as to advance micro- and nano-system applications. Manipulation of microstructures has been achieved through various microgripper devices developed recently, which lead to advances in micromachine assembly, and single cell manipulation, among others. Only two kinds of integrated feedback have been demonstrated so far, force sensing and optical binary feedback. As a result, the physical, mechanical, optical, and chemical information about the microstructure under study must be extracted from macroscopic instrumentation, such as confocal fluorescence microscopy and Raman spectroscopy. In this research work, novel Micro-Opto-Electro-Mechanical-System (MOEMS) microgrippers are presented. These devices utilize flexible optical waveguides as gripping arms, which provide the physical means for grasping a microobject, while simultaneously enabling light to be delivered and collected. This unique capability allows extensive optical characterization of the structure being held such as transmission, reflection, or fluorescence. The microgrippers require external actuation which was accomplished by two methods: initially with a micrometer screw, and later with a piezoelectric actuator. Thanks to a novel actuation mechanism, the “fishbone”, the gripping facets remain parallel within 1 degree. The design, simulation, fabrication, and characterization are systematically presented. The devices mechanical operation was verified by means of 3D finite element analysis simulations. Also, the optical performance and losses were simulated by the 3D-to-2D effective index (finite difference time domain FDTD) method as well as 3D Beam Propagation Method (3D-BPM). The microgrippers were designed to manipulate structures from submicron dimensions up to approximately 100 µm. The devices were implemented in SU-8 due to its suitable optical and mechanical properties. This work demonstrates two practical applications: the manipulation of single SKOV-3 human ovarian carcinoma cells, and the detection and identification of microparts tagged with a fluorescent “barcode” implemented with quantum dots. The novel devices presented open up new possibilities in the field of micromanipulation at the microscale, scalable to the nano-domain.

Computational Foundations for Safe and Efficient Human-Robot Collaboration in Assembly Cells

Human and robots have complementary strengths in performing assembly operations. Humans are very good at perception tasks in unstructured environments. They are able to recognize and locate a part from a box of miscellaneous parts. They are also very good at complex manipulation in tight spaces. The sensory characteristics of the humans, motor abilities, knowledge and skills give the humans the ability to react to unexpected situations and resolve problems quickly. In contrast, robots are very good at pick and place operations and highly repeatable in placement tasks. Robots can perform tasks at high speeds and still maintain precision in their operations. Robots can also operate for long periods of times. Robots are also very good at applying high forces and torques. Typically, robots are used in mass production. Small batch and custom production operations predominantly use manual labor. The high labor cost is making it difficult for small and medium manufacturers to remain cost competitive in high wage markets. These manufactures are mainly involved in small batch and custom production. They need to find a way to reduce the labor cost in assembly operations. Purely robotic cells will not be able to provide them the necessary flexibility. Creating hybrid cells where humans and robots can collaborate in close physical proximities is a potential solution. The underlying idea behind such cells is to decompose assembly operations into tasks such that humans and robots can collaborate by performing sub-tasks that are suitable for them. Realizing hybrid cells that enable effective human and robot collaboration is challenging. This dissertation addresses the following three computational issues involved in developing and utilizing hybrid assembly cells: - We should be able to automatically generate plans to operate hybrid assembly cells to ensure efficient cell operation. This requires generating feasible assembly sequences and instructions for robots and human operators, respectively. Automated planning poses the following two challenges. First, generating operation plans for complex assemblies is challenging. The complexity can come due to the combinatorial explosion caused by the size of the assembly or the complex paths needed to perform the assembly. Second, generating feasible plans requires accounting for robot and human motion constraints. The first objective of the dissertation is to develop the underlying computational foundations for automatically generating plans for the operation of hybrid cells. It addresses both assembly complexity and motion constraints issues. - The collaboration between humans and robots in the assembly cell will only be practical if human safety can be ensured during the assembly tasks that require collaboration between humans and robots. The second objective of the dissertation is to evaluate different options for real-time monitoring of the state of human operator with respect to the robot and develop strategies for taking appropriate measures to ensure human safety when the planned move by the robot may compromise the safety of the human operator. In order to be competitive in the market, the developed solution will have to include considerations about cost without significantly compromising quality. - In the envisioned hybrid cell, we will be relying on human operators to bring the part into the cell. If the human operator makes an error in selecting the part or fails to place it correctly, the robot will be unable to correctly perform the task assigned to it. If the error goes undetected, it can lead to a defective product and inefficiencies in the cell operation. The reason for human error can be either confusion due to poor quality instructions or human operator not paying adequate attention to the instructions. In order to ensure smooth and error-free operation of the cell, we will need to monitor the state of the assembly operations in the cell. The third objective of the dissertation is to identify and track parts in the cell and automatically generate instructions for taking corrective actions if a human operator deviates from the selected plan. Potential corrective actions may involve re-planning if it is possible to continue assembly from the current state. Corrective actions may also involve issuing warning and generating instructions to undo the current task.

Integrated visual perception architecture for robotic clothes perception and manipulation

This thesis proposes a generic visual perception architecture for robotic clothes perception and manipulation. This proposed architecture is fully integrated with a stereo vision system and a dual-arm robot and is able to perform a number of autonomous laundering tasks. Clothes perception and manipulation is a novel research topic in robotics and has experienced rapid development in recent years. Compared to the task of perceiving and manipulating rigid objects, clothes perception and manipulation poses a greater challenge. This can be attributed to two reasons: firstly, deformable clothing requires precise (high-acuity) visual perception and dexterous manipulation; secondly, as clothing approximates a non-rigid 2-manifold in 3-space, that can adopt a quasi-infinite configuration space, the potential variability in the appearance of clothing items makes them difficult to understand, identify uniquely, and interact with by machine. From an applications perspective, and as part of EU CloPeMa project, the integrated visual perception architecture refines a pre-existing clothing manipulation pipeline by completing pre-wash clothes (category) sorting (using single-shot or interactive perception for garment categorisation and manipulation) and post-wash dual-arm flattening. To the best of the author’s knowledge, as investigated in this thesis, the autonomous clothing perception and manipulation solutions presented here were first proposed and reported by the author. All of the reported robot demonstrations in this work follow a perception-manipulation method- ology where visual and tactile feedback (in the form of surface wrinkledness captured by the high accuracy depth sensor i.e. CloPeMa stereo head or the predictive confidence modelled by Gaussian Processing) serve as the halting criteria in the flattening and sorting tasks, respectively. From scientific perspective, the proposed visual perception architecture addresses the above challenges by parsing and grouping 3D clothing configurations hierarchically from low-level curvatures, through mid-level surface shape representations (providing topological descriptions and 3D texture representations), to high-level semantic structures and statistical descriptions. A range of visual features such as Shape Index, Surface Topologies Analysis and Local Binary Patterns have been adapted within this work to parse clothing surfaces and textures and several novel features have been devised, including B-Spline Patches with Locality-Constrained Linear coding, and Topology Spatial Distance to describe and quantify generic landmarks (wrinkles and folds). The essence of this proposed architecture comprises 3D generic surface parsing and interpretation, which is critical to underpinning a number of laundering tasks and has the potential to be extended to other rigid and non-rigid object perception and manipulation tasks. The experimental results presented in this thesis demonstrate that: firstly, the proposed grasp- ing approach achieves on-average 84.7% accuracy; secondly, the proposed flattening approach is able to flatten towels, t-shirts and pants (shorts) within 9 iterations on-average; thirdly, the proposed clothes recognition pipeline can recognise clothes categories from highly wrinkled configurations and advances the state-of-the-art by 36% in terms of classification accuracy, achieving an 83.2% true-positive classification rate when discriminating between five categories of clothes; finally the Gaussian Process based interactive perception approach exhibits a substantial improvement over single-shot perception. Accordingly, this thesis has advanced the state-of-the-art of robot clothes perception and manipulation.

Kinematics and Local Motion Planning for Quasi-static Whole-body Mobile Manipulation

This thesis studies mobile robotic manipulators, where one or more robot manipulator arms are integrated with a mobile robotic base. The base could be a wheeled or tracked vehicle, or it might be a multi-limbed locomotor. As robots are increasingly deployed in complex and unstructured environments, the need for mobile manipulation increases. Mobile robotic assistants have the potential to revolutionize human lives in a large variety of settings including home, industrial and outdoor environments.

Mobile Manipulation is the use or study of such mobile robots as they interact with physical objects in their environment. As compared to fixed base manipulators, mobile manipulators can take advantage of the base mechanism’s added degrees of freedom in the task planning and execution process. But their use also poses new problems in the analysis and control of base system stability, and the planning of coordinated base and arm motions. For mobile manipulators to be successfully and efficiently used, a thorough understanding of their kinematics, stability, and capabilities is required. Moreover, because mobile manipulators typically possess a large number of actuators, new and efficient methods to coordinate their large numbers of degrees of freedom are needed to make them practically deployable. This thesis develops new kinematic and stability analyses of mobile manipulation, and new algorithms to efficiently plan their motions.

I first develop detailed and novel descriptions of the kinematics governing the operation of multi- limbed legged robots working in the presence of gravity, and whose limbs may also be simultaneously used for manipulation. The fundamental stance constraint that arises from simple assumptions about friction and the ground contact and feasible motions is derived. Thereafter, a local relationship between joint motions and motions of the robot abdomen and reaching limbs is developed. Baseeon these relationships, one can define and analyze local kinematic qualities including limberness, wrench resistance and local dexterity. While previous researchers have noted the similarity between multi- fingered grasping and quasi-static manipulation, this thesis makes explicit connections between these two problems.

The kinematic expressions form the basis for a local motion planning problem that that determines the joint motions to achieve several simultaneous objectives while maintaining stance stability in the presence of gravity. This problem is translated into a convex quadratic program entitled the balanced priority solution, whose existence and uniqueness properties are developed. This problem is related in spirit to the classical redundancy resoxlution and task-priority approaches. With some simple modifications, this local planning and optimization problem can be extended to handle a large variety of goals and constraints that arise in mobile-manipulation. This local planning problem applies readily to other mobile bases including wheeled and articulated bases. This thesis describes the use of the local planning techniques to generate global plans, as well as for use within a feedback loop. The work in this thesis is motivated in part by many practical tasks involving the Surrogate and RoboSimian robots at NASA/JPL, and a large number of examples involving the two robots, both real and simulated, are provided.

Finally, this thesis provides an analysis of simultaneous force and motion control for multi- limbed legged robots. Starting with a classical linear stiffness relationship, an analysis of this problem for multiple point contacts is described. The local velocity planning problem is extended to include generation of forces, as well as to maintain stability using force-feedback. This thesis also provides a concise, novel definition of static stability, and proves some conditions under which it is satisfied.

Advanced electrospun nanofibrous mats for hindering delamination, improving damping and for sensing of composite laminates

Carbon Fiber Reinforced Polymers (CFRPs) display high specific mechanical properties, allowing the creation of lightweight components and products by metals replacement. To reach outstanding mechanical performances, the use of stiff thermoset matrices, like epoxy, is preferred. Laminated composites are commonly used for their ease of manipulation during object manufacturing. However, the natural anisotropic structure of laminates makes them vulnerable toward delamination. Moreover, epoxy-based CFRPs are very stiff materials, thus showing low damping capacity, which results in unwanted vibrations and structure-borne noise that may contribute to delamination triggering. Hence, searching for systems able to limit these drawbacks is of primary importance for safety reasons, as well as for economic ones. In this experimental thesis, the production and integration of innovative rubbery nanofibrous mats into CFRP laminates are presented. A smart approach, based on single-needle electrospinning of rubber-containing blends, is proposed for producing dimensionally stable rubbery nanofibers without the need for rubber crosslinking. Nano-modified laminates aim at obtaining structural composites with improved delamination resistance and enhanced damping capacity, without significantly lowering other relevant mechanical properties. The possibility of producing nanofibers nano-reinforced with graphene to be applied for reinforcing composite laminates is also investigated. Moreover, the use of piezoelectric nanofibrous mats in hybrid composite laminates for achieving self-sensing capability is presented too as a different approach to prevent the catastrophic consequences of possible structural laminate failure. Finally, an accurate, systematic, and critical study concerning tensile testing of nonwovens, using electrospun Nylon 66 random nanofibrous mats as a case study, is proposed. Nanofibers diameter and specimen geometry were investigated to thoroughly describe the nanomat tensile behaviour, also considering the polymer thermal properties, and the number of nanofibers crossings as a function of the nanofibers diameter. Stress-strain data were also analysed using a phenomenological data fitting model to interpret the tensile behaviour better.

Robotic Manipulation of DLOs for Wiring Harness Assembly: a Machine Learning Approach

In recent times, a significant research effort has been focused on how deformable linear objects (DLOs) can be manipulated for real world applications such as assembly of wiring harnesses for the automotive and aerospace sector. This represents an open topic because of the difficulties in modelling accurately the behaviour of these objects and simulate a task involving their manipulation, considering a variety of different scenarios. These problems have led to the development of data-driven techniques in which machine learning techniques are exploited to obtain reliable solutions. However, this approach makes the solution difficult to be extended, since the learning must be replicated almost from scratch as the scenario changes. It follows that some model-based methodology must be introduced to generalize the results and reduce the training effort accordingly. The objective of this thesis is to develop a solution for the DLOs manipulation to assemble a wiring harness for the automotive sector based on adaptation of a base trajectory set by means of reinforcement learning methods. The idea is to create a trajectory planning software capable of solving the proposed task, reducing where possible the learning time, which is done in real time, but at the same time presenting suitable performance and reliability. The solution has been implemented on a collaborative 7-DOFs Panda robot at the Laboratory of Automation and Robotics of the University of Bologna. Experimental results are reported showing how the robot is capable of optimizing the manipulation of the DLOs gaining experience along the task repetition, but showing at the same time a high success rate from the very beginning of the learning phase.

Apoplastic And Intracellular Plant Sugars Regulate Developmental Transitions In Witches' Broom Disease Of Cacao.

Witches' broom disease (WBD) of cacao differs from other typical hemibiotrophic plant diseases by its unusually long biotrophic phase. Plant carbon sources have been proposed to regulate WBD developmental transitions; however, nothing is known about their availability at the plant-fungus interface, the apoplastic fluid of cacao. Data are provided supporting a role for the dynamics of soluble carbon in the apoplastic fluid in prompting the end of the biotrophic phase of infection. Carbon depletion and the consequent fungal sensing of starvation were identified as key signalling factors at the apoplast. MpNEP2, a fungal effector of host necrosis, was found to be up-regulated in an autophagic-like response to carbon starvation in vitro. In addition, the in vivo artificial manipulation of carbon availability in the apoplastic fluid considerably modulated both its expression and plant necrosis rate. Strikingly, infected cacao tissues accumulated intracellular hexoses, and showed stunted photosynthesis and the up-regulation of senescence markers immediately prior to the transition to the necrotrophic phase. These opposite findings of carbon depletion and accumulation in different host cell compartments are discussed within the frame of WBD development. A model is suggested to explain phase transition as a synergic outcome of fungal-related factors released upon sensing of extracellular carbon starvation, and an early senescence of infected tissues probably triggered by intracellular sugar accumulation.

Remote sensing the vertical profile of cloud droplet effective radius, thermodynamic phase, and temperature

Cloud-aerosol interaction is a key issue in the climate system, affecting the water cycle, the weather, and the total energy balance including the spatial and temporal distribution of latent heat release. Information on the vertical distribution of cloud droplet microphysics and thermodynamic phase as a function of temperature or height, can be correlated with details of the aerosol field to provide insight on how these particles are affecting cloud properties and their consequences to cloud lifetime, precipitation, water cycle, and general energy balance. Unfortunately, today's experimental methods still lack the observational tools that can characterize the true evolution of the cloud microphysical, spatial and temporal structure in the cloud droplet scale, and then link these characteristics to environmental factors and properties of the cloud condensation nuclei. Here we propose and demonstrate a new experimental approach (the cloud scanner instrument) that provides the microphysical information missed in current experiments and remote sensing options. Cloud scanner measurements can be performed from aircraft, ground, or satellite by scanning the side of the clouds from the base to the top, providing us with the unique opportunity of obtaining snapshots of the cloud droplet microphysical and thermodynamic states as a function of height and brightness temperature in clouds at several development stages. The brightness temperature profile of the cloud side can be directly associated with the thermodynamic phase of the droplets to provide information on the glaciation temperature as a function of different ambient conditions, aerosol concentration, and type. An aircraft prototype of the cloud scanner was built and flew in a field campaign in Brazil. The CLAIM-3D (3-Dimensional Cloud Aerosol Interaction Mission) satellite concept proposed here combines several techniques to simultaneously measure the vertical profile of cloud microphysics, thermodynamic phase, brightness temperature, and aerosol amount and type in the neighborhood of the clouds. The wide wavelength range, and the use of multi-angle polarization measurements proposed for this mission allow us to estimate the availability and characteristics of aerosol particles acting as cloud condensation nuclei, and their effects on the cloud microphysical structure. These results can provide unprecedented details on the response of cloud droplet microphysics to natural and anthropogenic aerosols in the size scale where the interaction really happens.

Manipulation of quantum state transfer in cold Rydberg atom collisions

We investigate the role of the dc Stark effect in multilevel pairwise interactions between cold Rydberg atoms. We have observed the decay of nD + nD quasi-molecules by detecting the products in the (n + 2) P state after pulsed excitation for 29 <= n <= 41. The decay rate can be manipulated with a dc electric field and requires a consideration of the multilevel nature of the process to explain the observations. The time dependence of the (n + 2) P signal is found to support a time-dependent picture of the dynamics.