Successful plan generation for autonomous systems is necessary but not sufficient to guarantee reaching a goal state by an execution of a plan. Various discrepancies between an expected state and the observed state may occur during the plan execution (e.g., due to unexpected exogenous events, changes in the goals, or failure of robot parts) and these discrepancies may lead to plan failures. For that reason, autonomous systems should be equipped with execution monitoring algorithms so that they can autonomously recover from such discrepancies. We introduce a plan execution monitoring algorithm that operates under partial observability. This algorithm relies on novel formal methods for hybrid prediction, diagnosis and explanation generation, and planning. The prediction module generates an expected state after the execution of a part of the plan from an incomplete state to check for discrepancies. The diagnostic reasoning module generates meaningful hypotheses to explain failures of robot parts. Unlike the existing diagnosis methods, the previous hypotheses can be revised, based on new partial observations, increasing the accuracy of explanations as further information becomes available. The replanning module considers these explanations while computing a new plan that would avoid such failures. All these reasoning modules are hybrid in that they combine high-level logical reasoning with low-level feasibility checks based on probabilistic methods. We experimentally show that these hybrid formal reasoning modules improve the performance of plan execution monitoring.

In today's automation driven manufacturing environments, emerging technologies like cobots (collaborative robots) and augmented reality interfaces can help integrating humans into the production workflow to benefit from their adaptability and cognitive skills. In such settings, humans are expected to work with robots side by side and physically interact with them. However, the trade-off between stability and transparency is a core challenge in the presence of physical human robot interaction (pHRI). While stability is of utmost importance for safety, transparency is required for fully exploiting the precision and ability of robots in handling labor intensive tasks. In this work, we propose a new variable admittance controller based on fractional order control to handle this trade-off more effectively. We compared the performance of fractional order variable admittance controller with a classical admittance controller with fixed parameters as a baseline and an integer order variable admittance controller during a realistic drilling task. Our comparisons indicate that the proposed controller led to a more transparent interaction compared to the other controllers without sacrificing the stability. We also demonstrate a use case for an augmented reality (AR) headset which can augment human sensory capabilities for reaching a certain drilling depth otherwise not possible without changing the role of the robot as the decision maker.

Abstract For planning an assembly of a product from a given set of parts, robots necessitate certain cognitive skills: high-level planning is needed to decide the order of actuation actions, while geometric reasoning is needed to check the feasibility of these actions. For collaborative assembly tasks with humans, robots require further cognitive capabilities, such as commonsense reasoning, sensing, and communication skills, not only to cope with the uncertainty caused by incomplete knowledge about the humans’ behaviors but also to ensure safer collaborations. We propose a novel method for collaborative assembly planning under uncertainty, that utilizes hybrid conditional planning extended with commonsense reasoning and a rich set of communication actions for collaborative tasks. Our method is based on answer set programming. We show the applicability of our approach in a real-world assembly domain, where a bi-manual Baxter robot collaborates with a human teammate to assemble furniture.

Physical human-robot interaction (pHRI) integrates the benefits of human operator and a collaborative robot in tasks involving physical interaction, with the aim of increasing the task performance. However, the design of interaction controllers that achieve safe and transparent operations is challenging, mainly due to the contradicting nature of these objectives. Knowing that attaining perfect transparency is practically unachievable, controllers that allow better compromise between these objectives are desirable. In this article, we propose a multicriteria optimization framework, which jointly optimizes the stability robustness and transparency of a closed-loop pHRI system for a given interaction controller. In particular, we propose a Pareto optimization framework that allows the designer to make informed decisions by thoroughly studying the tradeoff between stability robustness and transparency. The proposed framework involves a search over the discretized controller parameter space to compute the Pareto front curve and a selection of controller parameters that yield maximum attainable transparency and stability robustness by studying this tradeoff curve. The proposed framework not only leads to the design of an optimal controller, but also enables a fair comparison among different interaction controllers. In order to demonstrate the practical use of the proposed approach, integer and fractional order admittance controllers are studied as a case study and compared both analytically and experimentally. The experimental results validate the proposed design framework and show that the achievable transparency under fractional order admittance controller is higher than that of integer order one, when both controllers are designed to ensure the same level of stability robustness.

Series elastic actuation (SEA) has become prevalent in applications involving physical human-robot interaction, as it provides considerable advantages over traditional stiff actuators in terms of stability robustness and force control fidelity. Several impedance control architectures have been proposed for SEA. Among these alternatives, the cascaded controller with an innermost velocity loop, an intermediate torque loop, and an outermost impedance loop is particularly favored for its simplicity, robustness, and performance. In this article, we derive the necessary and sufficient conditions for passively rendering null impedance and virtual springs with this cascade-controller architecture. Based on the newly established conditions, we provide nonconservative passivity design guidelines to haptically display these two impedance models, which serve as the basic building blocks of various virtual environments, while ensuring the safety of interaction. We also demonstrate the importance of including physical damping in the actuator model for deriving the passivity conditions, when integrators are utilized. In particular, we prove the unintuitive adversary effect of physical damping on the passivity of the system by noting that the damping term reduces the system $Z$-width, as well as introducing an extra passivity constraint. Finally, we experimentally validate our theoretical results using an SEA brake pedal.

We propose a force-feedback brake pedal with series elastic actuation to preserve the conventional brake pedal feel during cooperative regenerative braking. The novelty of the proposed design is due to the deliberate introduction of a compliant element between the actuator and the brake pedal whose deflections are measured to estimate interaction forces and to perform closed-loop force control. Thanks to its series elasticity, the force-feedback brake pedal can utilize robust controllers to achieve high fidelity force control, possesses favorable output impedance characteristics over the entire frequency spectrum, and can be implemented in a compact package using low-cost components. The applicability and effectiveness of the proposed series elastic brake pedal have been tested through human subject experiments that evaluate simulated cooperative regenerative braking scenarios with and without pedal feel compensation. The experimental results and responses to the accompanying questionnaire indicate that pedal feel compensation through the series elastic brake pedal can significantly decrease hard braking instances, improving safety and driver experience.

Various neuroimaging studies aim to understand the complex nature of human motor behavior. There exists a variety of experimental approaches to study neurophysiological correlates of performance during different motor tasks. As distinct from studies based on visuomotor learning, we investigate changes in electroencephalographic (EEG) activity during an actual physical motor adaptation learning experiment. Based on statistical analysis of EEG signals collected during a force-field adaptation task performed with the dominant hand, we observe a modulation of pre-movement upper alpha (10-12 Hz) and lower beta (13-16 Hz) powers over the contralateral region. This modulation is observed to be stronger in lower beta range and, through a regression analysis, is shown to be related with motor adaptation performance on a subject-specific level.

We present a systematic co-design optimization framework that enables comparison of antagonist variable stiffness actuators (VSA) with and without spring symmetry constraints. The proposed framework promotes a fair comparative analysis by maintaining design continuity among system-optimal designs of symmetric and asymmetric VSA configurations. Through a case study of a VSA-powered knee prosthesis, we not only demonstrate that co-design of robots driven by VSAs can provide important performance benefits with respect to sub-system level control-optimal designs, but also provide evidence that relaxing symmetry constraints for certain periodic tasks can result in substantial advantages in performance. Our systematic comparative analysis justifies the use of asymmetric stiffness arrangements for antagonistic VSA-powered robotic systems performing periodic tasks.

Abstract We introduce a parallel offline algorithm for computing hybrid conditional plans, called HCP-ASP, oriented towards robotics applications. HCP-ASP relies on modeling actuation actions and sensing actions in an expressive nonmonotonic language of answer set programming (ASP), and computation of the branches of a conditional plan in parallel using an ASP solver. In particular, thanks to external atoms, continuous feasibility checks (like collision checks) are embedded into formal representations of actuation actions and sensing actions in ASP; and thus each branch of a hybrid conditional plan describes a feasible execution of actions to reach their goals. Utilizing nonmonotonic constructs and nondeterministic choices, partial knowledge about states and nondeterministic effects of sensing actions can be explicitly formalized in ASP; and thus each branch of a conditional plan can be computed by an ASP solver without necessitating a conformant planner and an ordering of sensing actions in advance. We apply our method in a service robotics domain and report experimental evaluations. Furthermore, we present performance comparisons with other compilation based conditional planners on standardized benchmark domains.

We present the redundant kinematics and workspace centering control of AssistOn-Gait, an overground gait and balance trainer designed to deliver pelvis-hip exercises to correct compensatory movements arising from abnormal gait patterns. AssistOn-Gait consists of an impedance controlled pelvis-hip exoskeleton module, supported by a motion controlled holonomic mobile platform. The exoskeleton module possesses 7 active degrees of freedom to independently control the rotation of the each hip in the sagittal plane along with the pelvic tilt, pelvic rotation and the horizontal, vertical and lateral displacements of the pelvis. The holonomic mobile base can track the movements of patients on flat surfaces, allowing patients to walk naturally, start/stop motion, vary their speed, sidestep to maintain balance and turn to change their walking direction. The kinematics of AssistOn-Gait is redundant, as the exoskeleton module spans all the degrees of freedom covered by the mobile platform. The device features dual layer actuation, since the exoskeleton module is designed for force control with good transparency, while the mobile base is designed for motion control to carry the weight of the patient and the exoskeleton. The kinematically redundant dual layer actuation enables the mobile base of the system to be controlled using workspace centering control strategy without the need for any additional sensors, since the patient movements are readily measured by the exoskeleton module. The workspace centering controller ensures that the workspace limits of the exoskeleton module are not reached, decoupling the dynamics of the mobile base from the exoskeleton dynamics. Consequently, AssistOn-Gait possesses virtually unlimited workspace, while featuring the same output impedance and force rendering performance as its exoskeleton module

We present the novel kinematics, workspace characterization, functional prototype and impedance control of a six degrees of freedom haptic interface designed to train surgeons for laparoscopic procedures, through virtual reality simulations. The parallel kinematics of the device is constructed by connecting a 3RRP planar parallel mechanism to a linearly actuated modified delta mechanism with a connecting link. The configuration level forward and inverse kinematics of the device assume analytic solutions, while its workspace can be shaped to enable large end-effector translations and rotations, making it well-suited for laparoscopy operations. Furthermore, the haptic interface features a low apparent inertia with high structural stiffness, thanks to its parallel kinematics with grounded actuators. A model-based open-loop impedance controller with feed-forward gravity compensation has been implemented for the device and various virtual tissue/organ stiffness levels have been rendered.

We present GRAVITY-ASSIST, a series elastic active body weight support and inertia compensation system for use in robot assisted gait rehabilitation. The device consists of a single degree of freedom series elastic actuator that connects to the trunk of a patient. The series elastic system is novel in that, it can provide the desired level of dynamic unloading such that the patient experiences only a percentage of his/her weight and inertia. Inertia compensation is important, since the inertial forces can cause significant deviations from the desired unloading force, specially at low support forces and fast walking speeds. Furthermore, this feature enables the inertia of the harness and force sensing unit attached to the patient to be compensated for, making sure that the device does not interfere with the natural gait cycle. We present a functional prototype of the device, its characterization and experimental verification of the approach.

For reliable and fault tolerant operation of cognitive factories, we introduce an algorithm to monitor plan executions. According to this algorithm, when some changes or discrepancies are detected, appropriate decisions are given based on the causes of these changes or discrepancies. To identify these causes (e.g., broken robots or robot components), we introduce a novel diagnostic reasoning method which synergistically integrates hypothetical reasoning, geometric reasoning, and learning from earlier experiences. Based on these causes, if necessary, new hybrid plans (task plans integrated with feasibility checks) are computed to reach the manufacturing goals by allowing repairs of robots/components. The results of our experiments over reasonably-sized cognitive factory scenarios show the usefulness of (i) diagnostic reasoning for execution monitoring, (ii) allowing repair actions during replanning, and (iii) learning from experiences. We provide a video of dynamic simulation of our execution monitoring algorithm with Kuka youBots and a Nao humanoid robot as the supplementary material.

Fractional order calculus is a generalization of the familiar integer order calculus in that, it allows for differentiation/integration with orders of any real number. The use of fractional order calculus in systems and control applications provides the user an extra design variable, the order of differointegration, which can be tuned to improve the desired behavior of the overall system. We propose utilization of fractional order models/controllers in haptic systems and study the effect of fractional differentiation order on the stability robustness of the overall sampled-data system. Our results demonstrate that fractional calculus generalization has a significant impact on both the shape and area of stability region of a haptic system and inclusion of fractional order impedances may improve the stability robustness of haptic rendering. Our results also include experimental verification of the stability regions predicted by the theoretical analysis.

We consider multiple teams of heterogeneous robots, where each team is given a feasible task to complete in its workspace on its own, and where teams are allowed to transfer robots between each other. We study the problem of finding a coordination of robot transfers between teams to ensure an optimal global plan (with minimum makespan) so that all tasks can be completed as soon as possible by helping each other. We propose to solve this problem using answer set programming.

We introduce a novel method to answer natural language queries about rehabilitation robotics, over the formal ontology \rehabo. For that, 1) we design and develop a novel controlled natural language for rehabilitation robotics, called \rehabcnl; 2) we introduce translations of queries in \rehabcnl\ into \sparql\ queries, utilizing a novel concept of query description trees and description logics concepts; 3) we use an automated reasoner to find an answer to the \sparql\ query. To facilitate the use of our method by experts, we develop an intelligent, interactive query answering system, using Semantic Web technologies, and make it available on the cloud via Amazon web services. This interface guides the users to express their queries in natural language and displays the answers to queries in a readable format, possibly with links to detailed information. Easy access to information on \rehabo\ through complex queries in natural language may help engineers inspire new rehabilitation robot designs, while also guiding practitioners to make more informed decisions on technology based rehabilitation.

We introduce a novel computational method for geometric rearrangement of multiple movable objects on a cluttered surface, where objects can change locations more than once by pick and/or push actions. This method consists of four stages: (i) finding tentative collision-free final configurations for all objects (all the new objects together with all other objects in the clutter) while also trying to minimize the number of object relocations, (ii) gridization of the continuous plane for a discrete placement of the initial configurations and the tentative final configurations of objects on the cluttered surface, (iii) finding a sequence of feasible pick and push actions to achieve the final discrete placement for the objects in the clutter from their initial discrete place, while simultaneously minimizing the number of object relocations, and (iv) finding feasible final configurations for all objects according to the optimal task plan calculated in stage (iii). For (i) and (iv), we introduce algorithms that utilize local search with random restarts; for (ii), we introduce a mathematical modeling of the discretization problem and use the state-of-the-art ASP reasoners to solve it; for (iii) we introduce a formal hybrid reasoning framework that allows embedding of geometric reasoning in task planning, and use the expressive formalisms and reasoners of ASP. We illustrate the usefulness of our integrated AI approach with several scenarios that cannot be solved by the existing approaches. We also provide a dynamic simulation for one of the scenarios, as supplementary material.

We consider cognitive factories with multiple teams of heterogenous robots, and address two key challenges of these domains, hybrid reasoning for each team and finding an optimal global plan (with minimum makespan) for multiple teams. For hybrid reasoning, we propose (i) modeling each team's workspace taking into account capabilities of heterogeneous robots, (ii) embedding continuous external computations into discrete symbolic representation and reasoning by combining different methods of integration, (iii) not only optimizing the makespans of local plans but also minimizing the total cost of robotic actions, where costs of actions can be defined in various ways. To find a global plan with minimum makespan, we propose a semi-distributed approach: we formulate the problem of finding an optimal coordination of teams that can help each other, prove its intractability, and describe how to solve this problem using existing automated reasoners. As a case study, we show applications of our hybrid reasoning and coordination approaches on a cognitive toy factory with dynamic simulations and physical implementation utilizing KuKa youBots and Lego NXT robots (supplementary video provided). We also present experimental results to discuss the scalability of these methods.

We present the design and control of series elastic holonomic mobile platform, ASSISTON-MOBILE, aimed to administer therapeutic table-top exercises to patients who have suffered injuries that affect the function of their upper extremities. The proposed mobile platform is a low-cost, portable, easy-to-use rehabilitation device for home use. ASSISTON-MOBILE consists of four actuated Mecanum wheels and a compliant, low-cost, multi degree-of-freedom Series Elastic Element as its force sensing unit. Thanks to its series elastic actuation, ASSISTON-MOBILE is highly back-driveable and can provide assistance/resistance to patients, while performing omni-directional movements on plane. Feasibility tests and preliminary usability studies with the robot are presented. The device holds promise in improving accuracy and effectiveness of repetitive movement therapies completed at home, while also providing quantitative measures of patient progress.

The Tower of Hanoi puzzle, has recently been established as a robotics challenge as a part of EU Robotics coordination action in 2011 and IEEE IROS Conference in 2012. It provides a good standardized test bed to evaluate integration of high-level reasoning capabilities of robots together with their manipulation and perception aspects.We address this challenge within a general planning and monitoring framework: we represent the puzzle in a logic-based formalism, integrate task planning and motion planning, solve this hybrid planning problem with a state-of-the-art automated reasoner (e.g., a SAT solver), execute the computed plans under feedback control while also monitoring for failures, and recover from failures as required. We show the applicability of this framework by implementing it using two robotic manipulators on a physical experimental setup.

We present kinematics, actuation, detailed design, characterization results and initial user evaluations of AssistOn-Knee, a novel self-aligning active exoskeleton for robot-assisted knee rehabilitation. AssistOn-Knee can, not only assist flexion/extension movements of the knee joint but also accommodate its translational movements in the sagittal plane. Automatically aligning its joint axes, AssistOn-Knee enables an ideal match between human knee axis and the exoskeleton axis, guaranteeing ergonomy and comfort throughout the therapy. Self-aligning feature significantly shortens the setup time required to attach the patient to the exoskeleton, allowing more effective time spent on exercises. The proposed exoskeleton actively controls the rotational degree of freedom of the knee through a Bowden cable-driven series elastic actuator, while the translational movements of the knee joints are passively accommodated through use of a 3 degrees of freedom planar parallel mechanism. AssistOn-Knee possesses a lightweight and compact design with significantly low apparent inertia, thanks to its Bowden cable based transmission that allows remote location of the actuator and reduction unit. Furthermore, thanks to its series-elastic actuation, AssistOn-Knee enables high-fidelity force control and active backdrive-ability within its control bandwidth, while featuring passive elasticity for excitations above this bandwidth, ensuring safety and robustness throughout the whole frequency spectrum.

We present variable negative stiffness actuation (VnSA), an alternative method of achieving variable stiffness actuation based on the nonlinear deflection characteristics of buckling beams. The approach exploits transverse stiffness variations of axially loaded beams around their critical buckling load to achieve an actuator with adjustable stiffness. In particular, transverse stiffness of buckled beams are positive under tensile loading and for compressive loading below their first critical buckling load, while they display negative stiffness above this critical value. Furthermore, for small deflections transverse stiffness of buckled beams depends linearly on the amount of axial loading. Consequently, the stiffness of a variable stiffness actuator can be modulated (i) by decreasing the transverse stiffness through an increase of the axial compressive loading on a beam, up to values above the first critical buckling load where the overall stiffness of the actuator approaches its lowest negative value, and (ii) by increasing the transverse stiffness through application of tensile axial loading. Capitalizing on the concept of negative stiffness, the lowest stiffness of VnSA can be set arbitrarily close to zero or even to negative values (when counterbalanced), while very high stiffness values are also achievable by tensile loading of the beam. As a result, VnSA can modulate its stiffness over a uniquely large range that includes zero and negative stiffness values. Furthermore, thanks to the negative stiffness characteristics, the stiffness of VnSA can be kept very low without sacrificing the mechanical integrity and load bearing capacity of the actuator. We introduce the design of VnSA, theoretically analyze its stiffness modulation response, and provide implementation details of a prototype. We also provide experimental results detailing range of stiffness modulation and force tracking performance achieved with this prototype and discuss its correspondence with the theory.

Abstract We study the problem of finding optimal plans for multiple teams of robots through a mediator, where each team is given a task to complete in its workspace on its own and where teams are allowed to transfer robots between each other, subject to the following constraints: 1) teams (and the mediator) do not know about each other's workspace or tasks (e.g., for privacy purposes); 2) every team can lend or borrow robots, but not both (e.g., transportation/calibration of robots between/for different workspaces is usually costly). We present a mathematical definition of this problem and analyze its computational complexity. We introduce a novel, logic-based method to solve this problem, utilizing action languages and answer set programming for representation, and the state-of-the-art ASP solvers for reasoning. We show the applicability and usefulness of our approach by experiments on various scenarios of responsive and energy-efficient cognitive factories.

We propose the use of causality-based formal representation and automated reasoning methods from artificial intelligence to endow multiple teams of robots in a factory, with high-level cognitive capabilities, such as, optimal planning and diagnostic reasoning. In particular, we introduce algorithms for finding optimal decoupled plans and diagnosing the cause of a failure/discrepancy (e.g., robots may get broken or tasks may get reassigned to teams). We discuss how these algorithms can be embedded in an execution and monitoring framework effectively by allowing reusability of computed plans in case of failures, and show the applicability of these algorithms on an intelligent factory scenario.

We present AssistOn-SE:, a novel powered exoskeleton for robot-assisted rehabilitation that allows for movements of the shoulder girdle as well as shoulder rotations. Automatically adjusting its joint axes, AssistOn-SE: can enable a perfect match between human joint axes and the device axes, not only guaranteeing ergonomy and comfort throughout the therapy, but also extending the usable range of motion for the shoulder joint. Moreover, the adjustability feature significantly shortens the setup time required to attach the patient to the exoskeleton, allowing more effective time be spend on exercises instead of wasting this valuable resource for adjustments. Back-driveable design of AssistOn-SE: supports both passive translational movements of the center of glenohumeral joint and independent active control of these degrees of freedom. Thanks to this property, glenohumeral mobilization and scapular stabilization exercises can also be delivered with AssistOn-SE:, extending the type of therapies that can be administered using upper-arm exoskeletons. We introduce the design of the exoskeleton and present the kinematic analysis of its self-aligning joint. We also provide implementation details for an early prototype as well as some experimental results detailing range of motion of the device and its ability to track movements of the shoulder girdle.

Passive velocity field control is advantageous to deliver human-in-the-loop contour tracking rehabilitation exercises, since patients can be allowed to proceed with their preferred pace, while assistance can still be provided as determined by the therapist with ensured coupled stability. We introduce a framework based on passive velocity field control for robot assisted rehabilitation that includes prevention mechanisms against undesired slacking behavior of patients. This framework not only provides systematic approaches to prevent slacking, but also can do so while ensuring coupled stability of the overall robot patient system, a property that cannot be assured with any of the other ad-hoc slacking prevention methods. In particular, the proposed approach enables seamless on-line modification of the task difficulty, speed of contour following, and the level of assistance, while preserving passivity of the system with respect to external forces. The proposed slacking prevention schemes encourage active participation of the patients in rehabilitation protocols with even increased number of repetitions and thanks to flexibility introduced by the controller, render delivery of “repetitive tasks without repeating the same task” possible. Experiments with an haptic interface are included to demonstrate the passivity of the proposed control framework and preliminary human subject experiments with healthy volunteers are presented to validate feasibility and usability of the proposed approaches.

We explore the effects of parameters constituting a second order dynamic system on the rate of human motor adaptation while performing a rhythmic dynamic task. In our experiments, participants excite virtual second-order systems at resonance via a haptic interface. After overtraining subjects with a nominal system, we unexpectedly change the system parameters and study the resulting motor adaptation in catch trials. Through four experiment seatings, we demonstrate the effects of dynamic system parameters on human motor adaptation. Results indicate that gain and damping parameters significantly affect the rate of adaptation. In particular, as the effort required to complete the task increases, the rate of adaptation decreases, indicating a trade-off between task performance and the effort required to perform the task.

A new approach to online generation of velocity fields for parametric curves is presented for implementation in passive velocity field controllers (PVFC) that enable human-in-the-loop contour following tasks. In particular, a feedback stabilized closest point tracking algorithm is utilized for real-time determination of the contour error and online construction of the velocity field. The algorithm augments the system dynamics with a new state, and implements a uniformly asymptotically stable controller to update this new state to continual track the closest point to the robot end-effector. Thanks to its feedback-stabilized core, the algorithm is immune to initialization errors, and robust against drift and numerical noise. Furthermore, requiring simple evaluations of the curve and its unit tangents, the approach is computationally efficient. Applicability and effectiveness of the approach to implement passive contour following tasks have been demonstrated through simulations and experiments with a two degrees-of-freedom haptic interface.

We present a direct model-based friction rendering algorithm for continuous parametric surfaces. Unlike the existing approaches, friction rendering does not rely on the algorithms introduced for polyhedral surfaces. Our algorithm implements the stiction model of friction for haptic rendering by utilizing a feedback-stabilized closest point tracking algorithm developed for parametric surfaces. Therefore, our friction rendering algorithm is inherently stable and it can handle surfaces with high curvature. Furthermore, it allows for transitions from sticking to sliding and sliding to sticking, as well as surface to surface transitions, without introducing discontinuous force artifacts. Our algorithm allows for tuning of the friction coefficient during the mode transitions to enable rendering of the Stribeck effect. Thanks to its feedback-stabilized core, it is robust against drift and numerical noise. The algorithm is computationally efficient; its applicability and effectiveness to simulate friction is verified through real-time implementations.

We present a formal framework that combines high-level representation and causality-based reasoning with low-level geometric reasoning and motion planning. The frame-work features bilateral interaction between task and motion planning, and embeds geometric reasoning in causal reasoning, thanks to several advantages inherited from its underlying components. In particular, our choice of using a causality-based high-level formalism for describing action domains allows us to represent ramifications and state/transition constraints, and embed in such formal domain descriptions externally defined functions implemented in some programming language (e.g., C++). Moreover, given such a domain description, the causal reasoner based on this formalism (i.e., the Causal Calculator) allows us to compute optimal solutions (e.g., shortest plans) for elaborate planning/prediction problems with temporal constraints. Utilizing these features of high-level representation and reasoning, we can combine causal reasoning, motion planning and geometric planning to find feasible kinematic solutions to task-level problems. In our framework, the causal reasoner guides the motion planner by finding an optimal task-plan; if there is no feasible kinematic solution for that task-plan then the motion planner guides the causal reasoner by modifying the planning problem with new temporal constraints. Furthermore, while computing a task-plan, the causal reasoner takes into account geometric models and kinematic relations by means of external predicates implemented for geometric reasoning (e.g., to check some collisions); in that sense the geometric reasoner guides the causal reasoner to find feasible kinematic solutions. We illustrate an application of this framework to robotic manipulation, with two pantograph robots on a complex assembly task that requires concurrent execution of actions. A short video of this application accompanies the paper.

In this study, we present a novel active device for robot-assisted rehabilitation that accommodates transitional movements of the knee joint as well as its rotation, enabling a perfect match between human joint axes and the device axes. Automatically adjusting its joint axes, the proposed device is not only capable of guaranteeing ergonomy and comfort throughout the therapy, but also extends the usable range of motion for the knee joint. Moreover, the adjustability feature significantly shortens the setup time required to attach the patient to the exoskeleton, allowing more effective time be spend on exercises instead of wasting it for adjustments. The proposed system is different from the similar works in literature in that it supports both passive translational movements of the knee joint and independent active control of these degrees of freedom. In particular, we introduce implementation details of a prototype that features compact design and combines the power of three actuators to achieve high rotational torques, detail the model based impedance controller utilized to adjust interaction forces and present the experimental characterization of the exoskeleton.

Virtual reality with haptic feedback provides a safe and versatile practice medium for many manual control tasks. Haptic guidance has been shown to improve performance of manual control tasks in virtual environments; however, the efficacy of haptic guidance for training in virtual environments has not been studied conclusively. This article presents experimental results that show negative efficacy of haptic guidance during training in virtual environments. The haptic guidance in this study is a fixed-gain error-reducing shared controller, with the control effort overlaid on the dynamics of the manual control task during training. Performance of the target-hitting manual control task in the absence of guidance is compared for three training protocols. One protocol contained no haptic guidance and represented virtual practice. Two protocols utilized haptic guidance, varying the duration of exposure to guidance during the training sessions. Exposure to the fixed-gain error-reducing shared controller had a detrimental effect on performance of the target-hitting task at the conclusion of a month-long training protocol, regardless of duration of exposure. While the shared controller was designed with knowledge of the task and an intuitive sense of the motions required to achieve good performance, the results indicate that the acquisition of motor skill is a complex phenomenon that is not aided with haptic guidance during training as implemented in this experiment.

This article provides in-depth knowledge about our undergoing effort to develop an open architecture micromanipulation system with force sensing capabilities. The major requirement to perform any micromanipulation task effectively is to ensure the controlled motion of actuators within nanometer accuracy with low overshoot even under the influence of disturbances. Moreover, to achieve high dexterity in manipulation, control of the interaction forces is required. In micromanipulation, control of interaction forces necessitates force sensing in milli-Newton range with nano-Newton resolution . In this paper, we present a position controller based on a discrete time sliding mode control architecture along with a disturbance observer. Experimental verifications for this controller are demonstrated for 100, 50 and 10 nanometer step inputs applied to PZT stages. Our results indicate that position tracking accuracies up to 10 nanometers, without any overshoot and low steady state error are achievable. Furthermore, the paper includes experimental verification of force sensing within nano-Newton resolution using a piezoresistive cantilever end- effector. Experimental results are compared to the theoretical estimates of the change in attractive forces as a function of decreasing distance and of the pull off force between a silicon tip and a glass surface, respectively. Good agreement among the experimental data and the theoretical estimates has been demonstrated.

A new minimum-distance tracking algorithm is presented for moving convex bodies represented using tiled-together parametric surface patches. The algorithm is formulated by differentiating the geometric minimization problem with respect to time. This produces a hybrid dynamical system that incorporates dependence on rigid body motion, surface shape, and surface boundary interconnectedness. The minimum distance between a pair of previously identified closest features is found by feedback stabilizing the dynamical equations and numerically solving the resulting closed-loop system equations. Maintenance of the minimum distance and the associated closest points during motion is achieved through the action of a feedforward controller and a switching algorithm. The feedforward controller simultaneously accounts for surface shape and motion while the switching controller triggers updates to the extremal feature pair when extremal points on one body cross between Voronoi regions of the other body. In contrast to previously available minimum distance determination algorithms, attractive properties of the new algorithm include a means of determining the highest gain K that maintains stability under a given discretization scheme and a large and easily characterized basin of attraction of the stabilized closest points. These properties may be used to achieve higher computational efficiency. Simulation results are presented for various planar and spatial systems composed of a body and point or composed of two bodies.

A new extremal distance tracking algorithm is presented for parametric curves and surfaces undergoing rigid body motion. The essentially geometric extremization problem is transformed into a dynamical control problem by differentiating with respect to time. Extremization is then solved with the design of a stabilizing controller. We use a feedback linearizing controller. The controller simultaneously accounts for the surface shape and motion while asymptotically achieving (and maintaining) the extremal pair. Thus collision detection takes place in a framework fully analogous to the framework used for the simulation of dynamical response.