With the advancement of industrial manufacturing and an increase in introduction of robots in the workspace, the need of safe operation, communication and information sharing is paramount. The work presented here focuses on cyber-physical system integration through Digital Twin (DT) technology. Our novel DT architecture is based on a model-free Neuro-Adaptive controller (NAC), and an edge-computing scheme for scene monitoring. The NAC can account for varying robot dynamics in both real and virtual environments, and allows for the DT system to expand the realm of cyber-physical integration without expensive model tuning. The edge-computing device introduced in our architecture, observes the robot's workspace from a distance with a wider field of view. This wide viewpoint, enhances the detection and mitigation of any obstacles entering the robot's workspace during operation. We experimentally evaluated the performance of our proposed architecture by introducing dynamic obstacles during a pick-and-place task that both the physical robot and its digital twin had to avoid. Results show that the proposed DT architecture successfully integrates the novel controller and edge-computing elements and successfully performs the given navigation task. The results also show that NAC outperforms a PD controller with more than 70% improvement in joint tracking error between the physical and virtual robots. It was observed that the latency experienced while using NAC is about 48 % lower than when Proportional-Derivative (PD) controller was operational.

In this paper, we develop a novel adaptive motion imitation algorithm (AMI) for robotic systems. Although AMI can be used in a variety of human-robot interaction scenarios, we are particularly interested in robotic rehabilitation where the robot plays the role of demonstrating and practicing challenging motion physiotherapy. During therapy, the robot first demonstrates a reference trajectory to the patient that needs to be repeated during practice and then adapts its motion to a cyclic speed and amplitude based on the patient's abilities. Using this algorithm, the robotic system learns an upper-body motion of the human user and performs a unique, similar, and easier motion based on the learned trajectory from the user. Adaptation in the AMI is based on deep reinforcement learning with deep deterministic policy gradient implemented in the Robot Operating System (ROS) environment. Experimental data collected from 11 users during upper body human-robot imitation sessions with social robot Zeno was used to show that the algorithm can learn reference elbow joint trajectories of the user in an off-line manner after just a few cycles. Finally, we also implemented the algorithm online using the Baxter robot to demonstrate its learning and playback performance.

This paper presents an attention-based, deep learning framework that converts robot camera frames with dynamic content into static frames to more easily apply simultaneous localization and mapping (SLAM) algorithms. The vast majority of SLAM methods have difficulty in the presence of dynamic objects appearing in the environment and occluding the area being captured by the camera. Despite past attempts to deal with dynamic objects, challenges remain to reconstruct large, occluded areas with complex backgrounds. Our proposed Dynamic-GAN framework employs a generative adversarial network to remove dynamic objects from a scene and inpaint a static image free of dynamic objects. The Dynamic-GAN framework utilizes spatial-temporal transformers, and a novel spatial-temporal loss function. The evaluation of Dynamic-GAN was comprehensively conducted both quantitatively and qualitatively by testing it on benchmark datasets, and on a mobile robot in indoor navigation environments. As people appeared dynamically in close proximity to the robot, results showed that large, feature-rich occluded areas can be accurately reconstructed with our attention-based deep learning framework for dynamic object removal. Through experiments we demonstrate that our proposed algorithm has up to 25% better performance on average as compared to the standard benchmark algorithms.

In this paper, we propose a novel online algorithm for motion similarity measurements during human-robot interaction (HRI). Specifically, we formulate a Segment-based Online Dynamic Time Warping (SODTW) algorithm that can be used for understanding of repeated and cyclic human motions, in the context of rehabilitation or social interaction. The algorithm can estimate both the human-robot motion similarity and the time delay to initiate motion and combine these values as a metric to adaptively select appropriate robot imitation repertoires. We validated the algorithm offline by post-processing experimental data collected from a cohort of 55 subjects during imitation episodes with our social robot Zeno. Furthermore, we implemented the algorithm online on Zeno and collected further experimental results with 13 human subjects. These results show that the algorithm can reveal important features of human movement including the quality of motion and human reaction time to robot stimuli. Moreover, the robot can adapt to appropriate human motion speeds based on similarity measurements calculated using this algorithm, enabling future adaptive rehabilitation interventions for conditions such as Autism Spectrum Disorders (ASD).

The importance of tactile sensing for physical human–robot interaction (pHRI) and dexterous manipulation is well known. SkinSim is a novel simulation environment for tactile robot skins in which one can study design tradeoffs involved in deploying whole-body, dense sensor arrays. In this article, scalable modeling approaches are presented for simulating pressure-sensitive robot skin patches, with simultaneous consideration of sensing element geometry and mechanical structure, signal quality, data processing, and closed-loop force controller performance. The open-source simulation architecture of SkinSim is compatible with Gazebo and robot operating system (ROS) programming environments and supports both robot skin dynamic models, as well as tactile sensing element models. An experimentally validated force dispersion model was introduced for the simulation of sensors embedded in a mechanical damping layer. Simulation examples of robot skin with different tactile resolutions are presented using parameter values extracted from an experimental testbed. Thus, simulation results were experimentally validated and the skin sensor density impact on a simple pHRI controller performance was evaluated. Performance measures include center of pressure (COP) estimation error and control signal settling time, overshoot, and steady-state errors. Results suggest that while COP errors decrease in denser sensor skins, controller performance also deteriorates. Therefore, optimal robot skin designs will have to consider application-dependent tradeoffs. Similar results were confirmed in simulation with a larger skin patch containing approximately 4000 tactels and deployed on the end-effector of a collaborative mobile manipulator.Note to Practitioners—This article was motivated by the expensive and time-consuming process of designing tactile skin for robots. When designing pressure-sensitive whole-body sensor arrays for physical human–robot interaction, there are tradeoffs related to sensor resolution, accuracy, and response time, among others. Instead of building prototypes and evaluating them experimentally, the SkinSim simulation environment allows automatic testing of skins with simultaneous consideration of sensing element geometry and mechanical structure, signal quality, data processing, and closed-loop force controller performance. The user can specify several configurations to test and thereby explore the best tradeoffs before actually prototyping any hardware.

With the rise of collaborative robots, the need for safe, reliable, and efficient physical human-robot interaction (pHRI) has grown. High-performance pHRI requires robust and stable controllers suitable for multiple degrees of freedom (DoF) and highly nonlinear robots. In this article, we describe a cascade-loop pHRI controller, which relies on human force and pose measurements and can adapt to varying robot dynamics online. It can also adapt to different users and simplifies the interaction by making the robot behave according to a prescribed dynamic model. In our controller formulation, two neural networks (NNs) in the “outer-loop” predict human motion intent and estimate a reference trajectory for the robot that the “inner-loop” controller follows. The inner-loop imposes a prescribed error dynamics (PED) with the help of a model-free neuroadaptive controller (NAC), which uses a NN to feedback linearize the robot dynamics. Lyapunov stability analysis gives weight tuning laws that guarantee that the error signals are bounded and the desired reference trajectory is achieved. Our control scheme was implemented on a Personal Robot 2 robot and validated through an exploratory experimental study in point-to-point collaborative motion. Results indicate fast convergence of our controller, and the resulting tracking error, motion jerk, and human control effort are comparable with other methods that require prior training, knowledge, and calibration.

In this paper, we introduce a new class of submillimeter robot (ChevBot) for microfactory applications in dry environments, powered by a 532 nm laser beam. ChevBot is an untethered microrobot propelled by a thermal Micro Electro Mechanical (MEMS) actuator upon exposure to the laser light. Novel models for opto-thermal-mechanical energy conversion are proposed to describe the microrobot's locomotion mechanism. First, an opto-thermal simulation model is presented which is experimentally validated with static displacement measurements with microrobots tethered to the substrate. Then, stick and slip motion of the microrobot was predicted using a dynamic extension of our simulation model, and experiments were conducted to validate this model in one dimension. Promising microrobot designs were fabricated on a silicon on insulator (SOI) wafer with 20 μm device layer and a dimple was assembled at the bottom to initiate directional locomotion on a silicon substrate. Validation experiments demonstrate that exposure to laser power below 2W and repetition frequencies below 60 kHz can generate actuator displacements of a few microns, and 46 μm/s locomotion velocity.

This study investigates the effects of inertial sensor placement and noise characteristics on the accuracy of robot pose estimation. Of course, most robots are equipped with joint angle encoders for pose estimation and end-effector positioning. However, in some situations, it's not possible or not desirable to introduce encoders on all joints. Such common examples include legged locomotion, dual arm co-manipulation, and prosthetic limbs. To tackle such situations, one solution is to embed inertial measurement units (IMUs) into artificial skin patches placed on robots' limbs and body. This work analyzes the effects of design parameters such as the number of sensors, their placement on the robot, and noise properties on the quality of robot pose estimation and its signal-to-noise Ratio (SNR). We study the benefits of using a large number of IMUs, which is possible due to the proliferation of inexpensive micro-machined sensors. We use Monte-Carlo simulations and experiments with a two-link robot arm to obtain the distributions of expected estimation error metric values for several accelerometer configurations, which are then compared to determine the optimal number and placement for the IMUs. Results show that the placement of at least two accelerometers on each link has the most significant impact on the pose estimation error, while using a larger number of accelerometers plays a less significant role in reducing the arm pose estimation error and resultant SNR.

In this paper, we introduce a novel application of social robotics in healthcare: high fidelity, facially expressive, robotic patient simulators (RPSs), and explore their usage within a clinical experimental context. Current commercially-available RPSs, the most commonly used humanoid robots worldwide, are substantially limited in their usability and fidelity due to the fact that they lack one of the most important clinical interaction and diagnostic tools: an expressive face. Using autonomous facial synthesis techniques, we synthesized pain both on a humanoid robot and comparable virtual avatar. We conducted an experiment with 51 clinicians and 51 laypersons (n = 102), to explore differences in pain perception across the two groups, and also to explore the effects of embodiment (robot or avatar) on pain perception. Our results suggest that clinicians have lower overall accuracy in detecting synthesized pain in comparison to lay participants. We also found that all participants are overall less accurate detecting pain from a humanoid robot in comparison to a comparable virtual avatar, lending support to other recent findings in the HRI community. This research ultimately reveals new insights into the use of RPSs as a training tool for calibrating clinicians' pain detection skills.

An intelligent human-robot interaction (HRI) system with adjustable robot behavior is presented. The proposed HRI system assists the human operator to perform a given task with minimum workload demands and optimizes the overall human-robot system performance. Motivated by human factor studies, the presented control structure consists of two control loops. First, a robot-specific neuro-adaptive controller is designed in the inner loop to make the unknown nonlinear robot behave like a prescribed robot impedance model as perceived by a human operator. In contrast to existing neural network and adaptive impedance-based control methods, no information of the task performance or the prescribed robot impedance model parameters is required in the inner loop. Then, a task-specific outer-loop controller is designed to find the optimal parameters of the prescribed robot impedance model to adjust the robot's dynamics to the operator skills and minimize the tracking error. The outer loop includes the human operator, the robot, and the task performance details. The problem of finding the optimal parameters of the prescribed robot impedance model is transformed into a linear quadratic regulator (LQR) problem which minimizes the human effort and optimizes the closed-loop behavior of the HRI system for a given task. To obviate the requirement of the knowledge of the human model, integral reinforcement learning is used to solve the given LQR problem. Simulation results on an x - y table and a robot arm, and experimental implementation results on a PR2 robot confirm the suitability of the proposed method.

Effective physical Human-Robot Interaction (pHRI) needs to account for variable human dynamics and also predict human intent. Recently, there has been a lot of progress in adaptive impedance and admittance control for human-robot interaction. Not as many contributions have been reported on online adaptation schemes that can accommodate users with varying physical strength and skill level during interaction with a robot. The goal of this paper is to present and evaluate a novel adaptive admittance controller that can incorporate human intent, nominal task models, as well as variations in the robot dynamics. An outer-loop controller is developed using an ARMA model which is tuned using an adaptive inverse control technique. An inner-loop neuroadaptive controller linearizes the robot dynamics. Working in conjunction and online, this two-loop technique offers an elegant way to decouple the pHRI problem. Experimental results are presented comparing the performance of different types of admittance controllers. The results show that efficient online adaptation of the robot admittance model for different human subjects can be achieved. Specifically, the adaptive admittance controller reduces jerk which results in a smooth human-robot interaction.

This Special Section presents recent and original advances in microassembly methods and technologies. An overview of the technical articles and features is provided.

This paper presents a microassembled robot called the Articulated Four Axes Microrobot (AFAM). Target application areas include micro and nano part manipulation and probing. The robot consists of a cantilever actuated along four axes: in-place$X, Y$and$YAW$; out-of-plane pitch. The microrobot size spans a total volume of$3~{\rm mm}\times 1.5~{\rm mm}\times 1~{\rm mm}$($XYZ$), and operates within a workspace envelope of$50~\mu{\rm m}\times 50~\mu{\rm m}\times 75~\mu{\rm m}$($XYZ$). This is by far the largest operating envelope of any micropositioner with nonplanar dexterity. As a result it can be classified as a new type of three-dimensional microrobot and a candidate for miniaturizing top-down assembly systems to dimensions under$1~{\rm cm}^{3}$. A key feature in this design is a cable-like microwire that transforms in-plane actuator displacement into out-of-plane pitch and yaw motion (via flexure joints). Finite-element analysis simulation followed by microfabrication and assembly processes developed to prototype the designs are described. The microrobot is designed to carry an AFM tip as the end effector and accomplish nanoindentation on a polymer surface. The tip attachment technique and nanoindentation experiments have also been described in this paper. Open loop precision has been characterized using a laser interferometer which measured an average resolution of 50 nm along$XYZ$, repeatability of 100 nm and accuracy of 500 nm. Experiments to determine microrobot reliability are also presented.

This paper proposes a methodology for analysis of kinematic errors in robotic manipulators. The approach is based on interval analysis and predicts end-effector pose deviation from its ideal model using the interval bounds on the errors of the individual axes. In contrast to sampling-based Monte Carlo methods, the proposed methodology offers guaranteed bounds for the error that accumulates at the end-effector. The forward kinematics map is extended to intervals using the product of exponentials formulation with interval joint parameters. This is a convenient method that incorporates both analytical and computational techniques and can be used for error analysis, or inversely, for manipulator design. Simulation and experimental results confirm that the calculated interval bounds fully enclose the end-effector error distribution and provide a measure of its volumetric size. An important application of this method is in the design of modular precision manipulators that can be assembled using individual linear and rotary stages.

The success of assembly and manipulation tasks is highly dependent on the precision of robotic positioners employed. In turn, precision metrics for robots depend on the kinematic design, choice of actuators, sensors, and control system. In this paper, we investigate the effect of parametric uncertainties on the robot precision using interval analysis. The advantage of interval analysis is that it provides rigorous bounds on the effects of errors in terms of interval numbers. Two types of errors are considered: geometric errors due to link and joint parameter uncertainties, and sensing errors due to inaccurate measurement of joint positions. We show that modeling and simulation of these uncertainties using intervals can provide useful insight into the evaluation of manipulator precision for a given task. In particular, simulation results are offered to predict the required tolerances in a peg-in-hole microassembly operation. It is illustrated that the presented approach can replace computationally more expensive Monte-Carlo simulations to estimate the effect of uncertainties.

Reliable manufacturability has always been a major issue in commercialization of complex and heterogeneous microsystems. Though successful for simpler and monolithic microdevices such as accelerometers and pressure sensors of early days, conventional surface micromachining techniques, and in-plane mechanisms do not prove suffice to address the manufacturing of today's wide range of microsystem designs. This has led to the evolution of microassembly as an alternative and enabling technology which can, in principle, build complex systems by assembling heterogeneous microparts of comparatively simpler design; thus reducing the overall footprint of the device and providing high structural rigidity in a cost efficient manner. However, unlike in macroscale assembly systems, microassembly does not enjoy the flexibility of having ready-to-use manipulation systems or standard off-the-shelf components. System specific designs of microparts and mechanisms make the fabrication process expensive and assembly scheme diverse. This warrants for a modular microassembly cell which can execute the assembly process of multiple microsystems by reconfiguring the kinematics setup, end-effectors, feedback system, etc.; thus minimizing the cost of production. In this paper, we present a multiscale assembly and packaging system (MAPS) comprising of 20 degrees of freedom (DoFs) that can be arranged in several reconfigurable micromanipulation modules depending on the specific task. The system has been equipped with multiple custom-designed microgrippers and end-effectors for different applications. Stereo microscopic vision is achieved through four high-resolution cameras. We will demonstrate the construction of two different microsystems using this microassembly cell; the first one is a miniature optical spectrum analyzer called microspectrometer and the second one is a MEMS mobile robot/conveyor called Arripede.

The paper proposes a method of using laser as both a source of energy and means of control for untethered microrobots. Instead of using multiple laser spots to thermally control the motion of multiple robot actuators, the paper proposes to shape the power, frequency, and duty cycle of a single laser beam focused onto the whole body of the robot. It is shown through simulations that an appropriate selection of laser parameters along with a corresponding mechanical design can generate appropriate "stick and slip" motions resulting in 3-DOF (planar) operation for the microrobot with nonholonomic constraints. Based on the simulation results, we anticipate that the microrobot with a thickness of a few micrometers and a width of several hundred micrometers can achieve speeds in excess of a few mm/second, comparable with more conventional electrostatically and electromagnetically actuated microrobots. Initial experiments on chevron actuators confirm that pulsed laser can effectively drive stick-slip microrobots.

The design of a robotic manipulator, including the type of joints, actuators, and other geometric parameters significantly affects its precision (or positioning uncertainty) at the end-effector. Furthermore, sensor and actuator resolution and choice of control scheme will also contribute to the manipulator's precision. Modeling and simulation of these uncertainties can provide useful insight and serve as design guidelines for precision manipulators used in micro and nanomanufacturing. Of particular interest are assembly scenarios where the tolerance budgets are stringent and precision requirements are high, but there is little space for extensive sensor feedback due to a small work volume. In this paper, we investigate the effect of parametric uncertainties in a serial robot chain composed of prismatic or rotary “modules” on the overall positioning uncertainty at the end-effector. Two types of errors are considered: static errors due to misalignment and link parameter uncertainties, and dynamic errors due to inaccurate motion of individual links. Using common uncertainty metrics, we compare the precision of six different robot kinematic chain configurations and select the best suited ones for a generic Peg-in-Hole microassembly task.

Monitoring widespread environmental fields is a complex task that is of great use in many areas, such as building models of natural phenomenon: e.g. moisture in a crop field, oil reservoirs, etc. A successful monitoring of such spatio-temporally distributed fields hinges upon the use of wireless sensor networks which, through their distributed nature, allow for an effective adaptive sampling procedure to gather the statistical information necessary for field density estimation. The adaptive nature of the sampling procedure used embodies a strategy which selects the next sampling location based on the gathered statistical information, and which evolves with past measurements. This paper presents a novel distributed multi-robot "Adaptive sampling algorithm", which is an extension of the algorithm proposed earlier for complex field estimation using a single-robot only. New formulations of sensor fusion in a centralized, decentralized, federated-decentralized, and distributed sensor network are presented for field density estimation, and not just cloud boundary determination. A comparison of the various computational loads involved is included. Simulation results show that adding an efficient partitioning of the sampling area and parallel multi-robot sampling improves the field reconstruction time. With N robots, more than an N-fold reduction in the number of sampling times is observed. The federated and distributed scheme also leads to an improved communication and computational efficiency.

Microassembly is an enabling technology for micro manufacturing that offers well-known pathways to building heterogeneous microsystems with a higher degree of robustness and more complex designs than monolithic fabrication. The success of assembly in micro domain, however, is directly related to the level of precision automation employed. Control and planning are two defining factors for the microassembly yield and its cycle time. Assembly at the microscale harbors many difficult challenges due to scaling of physics, stringent tolerance budget, high precision requirements, limited work volumes, and so on. These difficulties warrant new control and planning algorithms, different than their macro-scale counterparts. In this paper, we use precision metrics to formalize a hybrid controller for automated MEMS assembly. In the past, we formulated the “high yield assembly condition (HYAC)”, which gives a quantitative condition for the success and failure of compliant microassembly. Using this quatitative tool, we formalize a precision-adjusted hybrid controller switching between open, closed, and calibrated operation in the microassembly cell. Simulation and experimental results for the assembly of a microspectrometer are presented to indicate that the proposed hybrid controller lead to high yields at faster cycle times than traditional precision control methods.

Current top down manipulation systems used in micro and nanomanufacturing are many orders of magnitude larger than the parts being handled, leading to difficult tradeoffs between their precision, throughput and cost. This paper presents recent research progress in the manufacturing of millimeter sized robotic positioning technology that allows combining high precision with high throughput along with other application-specific requirements such as strength, dexterity, and work volume. The first robot type is the ARRIpede microcrawler, and we describe recent progress in microrobot packaging and backpack electronics leading to its untethered operation. Precision measurements describing the ARRIpede motion resolution and repeatability are reported. The second microrobot called the Articulated Four Axes Microrobot (AFAM) is a 3D dexterous micromanipulator robot, and we describe nanoindentation experiments using SPM tips mounted on the microrobot. By combining positioning data obtained using laser interferometers and SEM imaging of nanoindentation data, precision metrics such as accuracy, repeatability and resolution of the AFAM robot are determined. Using these two microrobots as basic positioning and manipulation units, we propose a concept for a nanoassembly module, or a so-called wafer-level factory.

This paper presents a unique four-axis articulated MEMS robot, constructed by microassembly, targeting micro and nano scale manipulation and probing applications. The first version of this microrobot has a 2P2R (Prismatic Prismatic Revolute Revolute) kinematic configuration, occupies a total volume of 3mm × 2mm × 1mm, and operates within a workspace envelope of 50µm × 50µm × 75µm. This is by far the largest operating envelope of any independent micropositioner with non-planar dexterity. As a result, it can be classified as a new type of 3 dimensional miniaturized top-down assembly robot with dimensions smaller than 1 cm. The robot incorporates a combination of miniature flexures and cables to drive its joints from high force MEMS actuators. Actuation is accomplished via two banks of in-plane electrothermal actuators, one coupled through an out of plane compliant socket, and the other one coupled remotely via a 30 µm diameter Cu wire. In this paper, we decouple the motion of the robot joints by identifying the robot Jacobian, and we offer preliminary experimental characterization of the microrobot repeatability. Results show that the robot is repeatable to under 0.5 µm along XY and 0.015 degrees along pitch and yaw degrees of freedom.

The dawn of next generation robots and systems era which is quietly emerging, requires miniaturized and integrated sensors, actuators, and entire microrobots. One of the defining characteristics of these microsystems is their multiscale nature, e.g., the span of their size, features, and tolerances across multiple dimensional scales, from the meso to the micro and nanoscales. Another defining characteristic is the need to reliably integrate heterogeneous materials via assembly and packaging, in a cost-effective manner, even in low quantities. Thus, it is argued in this paper that cost-effective manufacturing of complex microsystems requires special precision robotic assembly cells with modular and reconfigurable characteristics. This paper presents recent research aimed at developing theoretical underpinnings for how to construct such a manufacturing platform. M3is a multirobot system spanning across the macro-meso-microscales and specifically configured to package. The M3robots are systematically characterized in terms of quasi-static precision measures and assembly plans are generated using kinematic identification, inverse kinematics and visual servoing. The advantage of our approach the fact that high assembly yields for our system are a consequence of a set of so-called precision resolution-repeatability-accuracy (RRA) rules introduced in this paper. Experimental results for packaging of a microelectromechanical systems switch are provided to support our findings.

Recent advances in 2frac12D and 3D hybrid microassembly using MEMS snap fasteners and die-level bonding for interconnects, makes possible the miniaturization of exciting new small robots configured for various functions, such as flying, crawling, or jumping. ARRIpede is one example of a ldquodie-sizerdquo crawling microrobot constructed by assembly and die stacking. It consists of a MEMS die ldquobodyrdquo, in-plane electrothermal actuators, vertically assembled legs, and an electronic ldquobackpackrdquo to generate the necessary gait sequence. The robot has been designed using a stick-slip simulation model for a target volume of 1.5 cm times 1.5 cm times 0.5 cm, a 3.8 g mass, and velocities up to 3 mm/s. Even though work remains to be completed in packaging the robot, we demonstrated that the robot design is sound by experimentally evaluating the leg actuation force, the payload carrying capacity, the power consumption, and the manipulation ability of an inverted ARRIpede prototype. A configuration that carries a payload approximately equal to its own weight shows excellent steering ability. A reasonable match between simulations and experiments is noted, for example, when the legs are actuated at 45 Hz and 10 V, the crawling velocity of the microrobot was experimentally measured to be 0.84 mm/s or 18.7 mum per step, while the simulated leg displacement was 18.5 mum per step. The prototyped ldquoconveyorrdquo mode had a maximum measured linear velocity in excess of 1.5 mm/s, while consuming approximately 500 mW of power. We expect that for achieving lower speeds, such as 0.15 mm/s, the power consumption can be reduced to a few mW, enabling untethered operation.

Distributed monitoring applications require wireless sensors that are efficiently deployed using robots. This paper proposes to deploy sensor nodes in order to estimate the time-varying spread of wildfires. We propose a distributed multi-scale adaptive sampling strategy based on neural networks, the extended Kalman filter (EKF) and greedy heuristics, named ldquoEKF-NN-GASrdquo. This strategy combines measurements arriving at different times from sensors at different scale lengths, such as ground, air-borne or space-borne observation platforms. We use the EKF covariance matrix to derive quantitative information measures for sampling locations most likely to yield optimal information about the sampled field distribution. Furthermore, we reconstruct the spatio-temporal forest fire spread, based on parameterized radial basis functions (RBF) neural networks. To replicate the complexity involved in actual fire-spread we simulate it using discrete event cellular automata acting as our ldquotruth modelrdquo. Finally, we present experimental results with ground vehicles that navigate over a ldquovirtual firerdquo projected on the lab floor from a ceiling-mounted projector to emulate a sampling mission performed by aerial robots.

One of the major issues enduring with micro-scale mechanics has been to design high fidelity miniature machines capable of performing complex operations. Though achieved in some proportion through conventional in-plane and out-of-plane designs, the efficacy of such micro-electromechanical systems (MEMS) structures is highly limited due to complicate fabrication and inadequate robustness. On the other hand, the use of precise robots to assemble MEMS parts of comparatively simpler design to build 3D micromechanical structures has recently emerged as a viable approach. Such modular assemblies of microscale parts typically utilize minimum energy connectors that are multifunctional, e.g., mechanical, electrical etc. The μ3is a 3D microassembly station consisting of 19 DOF arranged into 3 micromanipulators, with additional microgrippers and stereo microscope vision. The platform is capable of motion resolutions of 3nm and is small enough to be used inside of a scanning electron microscope (SEM) for nano-manipulation. In this paper we discuss how systematic identification and calibration of the station, combined with appropriate part connector designs can lead to multi-degree of freedom active MEMS robots assembled on a wafer

Microgrippers based on electrothermal actuation were designed and fabricated using the deep reactive ion etching (DRIE) process with 100mum thick silicon on insulator (SOI) wafer. The design requirements are restricted to basic manipulation tasks such as pick and place, and nonprehensile manipulation. This paper explores several electrothermal end-effectors which have been fabricated for serial and parallel microassembly. The end-effectors include three main building blocks: 1) Integrated and symmetrical actuators of V and U shapes. The symmetrical expansions on Chevron and hot arms allow combination of forward translations that amplify angular motion at the tips of a gripper. 2) A joule heating element based on a resistive V-shape electrothermal actuator. In 3D microassembly, the joining of a micropart is essentially performed by providing an integrated microheater device. 3) A force or position feedback sensing block based on self-straining or electrostatic principle. The integrated sensor can be calibrated for both position and force measurements. Serial heterogeneous assembly of meso and micro-scale objects is demonstrated using a 3D microassembly station. Black-box dynamical models for microgrippers are derived using experimentally obtained data, and performance variations due to the way the microgrippers are mounted onto the robot are discussed.

This paper presents recent results in configuring a multiscale (macro-micro) robotic assembly platform with modular and reconfigurable characteristics. M3 is a multi-robot system capable of spanning across the macro-meso-micro scales, and has been specifically designed to package MOEMS (Micro Opto Electro Mechanical Systems). The emphasis on packaging (as opposed to assembly) is inclusive of the latter, but it also recognizes that bonding, sealing and attachment processes must also be controlled, and will greatly influence the reliability of the microsystem. The system components include precision robots, microstages, end-effectors and fixtures that accomplish assembly tasks in a shared workspace. The system components are systematically characterized in terms of accuracy and repeatability, and assembly plans are performed using kinematic identification, visual servoing, inverse kinematics, and dynamic vibration suppression. As an application packaging problem, various micro and mesoscale parts are assembled into a MEMS device. The tolerance budget of assembly ranges from 4 microns to 300 microns, while the size of components in the assembly ranges from 126 microns to 30mm. Various end-effectors and fixtures have been designed for use with off-the-shelf hardware (robots and microstages) and were tested for precision performance. The robots are calibrated to accuracies of 10 microns or less. In this paper we present experimental results of precision robot calibration and visual servoing for fiber pigtailing with one of the robots within M3

This paper presents advancement in microassembly and packaging automation accomplished using multiscale robotic platforms. We introduce M3and mu3- two unique multiscale platforms (M3- macro-meso-micro; mu3$meso-micro-nano). The robots with their corresponding end-effectors are characterized in terms of accuracy, repeatability and resolution in positioning. A typical assembly application in the M3consists of components whose size range from 126 microns to 25 millimeters and the typical assembly tolerances range from 4 microns to 300 microns. A typical assembly application for mu3consists of manipulating components mum to 1mm in size, with tolerances between 500nm and 50 mum. The M3has a macroscale workspace and four high precision robots performing coordinated tasks, vision systems for positioning, calibration and closed-loop control, reconfigurable end-effectors for part pick and place and fixtures such as parts tray and tool rests. The mu3has a centimeter scale workspace and 3 robots configured into a 3D microassembly station with 19 DOF with coarse motion servos and fine motion piezos, microgrippers and 3D stereo vision. In addition, the mu3is small enough to be used inside of a scanning electron microscope (SEM). This allows multiscale micro/nano-scale assembly up to a precision of 3 nm. Both multiscale platform stations aim to deliver reconfigurable precision assembly using Labviewtrade based supervisory software modules. Integrated bonding process tools allow for packaging of a variety of different MEMS devices

The use of robotics in distributed monitoring applications requires mobile wireless sensors that are deployed efficiently. Efficiency can be defined in multiple ways, such as in terms of the amount of energy expenditure, communication bandwidth or information content. A very important aspect of mobile sensor deployment includes sampling algorithms at location most likely to yield useful information about a field variable of interest. In this paper, we use inexpensive mobile robot nodes built in our lab (ARRI-Bots) as wireless sensor deployment agents, and we use them to demonstrate information efficient algorithms (e.g., "adaptive sampling"). Each mobile robot node is characterized by sensor measurement noise in addition to localization uncertainty. We use the extended Kalman filter (EKF) to derive quantitative information measures for sampling locations most likely to yield optimal information about the sampled field distribution. We present simulation and experimental results using this approach

The use of robotics in environmental monitoring applications requires distributed sensor systems optimized for effective estimation of relevant models subject to energy and environmental constraints. The mobile robot nodes are agents facilitating the repositioning of sensors in order to estimate a field distribution. This field distribution could be, for instance, water salinity in a lake, or air pollution over an industrial area. Each mobile robot node is characterized by sensor measurement noise in addition to localization uncertainty. This paper addresses an important problem for the robotic deployment of sensor networks, namely adaptive sampling (AS) by selection and repositioning of nodes in order to optimally estimate the parameters of distributed variable field models. The AS problem is posed as a sensor fusion problem within the extended Kalman filter (EKF) framework. We present simulation and experimental results of 2D deployment scenarios using low-cost mobile sensor robots developed in our lab

This paper presents recent results in prototyping of a multiscale (macro-micro) robotic assembly platform with modular and reconfigurable characteristics. The system components include precision robots, microstages, end-effectors and fixtures that accomplish assembly tasks in a shared workspace. The system components are systematically characterized in terms of accuracy and repeatability, and assembly plans are performed using kinematic identification, visual servoing, inverse kinematics, and dynamic vibration suppression. As an application packaging problem, various micro and meso scale parts are assembled into a MEMS device. The tolerance budget of assembly ranges from 4 microns to 300 microns, while the size of components in the assembly ranges from 126 microns to 30 mm. Various end-effectors and fixtures have been designed for use with off-the-shelf hardware (robots and microstages) and were tested for precision performance. The robots and the vision system are calibrated to accuracies of 10 microns or less. Inverse kinematics solutions for the robots have been developed in order to position parts in a global coordinate frame. Conclusions are drawn about implementation of calibration, fixturing and visual servoing in order to assemble within the specified tolerance budget

Wireless sensor networks (WSN) are increasingly used in a multitude of applications such as environmental and structural health monitoring, and condition-based maintenance. Even though the sensors collect a vast amount of data, only a tiny fraction of this data may be useful. This paper introduces and implements a data-logging & supervisory control architecture to manage the information gathered by the WSN and make decisions based on this information. We present an application module which would be able to effectively manipulate the sensor data and to support a discrete event controller (DEC). The DEC is responsible for generating rule-based tasks in order to address information-centric issues such as data-logging, alarm & event reporting and security. A combined data-logging and supervisory control framework (DSC) is proposed to address data pre-processing challenges such as acquiring and recording signals, online analysis, offline analysis, report generation, and data sharing

Wireless communication has been traditionally used in robotics to transmit sensory and telemetry information between a robot and a base station. Because research in mobile robotics has typically focused on navigation, mapping and sensor fusion, network oriented problems such as communication bandwidth optimization, coverage and fault tolerance are not usually considered in this context. The motivation behind this research is formulating and solving combined robot navigation issues (such as obstacle avoidance, environment mapping and coverage) with sensor network issues (such as congestion control, routing and node energy minimization). In this paper we present several types of algorithms for mobile wireless sensor nodes (MWSN) as well as experimental results with a fleet of mobile robots and sensors in our lab. The algorithms include adaptive sampling (AS) for distributed field estimation, potential fields (PF) for communication bandwidth optimization, and a discrete event controller (DEC) for mission planning

The integration of mobile robotic vehicles with distributed sensor networks requires the development of methods for vehicle navigation to achieve sample selection and effectively estimate distributed task variables. In this paper, singular value decomposition (SVD) of the parameter variance space is introduced as a basis for optimal sample selection. Simulation results are used to evaluate the algorithm performance, and significant reduction in field prediction variance are achieved over more conventional incremental rectangular measurement grids. An example of field estimation sensors on an autonomous underwater vehicle (AUV) is described.

Deploying large numbers of sensors has been receiving a lot of attention for detection of hazardous biological or chemical substances in public buildings, airports, shallow water harbors, etc. The sensor-carrying robots are in fact agents that facilitate the repositioning of network nodes in order to increase their coverage and accuracy. Wireless network communication is an essential technology in transmitting the sensed and telemetry information between robots, but it has traditionally been addressed separately from mobile robot navigation. In this work we propose to use a potential field framework to control the behavior of the mobile sensor nodes by combining classical robotic team concepts (obstacle avoidance, goal attainment, flight formation, environment mapping and coverage) with traditional sensor network concepts (node energy minimization, optimal data rate and congestion control, routing in ad-hoc networks). Simulation results are used to illustrate the proposed concepts, and an experimental mobile sensor fleet is built at the author's institution.

Thermal bimorphs are a popular actuation technology in MEMS (micro-electro-mechanical systems). Their operating principle is based on differential thermal expansion induced by Joule heating. Thermal bimorphs, and other thermal flexure actuators have been used in many applications, from micro-grippers, to micro-optical mirrors. In most cases open-loop control is used to difficulties in fabricating positioning sensors together with actuator. In this paper we present several methods for extracting reduced-order thermal flexure actuator models based on experimental data, physical principles, and FEA simulation. We then use the models to generate optimal driving signals using input shaping techniques. Both simulation and experimental results are included to illustrate the efficacy of our approach. This framework can also be applied to other types of MEMS actuators, including electrostatic comb drives.

The assembly of parts with dimensions several hundred microns or less is a challenging problem, and has received increasing attention for applications in areas such as telecommunication, automotive, and biotechnology. Current state of the art micro-assembly systems are often specialized devices and software. In this paper we present a reconflgurable assembly system designed to handle micro-parts in such a way that high precision actuation and sensing is used only in the subsystems where it is actually necessary. Aspects related to part gripping, fucturing, sensing, motion and bonding are discussed. Analysis and experiments are presented to show that this architecture can lead to a relatively low cost and flexible assembly solution.

Force feedback devices for use in virtual reality have been the design, which enable the user to interact with the virtual environment in real-time. We have developed a virtual environment for lumbar puncture, a widely used medical technique involving the insertion of a 3 inch long needle in the spinal region of a patient involving real-time 3D display of the spinal region and real-time force feedback. The trainee can insert a specialized joystick needle in a virtual spine and simultaneously "feel" a realistic force response, matching the real procedure. Our goal is to be able to control the motor such that the whole system "feels" like a needle penetrating the human tissue. In order to create a realistic simulation involving force feedback we show how force feedback theory can be applied to our virtual environment.

For the problem of nonholonomic motion planning in the presence of obstacles, a path-iteration algorithm with an "exterior penalty" function has recently been proposed. The convergence of the iterative algorithm requires the avoidance of singular controls. With the inclusion of a penalty function, the set of singular control may increase substantially. In this paper, we propose a specific guideline for choosing the penalty function which does not introduce additional singular controls. In the case of the N-trailer system, this result can be used to guarantee the convergence of the path planning algorithm. To demonstrate the effectiveness of the proposed algorithm, simulation results are included.

Force control in robotic mechanisms has been extensively researched. There have been several results which have concentrated either on explicit force control or impedance control. Further, many of these works require accurate identification of the stiffness of the environment. In this work the authors examine the role of adaptive controllers for force control with unknown system and environment parameters and clearly integrate several fundamental issues such as explicit force control, impedance control, and impedance control combined with a desired force control. All of these issues are treated using standard model-reference adaptive control (MRAC) approach. Within the same framework, the authors analyze other equally fundamental issues such as environment stiffness identification, stability of the complete system, and parameter convergence. The authors demonstrate the effectiveness of the proposed theory through experiments.

Lumbar puncture for purposes of administering spinal or epidural anesthesia is a complex clinical skill which requires the clinician to precisely correlate a detailed mental map of hidden three-dimensional anatomy with tactile feedback from the spinal needle as it's being inserted. At Dartmouth, a collaborative work between the engineering and medical schools is aimed at developing a computer-based simulation utilizing high-resolution 3D graphics and a force-feedback device to simulate the "feel" of inserting the needle. This paper describes the mechanical design, hardware and software issues and some of the technical challenges being addressed in this project.>