Human brain possesses the ability to effectively focus on important environmental components, which enhances perception, learning, reasoning, and decision-making. Inspired by this cognitive mechanism, we introduced a novel concept termed relevance for Human-Robot Collaboration (HRC). Relevance is a dimensionality reduction process that incorporates a continuously operating perception module, evaluates cue sufficiency within the scene, and applies a flexible formulation and computation framework. In this paper, we present an enhanced two-loop framework that integrates real-time and asynchronous processing to quantify relevance and leverage it for safer and more efficient human-robot collaboration (HRC). The two-loop framework integrates an asynchronous loop, which leverages an LLM's world knowledge to quantify relevance, and a real-time loop, which performs scene understanding, human intent prediction, and decision-making based on relevance. HRC decision-making is enhanced by a relevancebased task allocation method, as well as a motion generation and collision avoidance approach that incorporates human trajectory prediction. Simulations and experiments show that our methodology for relevance quantification can accurately and robustly predict the human objective and relevance, with an average accuracy of up to 0.90 for objective prediction and up to 0.96 for relevance prediction. Moreover, our motion generation methodology reduces collision cases by 63.76% and collision frames by 44.74% when compared with a state-of-theart (SOTA) collision avoidance method. Our framework and methodologies, with relevance, guide the robot on how to best assist humans and generate safer and more efficient actions for HRC.

Perception plays a pivotal role in enhancing the functionality of autonomous agents. However, the intricate relationship between robotic perception metrics and actuation metrics remains unclear, leading to ambiguity in the development and fine-tuning of perception algorithms. In this paper, we introduce a methodology for quantifying this relationship, taking into account factors such as detection rate, detection quality, and latency. Furthermore, we introduce two novel perception metrics for Human-Robot Collaboration safety predicated upon basic perception metrics: Critical Collision Probability (CCP) and Average Collision Probability (ACP). To validate the utility of these metrics in facilitating algorithm development and tuning, we develop an attentive processing strategy that focuses exclusively on key input features. This approach significantly reduces computational time while preserving a similar level of accuracy. Experimental findings demonstrate that integrating this strategy into an object detector results in a notable maximum reduction of 30.09% in inference time and 26.53% in total time per frame. Additionally, the strategy lowers the CCP and ACP in a baseline model by 11.25% and 13.50%, respectively.

Robotic 3D Concrete Printing (3DCP) is a process of additive manufacturing using building materials. The system that performs 3DCP is a complex system consisting of multiple parts that are independent of each other. However, conventional 3DCP workflows usually lack automatic monitoring of print quality which can be easily affected for various reasons. This paper proposes an integrated workflow of automatic detection of filament deviation in a 3DCP process. The deformation of the filament is adopted as the criterion for print quality evaluation. A Deep Learning-morphology-based filament width estimation method is developed, and a filament deviation detection algorithm with presence of parametric uncertainties is proposed. This workflow allows to detect width deviations in the printed filament by considering several parameters of the printing system. The integrated workflow is implemented and tested through on-site printing tests.

Condition synthesis is vital for generating data for fault detection and diagnosis studies. Traditional methods rely heavily on human labor. This study proposes a robotic method and its instru-ment to efficiently synthesize faulty conditions and mass-produce data to develop fault detection and diagnosis algorithms. The first contribution is the formalization of a new approach called Robotic Condition Synthesis, which shifts the traditionally labor-intensive task of condition synthesis to a robot-based force control task. The second contribution is developing a new robotic manipulator, which is more effective than current lab-grade robots for the tasks involved in the Robotic Condition Synthesis. The third contribution is empirical evidence of the superiority of this new robot in performing the Robotic Condition Synthesis tasks. This study also explores the potential of the new robot by conducting a three-dimensional system identification of a rotordynamic plant, which lays the foundation for more advanced Robotic Condition Synthesis policies in the future.

The time-series datasets commonly applied for anomaly detection research showcase specific suboptimal properties. This work novelly conceptualizes condition state synthesis to improve the data-synthetic pipeline of an anomalous-event dataset. We demonstrate two technical contributions in this study. First, we propose a methodology to formulate, accelerate and enrich the condition state synthetic process. The proposed method includes three critical phases: analysis of a rotodynamic plant, systematic design of its condition state space, and development of a Markovian model for controlled state transitions. Second, a Rotodynamic System with Synthetic Anomaly dataset is constructed. It is a large-scale time-series dataset featuring controlled, abundant and diverse anomalous condition states, and per-time-step condition state labels. A comprehensive learning-based case study is conducted to illustrate that these unique features tangibly benefit anomaly detection research. Potential usages of the proposed dataset as an anomaly detection study benchmark are discussed.

This paper focuses on problems associated with the deployment of automatic agents for last-mile delivery. We propose a framework and methodology to systematically evaluate and compare different hybrid strategies. Performance metrics in agent noise, delivery time, energy consumption, coverage rate, package throughput, and system costs are defined rigorously and modeled mathematically. Using the methodology, we conduct a case study in the city of Boston for four agent delivery strategies, including a hybrid strategy proposed in this paper. The proposed strategy utilizes available space in public transits' cabins during off-peak hours to relocate the agent traveling start locations. Simulations and analyses show that hybrid strategies outperform the Agent-Only delivery strategy in terms of noise exposure, energy consumption, and coverage rate. The performance of hybrid strategies highly depends on the characteristics of the ground transportation methods accompanying agents. Thus, the methods of ground transportation should carefully be examined and selected for each case and strategy in real-world applications.

Data synthesizing is crucial for data-driven anomaly prognostics on physical machines. We propose the first general-purpose anomalous scenario synthesizer, GPASS, for rotary equipment. More specifically, we present a design of implementing modular rotational damping, large lateral force, with high-frequency range capability as fundamental modes of physical inputs. The GPASS is a general-purpose platform that can impose inputs independently or jointly, and generate an extensive range of anomalous scenarios on the same subject. Finally, it has the capability of capturing multi-variate sensor readings on the same anomalous event. Experimental results demonstrate that the synthesizer can dynamically and accurately introduce lateral force at specified magnitudes and frequencies, proving the effectiveness of the proposed device.

The recent development of in-pipe robots (IPR) with locomotion and inspection functions provides a new possibility to water distribution pipe maintenance - to rehabilitate pipe defects internally. Yet only a limited number of Rehabilitation in-pipe robots (R-IPR) have been proposed. One primary concern that impedes the development of Rehabilitation in-pipe robots is the excessive amount of contamination generated during the rehabilitation process. Correspondingly, we propose a novel concept: Contamination-Less in-pipe Rehabilitation (CLR) and develop the CLR in-pipe robot as an innovative solution. The proposed robot contains three modules for pipe-surface sealing, pipe-wall cleaning, and in-pipe manipulation. This paper centers on the comprehensive design of the manipulator module. First, the manipulator features a high-DoF configuration to deploy the other two modules simultaneously. Second, the configuration adopts a nested-outer-inner architecture to ensure the seal always encloses the pipe-wall cleaning device. The holistic and detailed design process of the manipulator, including design concept, kinematics, load requirements, design for manufacturing, and simulated deployment, are presented. Eventually, the fully implemented robot accomplished the first Contamination-Less in-pipe Rehabilitation.

The twenty-six papers in this special issue focus on Red Blue Green (RBG)-D vision, an emerging research topic in computer vision, with a number of applications in robotics, entertainment, biometrics and multimedia. Compared to 2D images and 3D data (including depth images, point clouds and meshes), RGB-D images represent both the photometric and geometric information of a scene. Moreover, low-cost consumer depth cameras (e.g., Microsoft Kinect v2, Intel Realsense, Orbbec Astra) can enable realtime applications due to their high acquisition frame-rate. In the last few years, a large number of RGB-D datasets have also been publicly released to tackle various vision tasks. Although remarkable progress has been achieved, several critical problems still remain open. The aim of this special issue is to stimulate researchers from different fields to present their state-of-the-art work, and to provide a cross-fertilization ground for discussions on the next steps in this important research area.

This article proposes a confidence-based approach for combining two visual tracking techniques to minimize the influence of unforeseen visual tracking failures to achieve uninterrupted vision-based control. Despite research efforts in vision-guided micromanipulation, existing systems are not designed to overcome visual tracking failures, such as inconsistent illumination condition, regional occlusion, unknown structures, and nonhomogenous background scene. There remains a gap in expanding current procedures beyond the laboratory environment for practical deployment of vision-guided micromanipulation system. A hybrid tracking method, which combines motion-cue feature detection and score-based template matching, is incorporated in an uncalibrated vision-guided workflow capable of self-initializing and recovery during the micromanipulation. Weighted average, based on the respective confidence indices of the motion-cue feature localization and template-based trackers, is inferred from the statistical accuracy of feature locations and the similarity score-based template matches. Results suggest improvement of the tracking performance using hybrid tracking under the conditions. The mean errors of hybrid tracking are maintained at subpixel level under adverse experimental conditions while the original template matching approach has mean errors of 1.53, 1.73, and 2.08 pixels. The method is also demonstrated to be robust in the nonhomogeneous scene with an array of plant cells. By proposing a self-contained fusion method that overcomes unforeseen visual tracking failures using pure vision approach, we demonstrated the robustness in our developed low-cost micromanipulation platform.

Virtually every mechanized form of transportation, power generation system, industrial equipment, and robotic system has rotating shafts. As the shaft is often the main means of mechanical power transmission, measuring the torque, speed, vibration, and bending of the shaft can be used in many cases to access device performance and health and to implement controls. This paper proposes a shaft sensor that measures all of these phenomena with reasonable accuracy while having a low cost and simple installation process. This sensor transfers strain from the shaft and amplifies it to increase sensitivity. Furthermore, this sensor requires no components to be in the stationary reference frame, allowing the entire device to rotate with the shaft. A prototype is presented. Experimental results illustrate the effectiveness of the proposed system.

Automatic targeting of plant cells to perform tasks like extraction of chloroplast is often desired in the study of plant biology. Hence, this paper proposes an improved cell segmentation method combined with a robust tracking algorithm for vision-guided micromanipulation in plant cells. The objective of this work is to develop an automatic plant cell detection and localization technique to complete the automated workflow for plant cell manipulation. The complex structural properties of plant cells make both segmentation of cells and visual tracking of the microneedle immensely challenging, unlike single animal cell applications. Thus, an improved version of watershed segmentation with adaptive thresholding is proposed to detect the plant cells without the need for staining of the cells or additional tedious preparations. To manipulate the needle to reach the identified centroid of the cells, tracking of the needle tip is required. Visual and motion information from two data sources namely, template tracking and projected manipulator trajectory are combined using score-based normalized weighted averaging to continuously track the microneedle. The selection of trackers is influenced by their complementary nature as the former and latter are individually robust against physical and visual uncertainties, respectively. Experimental results validate the effectiveness of the proposed method by detecting plant cell centroids accurately, tracking the microneedle constantly and reaching the plant cell of interest despite the presence of visual disturbances.

Haptic devices have been employed to immerse users in VR environments. In particular, hand and finger haptic devices have been deeply developed. However, this type of devices occludes hand detection for some tracking systems, or, for some other tracking systems, it is uncomfortable for the users to wear two different devices (haptic and tracking device) on both hands. We introduce RecyGlide, a novel wearable multimodal display located at the forearm. The RecyGlide is composed of inverted five-bar linkages with 2 degrees of freedom (DoF) and vibration motors (see Fig. 1.(a). The device provides multimodal tactile feedback such as slippage, force vector, pressure, and vibration. We tested the discrimination ability of monomodal and multimodal stimuli patterns on the forearm and confirmed that the multimodal patterns have higher recognition rate. This haptic device was used in VR applications, and we proved that it enhances VR experience and makes it more interactive.

This paper presents an image-based approach to estimate the motorcycle roll angle. The algorithm estimates directly the absolute roll to the road plane by means of a basic monocular camera. This means that the estimated roll angle is not affected by the road bank which is often a problem for vehicle observation and control purposes. For each captured image, the algorithm uses a numeric roll loop based on some simple knowledge of the road geometry. For each iteration, a bird-eye-view of the road is generated with the inverse perspective mapping technique. Then, a road marker filter associated with the well-known clothoid model are used respectively to track the road separation lanes and approximate them with mathematical functions. Finally, the algorithm computes two distinct areas between the two-road separation lanes. Its performances are tested by means of the motorcycle simulator BikeSim. This approach is very promising since it does not require any vehicle or tire model and is free of restrictive assumptions on the dynamics.

This paper presents a vision-based approach to prevent dangerous steering situations when riding a motorcycle in turns. The proposed algorithm is capable of detecting under, neutral or over-steering behavior using only a conventional camera and an inertial measurement unit. The inverse perspective mapping technique is used to reconstruct a bird-eye-view of the road image. Then, filters are applied to keep only the road markers which are, afterwards, approximated with the well-known clothoid model. This allows the prediction of the road geometry such as the curvature ahead of the motorcycle. Finally, from the predicted road curvature, the measurements of the Euler angles and the vehicle speed, the proposed algorithm is able to characterize the steering behavior. To that end, we propose to estimate the steering ratio and we introduce new pertinent indicators such as the vehicle relative position dynamics to the road. The method is validated using the advanced simulator BikeSim during a steady turn.

Coal mines pose a high safety risk for human workers. An autonomous inspection drone would enable a coal mine operation to reduce this risk by minimizing the time spent by workers inside the mine. This inspection drone must be highly robust to the harsh and dangerous environment of an underground coal mine, with high levels of coal dust and humidity that can obstruct many conventional sensing methods. For high functionality, the drone must sense and avoid potential obstacles and as well as inspect and map the mining wall face. The objective of this paper is to present ultra-wideband (UWB) radar as a robust sensing solution to this challenging environment and validate its performance experimentally in the typical coal mine environment, both statically and dynamically.

In this paper, an automatic vision-guided micromanipulation approach to facilitate versatile deployment and portable setup is proposed. This paper is motivated by the importance of micromanipulation and the limitations in existing automation technology in micromanipulation. Despite significant advancements in micromanipulation techniques, there remain bottlenecks in integrating and adopting automation for this application. An underlying reason for the gaps is the difficulty in deploying and setting up such systems. To address this, we identified two important design requirements, namely, portability and versatility of the micromanipulation platform. A self-contained vision-guided approach requiring no complicated preparation or setup is proposed. This is achieved through an uncalibrated self-initializing workflow algorithm also capable of assisted targeting. The feasibility of the solution is demonstrated on a low-cost portable microscope camera and compact actuated microstages. Results suggest subpixel accuracy in localizing the tool tip during initialization steps. The self-focus mechanism could recover intentional blurring of the tip by autonomously manipulating it 95.3% closer to the focal plane. The average error in visual servo is less than a pixel with our depth compensation mechanism showing better maintaining of similarity score in tracking. Cell detection rate in a 1637-frame video stream is 97.7% with subpixels localization uncertainty. Our work addresses the gaps in existing automation technology in the application of robotic vision-guided micromanipulation and potentially contributes to the way cell manipulation is performed.

Robotic image-guided micromanipulation contributes towards the ease of operation, speed, accuracy, and repeatability in cell manipulation. However, such technology is not fully exploited because of the challenges in the integration of robotic modules with existing microscope systems, and the difficulty in incorporating robot assistance seamlessly into the workflow. In this paper, we propose a vision-based workflow algorithm termed Detect-Focus-Track-Servo (DFTS). It facilitates easy integration of robotic modules. It also supports user interactions while minimizing the need for manual intervention and disruption to workflow through automatic detection, focusing, tracking and servoing. Experimental results suggest satisfactory detection accuracy of 99.0 % at 70 μm tolerance. The robustness test suggests no difference in the accuracy under blurred and cluttered images. The self-focus algorithm is also demonstrated to bring the tip into focus consistently. The track-servo algorithm achieves low sub-pixel uncertainty. By proposing the DFTS workflow algorithm, we hope that the level of autonomy and ease of deployment in robot and vision modules for micromanipulation can be improved so as to open up new possibilities in the development of robotic image-guided cell manipulation.

Water pipe leakage is a common and significant problem around the world. In recent years, an increasing amount of effort has been put into developing effective leak detection solutions for water pipes. Among them, the pressure gradient based method developed at Massachusetts Institute of Technology excels for its sensitivity in low pressure, small diameter pipes. It can also work in both plastic and metallic pipes carrying gas or water. However, the method was only verified in static fluid pipes, and the previous sensor designs were unable to detect leaks when there is a significant water flow in the pipe. This is undesired as the inspection can only be performed when water service is shut down. A modeling analysis shows that fluid dynamic effects in the water pipe make the original sensors dynamics too slow to react to leaks. Moreover, this leak detection method is prone to false alarms such as obstacles in the pipes, but there is a lack of studies on this topic. In this paper, we present three things: the design of a new leak sensor that is fast enough to detect leaks in dynamic fluid environments, a prototype for 52mm-inner-diameter pipeline tested in an industrial facility, and a method to differentiate leaks from false alarms supported by the test results.

In this paper, we propose a workflow algorithm for timely tracking of the tool tip during cell manipulation using a template-based approach augmented with low level feature detection. Doing so addresses the problem of adverse influences on template-based tracking during tool-cell interaction while maintaining an efficient track-servo framework. This consideration is important in developing autonomous robotic vision-guided micromanipulators. Our method facilitates vision-guided micromanipulation autonomously without manual interventions even during tool-cell interaction. This is done by decomposing the process to four scenarios that operate on their respective mode. The self-initializing mode is first used to localize and focus a region of interest (ROI) which the tip lies in. Once in focus, the tip is manipulated using a unified visual track-servo template-based approach. A reinitialization mechanism will be triggered to prevent tracking from being interrupted by partial cell occlusion of the tracking ROI. This mechanism uses the self-initializing concept combining motion cue and low-level feature detection to localize the needle tip. Following the reinitialization, we further recover tracking of the needle tip using a mechanism that updates the base template. This adaptive approach ensures uninterrupted tracking even when the cell is interacting with the tool and under deformation. Results demonstrated that with the newly incorporated mechanisms, the localized position improved from an error of more than 50% to less than 10% of the specimen size. When there is no specimen in the scene the new workflow shows no adverse effect on the localization through 270 tracked frames. By incorporating reinitialization and recovery to this workflow algorithm, we hope to initiate the first step towards uncalibrated autonomous vision-guided micromanipulation process.

Micromanipulation during live microscopic imaging relies heavily on good manual controls, dexterity, and hand-eye coordination. However, unassisted manual operations in these procedures greatly limit the speed, repeatability, and ease of operation. This is especially challenging in the case of microinjection where the insertion path needs to be in precise alignment with the imaging plane to avoid damage to cells. In this paper, we proposed an assistive robotic system that facilitates micromanipulation under microscopy. This comes in the form of intelligent robotic vision and guided manipulation. Using user-selected patch similarity, the system registers target templates and provides online coordinated depth compensation that ensures in-plane microinjection without the need for any prior calibration. This vision-based auto-registration approach readily integrates to any existing microscope system uncalibrated. It can also work as a standalone imaging solution with any general digital microscope camera. Experiments show that the similarity-score based depth compensation performed better than the uncompensated method. The method was shown to self-recover from an unfocused position. By robotizing conventional microscopy and micromanipulation procedures, we hope to address traditional latent needs and open up new possibilities in the ways experimental biology is performed.

Robotic sensor networks provide an effective approach for underground pipeline inspection. Such networks are comprised of sensor nodes (SNs) and relay nodes (RNs) carried by robots for information sensing and communication, and are able to perform accurate and realtime inspection, especially in adverse environments. SN localization is critical in such networks because localization results can be used not only for locating and pinpointing leaks, but also for maneuvering mobile SNs in a pipeline of complex configuration. However, both the underground operational environment and the limited resources of the SNs pose significant challenges for SN localization. This paper presents algorithms for SN localization in robotic sensor networks for underground pipeline inspection. Specifically, self-localization of underground in-pipe SNs were investigated by taking into account SN movement dynamics, and using the measurements of the SN's velocity and the received signal strength (RSS) of the radio signal from aboveground RNs. Depending on the availability of the RSS at the SN, different localization algorithms based on the Kalman filter are proposed for different scenarios. Simulation results show the efficacy of the proposed algorithms. The framework also provides insight into the design of robotic sensor networks for the inspection and maintenance of other types of pipeline systems, such as oil and gas pipelines.

Despite the recognized importance of demand side management (DSM) for mitigating the impact of variable energy resources and reducing the system costs, the academic and industrial literature have taken divergent approaches to DSM implementation. The prequel to this work has demonstrated that the inflation of the net load baseline forecast, used by the industrial unit commitment formulation, leads to higher and costlier day-ahead scheduling of dispatchable resources compared to the academic method. Consequently, these baseline inflation errors have to be corrected in the downstream enterprise control activities at faster time scales, increasing the control efforts and reserve requirements for the real-time market dispatch and regulation service. This paper compares the two DSM approaches and quantifies the technical impact of industrial baseline errors in subsequent layers of control using an enterprise control methodology. The adopted enterprise control simulator encompasses three interconnected layers: a resource scheduling layer composed of a security-constrained unit commitment (SCUC), a balancing layer composed of a security-constrained economic dispatch (SCED), and a regulation layer. Baseline error is absent in the social welfare model. The simulations with the industrial model are run for different baseline error levels. The baseline inflation is assumed to have the same effects in the day-ahead and real-time market. The resulting implications of baseline errors on power grid imbalances and regulating reserve requirements are tracked. It is concluded that with the same regulating service, the introduction of baseline error leads to additional system imbalance compared to the social welfare model results, and the imbalance amplifies itself as the baseline error increases. As a result, more regulating reserves are required to achieve the same satisfactory system performance with higher baseline error.

Autonomous underwater robots provide opportunities to perform missions in confined environments such as water pipe networks. They can carry sensors in these pipes and perform tasks such as mapping and inspection. Those robots must have a high level of maneuverability in order to navigate through complex networks of pipes with irregularities due to rust and calcite deposition. We propose a fully integrated, untethered robot capable of carrying sensors and maneuver into water pipe networks. The objective of this paper is to present (i) the optimal shape design and (ii) a propulsion system selection and sizing procedure for such robots. A prototype is built to demonstrate the basic elements of maneuverability, including following straight lines and making sharp turns.

In this work we present a new in-pipe leak detection robot, the MIT Leak Detector. The system performs autonomous leak detection in gas pipes in a reliable and robust fashion. Detection in based on the presence of a pressure gradient in the neighborhood of the leak. As the MIT Leak Detector travels through pipes, it picks up the pressure gradient in case of leaks via a carefully designed detector. In this work we demonstrate the performance of the system in a lab setup, which consists of 100mm ID pipes containing pressurized air.

Leakage is the most important factor for unaccounted losses in any pipe network around the world. Most state of the art leak detection systems have limited applicability, lack in reliability and depend on user experience for data extraction. This paper is about a novel system for robotic pipe integrity inspection. Unlike existing systems, detection in based on the presence of a pressure gradient in the neighborhood of a leak. This phenomenon is translated into force measurements via a specially designed and instrumented mechanical embodiment (detector). In this paper an analytic dynamic model of the robotic detector is derived and studied. A prototype is built and the main concepts are validated via experiments.

Wireless sensor networks (WSNs) provide an effective approach for underground pipeline inspection. Such WSNs comprise sensor nodes (SNs) and relay nodes (RNs) for information sensing and communication. WSNs can perform accurate and realtime inspection, especially in adverse environments. However, transmitting information between underground and aboveground nodes is very challenging. First, in-pipe SNs conducting controlled maneuvers underground are mobile. Second, SNs need to transmit the information wirelessly to aboveground base stations (BSs). In addition, radio propagation is complex because radio waves travel in a multi-medium environment. Finally, the SNs have limited energy supply. Therefore, proper deployment of a WSN is critical to providing reliable communications and efficient inspection. This paper presents a channel-aware methodology for deploying aboveground RNs in WSNs for underground pipeline inspection. Specifically, first, the paper provides a path loss model for radio propagation over multiple transmission media. Then, based on the path loss model a method is developed for optimum placement of the RNs so as to minimize the energy use of SNs and allow reliable communications. This method takes into account characteristics of the wireless channels, power consumption constraint, pipeline coverage requirement, and the limit of the number of the RNs. We provide an algorithm for optimization of RN placement and SN's power consumption. Simulation results show the efficacy of the proposed framework.

In-pipe robots are important for inspection of pipe network that form vital infrastructure of modern society. Nevertheless, most in-pipe robots developed so far are targeted at working inside gas pipes and not suitable for liquid pipes. This paper presents a new approach for designing in-pipe robot to work inside a liquid environment in the presence of high drag forces. Three major subsystems - propulsion, braking, and turning - are described in detail with new concepts and mechanisms that differ from conventional in-pipe robots. Prototypes of each subsystem are designed, built and tested for validation. Resulting is a robot design that navigates efficiently inside liquid pipe network and can be used for practical inspection purposes.

In-pipe wheeled robots require friction on the wheels to maintain traction. Ability to vary this friction is highly desirable but conventionally used linkage mechanism is not suitable for it. This paper presents a novel mechanism generating adjustable friction with minimal energy consumption for in-pipe robots. The mechanism uses permanent magnets to achieve the objective. An appropriate model for the system is also presented and discussed. The paper identifies the important design parameters, and more importantly establishes the relation between the design parameters and the system's performance. In addition, a prototype of the mechanism was designed, fabricated and tested for validation.

The maneuvering performance of a robotic device designed to mimic the swimming motions of Thunniform swimmers is presented. In contrast to existing designs, this design achieves fish like locomotion through the use of a single actuator and a compliant body and tail. Experiments were performed using both biased swimming motions and coasted turns. During these experiments the Compliant Robotic Tuna (CRT) achieved steady swimming speeds of up to 0.37 body lengths/second, average turning rates of up to 12.6 degrees per second, and turning radii as low as 1 body length. In addition, this paper compares the measured maneuvering performance with the predictions of a simplified rigid body model that was derived using both theoretical and empirical techniques. The swimming motions studied in this paper were achieved using open loop mechanisms. Therefore the potential for performance improvements exists.

The self-propelled swimming performance of two prototypes designed to mimic the kinematics of real fish swimming at high Reynolds numbers is presented. The design methodology uses structural compliance instead of discrete assemblies to achieve desired kinematics. Experiments took place at Reynolds numbers between 15×103 and 90×103. A prototype with a thick peduncle, low aspect ratio caudal fin, and uniform material distribution throughout its flexible tail, reached a maximum forward velocity of 0.11ms− 1 that coincided with a maximum thrust of 0.2N. A second prototype with a thin peduncle, high aspect ratio caudal fin, and nonuniform material distribution throughout its tail, reached a maximum forward velocity of 0.31ms− 1 that coincided with a maximum thrust close to 0.11N. Peak velocities and thrusts occured at driving frequencies between 2.7Hz and 3.5Hz for the type of kinematics used, and corresponded to Strouhal numbers close to 1.75 and 0.8 respectively. Mechanism motions were achieved in open loop, hence improvements can be made by implementing closed loop control to correct errors in desired kinematics. Finally, this study shows that simpler, more robust mechanisms, as the ones accomplished with our new design methodology, can display similar or better performance than the state of the art.

This paper develops a model and control scheme for nano-manipulation systems based on atomic force microscopes (AFM). The model includes the micro-cantilever's and piezotube actuator's coupled dynamics. An identification-based controller is proposed for piezotube scanner positioning accounting for the piezotube's nonlinear sensitivity and axes coupling. A novel robust adaptive controller is developed to compensate for large parametric uncertainties including time varying and switching parameters due to probe-surface contacts as well as time varying and impulsive forces due to contact and impact. Discussions and simulations are presented for typical nano-manipulation tasks.

A friction drive high precision positioning stage is proposed. While conventional three degrees of freedom stage systems usually utilize three different stages, the proposed system has only one movable stage for the three degrees of freedom. Therefore, this design has a simple structure and low cost. Furthermore, the stage can move omni-directionally with three degrees of freedom, namely, two linear and one rotational motions. The stage uses three special actuation systems, each of which is designed so as to generate a directional elliptical motion which allows the stage to move in a desired direction. The kinematic and dynamic models of the proposed system are presented. The bond graph modeling representation is used to offer a clear understanding of the general friction drive stage dynamic behavior. Simulation results demonstrate the feasibility of the design concept.

Topographic features on the surface of samples can be imaged by using a wide variety of profilometry methods. Scanning Probe Microscopy (SPM) comprises a group of profilometry methods that are often used for high precision, nano-scale, short range 3-D imaging. SPM can be used on a wide variety of surface types, with possible atomic resolution, relying on many physical phenomena such as inter-atomic forces, tunneling currents, capacitive and magnetic fields. The intrinsic difficulties that arise when imaging high aspect ratio surfaces-such as field emission probes, SPM probes and other "sharp and tall" micro-structures-with scanning probe profilers are mostly related to (1) image convolution, (2) sample-probe relative orientation, (3) piezo-scanner vertical range, (4) scanning-induced wear of probe and sample and (5) the location and engagement of the target feature. In this paper we discuss these practical difficulties in the imaging of high aspect ratio features and we offer some solutions.

This paper presents the design, fabrication, and evaluation of an inductive position sensor for a two dimensional linear motor. The working principle and basic components of the sensor are described. A description of the model used to simulate the dynamics and investigate the effects of various design parameters is also presented. The result is a prototype sensor with a resolution of 4 microns.

Proposes real-time position sensing devices with subnanometer precision. The precision level sought is in the range of 10/sup -9/ m to 10/sup -12/ m with long range capability on the order of 8-12 inches. These devices will use either synthetic gratings or an atomic layer of a material, such as graphite, as the reference scales in conjunction with scanning probe microscope (SPM) technologies. This paper discusses the feasibility of the proposed subnanometer position sensing concept, with preliminary experimental results.

A precision angular sensing system based on scanning tunneling microscopy (STM) is presented. The proposed system consists of three STM tips for the tracking of atoms of a reference lattice. This method will offer a real-time precision angular measurement system of pitch, roll and yaw with nano-radian resolution. The conceptual design is first described along with the computation of angles. A kinematic error analysis is also conducted for predicting the performance of the sensing system. A prototype system is described and experimental results involving atom tracking and angle measurements are presented. The proposed system achieves resolutions on the order of 1 nano-radian for all angle measurements. The strokes associated with the pitch and roll are on the order of 5/spl times/10/sup -5/ deg.

High-speed precision trimming of three-dimensional parts using laser-cutting industrial robots is addressed. A control system for direct-drive manipulators specially designed for high-speed trajectory control applications is developed. The concept of decoupled and invariant dynamics is discussed for a specific manipulator, and a simple procedure for system identification and control system design is presented. It is demonstrated that, through proper arm mechanism design, the control system can be greatly simplified and satisfactory control performance achieved. The arm mechanism design and control system are evaluated through simulations and experiments. Experimental tracking performance achieves a speed of 3 m/s and an acceleration of 3.8 g, with a joint mean tracking error of 0.0556 degrees .>

An algorithm is presented for estimating unknown system dynamics and unpredictable disturbances. Estimation is performed by convolving system input/output data with a very simple model of the plant. Dynamics are estimated directly, without estimating model parameters or measuring state derivatives. The estimation algorithm is used in the context of time delay control to provide accurate control of a multi-input, multi-output nonlinear plant in both simulations and experiments. The control action is continuous and accurate upon system startup. The simulation and experimental data show excellent control performance. Computer simulations and experimental data indicate that the proposed method for estimating unknown dynamics is a viable alternative to derivative feedback, previously required for estimation of unknown dynamics in the time delay control algorithm.>

A kinematic method is developed to analyze the workholding condition by evaluating the motion stops corresponding to the reciprocal screw motions within a given fixture configuration. More significantly, the method can be used to compare the relative quality of two or more configurations in terms of the overall kinematic constraint. Graphically based methods are then developed which can be used to synthesize a fixture layout configuration for a given 3-D workpart geometric model. A CAD system is used to demonstrate the techniques for automated fixture layout planning. The results of this work have been applied directly to a set of modular fixture elements for sheet metal workparts. For simple geometries, the fixture configurations chosen with the motion stop method agree well with the intuitive choices an engineer would make. However, for complicated geometries, these methods provide analysis and synthesis solutions which would not otherwise be possible.>

The authors first review the concept of time-delay control, then present a decentralized version. The application of such a decentralized controller to robot manipulators is then discussed. Successful experimental results demonstrate the effectiveness of the time-delay concept. The controller functions despite any system parameter uncertainty, and it is not necessary that the parameters be estimated. The practical use is verified.>

Robot manipulators and drive systems can experience instability or poor control performance after impacting with an environment. The authors present an analytical model for impact which is experimentally validated step-by-step. Extensive simulations and experiments are conducted to explain impact phenomena for the case of a force feedback control of a single-axis drive system. The experimental tests were conducted on a manipulator drive system which consists of a motor, a transmission, a link, a force sensor, and a movable environment. The results are based on an energy method and presented concisely in dimensionless form. To this end, a small number of dimensionless groups are used to characterize the impact behavior through simulations and tests. It is shown that integral force compensation with velocity feedback improves force tracking and reject impacts. It is also revealed that impact response can be tuned by selecting a favourable dimensionless ratio of force to approach velocity.>

The surface following by a robot arm is investigated in this paper. The goal is to present control strategies so that the arm can follow a surface at a given speed with desired contact force. First, a discussion on the use of direct-drive manipulators with decoupled and invariant dynamics is given. These are shown to be suitable for such force control applications. In this case, the controller design in either joint space or end effector space would involve nonlinearities due to kinematics only. This would reduce the computation burden and allows for high performance since the kinematic parameters can be estimated with better accuracies than dynamic parameters. Second, to have a better understanding of some fundamental characteristics of force control, such as the effects of actuator dynamics, environment/sensor stiffness on the performance, experiments were done using a one degree-of-freedom direct-drive arm. The use of a high gain inner velocity loop is shown to give the force control good command following and disturbance rejection characteristics. The direct-drive manipulator shows a superior performance over conventional systems as a result of fast actuator dynamics, low friction and no mechanical backlash. The experiments are conducted to investigate the force speed of response and steady state behavior, impact control and surface following performance.

A manipulator design theory for reduced dynamic complexity is presented. The kinematic structure and mass distribution of a manipulator arm are designed so that the inertia matrix in the equation of motion becomes diagonal and/or invariant for an arbitrary arm configuration. For the decoupled and invariant inertia matrix, the system can be treated as linear, single-input, single-output systems with constant parameters. As a result, the control of the manipulator arm is simplified, and, more importantly, control performance can be improved due to the reduced dynamic complexity. First, the problem of designing such an arm with the decoupled and/or configuration-invariant inertia matrix is defined. The inertia matrix is then analyzed in relation to the kinematic structure and mass properties of the arm links. Necessary conditions for the manipulator arm to possess a decoupled and/or configuration-invariant inertia matrix are obtained. Using the necessary conditions, we find the kinematic structure and mass properties for which the inertia matrix reduces to a constant, diagonal form. For 2 and 3 degree-of-freedom arms, possible arm designs for decoupled and/or invariant inertia matrices are then determined.