ICRA 2017 Papers

Fiber-Reinforced Elastofluidic Enclosures (FREEs), are a subset of pneumatic soft robots with an asymmetric continuously deformable skin that are able to generate a wide range of deformations and forces, including rotation and screw motions. Though these soft robots are able to generate a variety of motions, simultaneously controlling their end effector rotation and position has remained challenging due to the lack of a simple model. This paper presents a model that establishes a relationship between the pressure, torque due to axial loading, and axial rotation to enable a model-driven open-loop control for FREEs. The modeling technique relies on describing force equilibrium between the fiber, fluid, and an elastomer model which is computed via system identification. The model is experimentally tested as these variables are changed, illustrating that it provides good agreement with the real system. To further illustrate the potential of the model, a precision open-loop control experiment of opening a rotational combination lock is presented2.

Recent work has shown the potential for lights to act as simple, yet expressive signaling mechanisms for use in a variety of human-robot applications. However, the wide range of robot shapes and sizes makes it difficult for researchers to quickly prototype and evaluate different light configurations and signal designs. In this work, we present the design of ModLight, a modular research tool consisting of a set of low cost light blocks that can be easily reconfigured to fit a myriad of robots and applications. ModLight also provides researchers, designers, and students with open-source software tools that enable them to visually design new signals and easily integrate them into existing systems. This work also serves to motivate the need for further research in developing light behaviors for use in human-robot interaction. Towards this goal, we present our design rationale including a brief analysis of the signaling needs of several robots and applications.

Sensor networks have limited capabilities to model complex phenomena occuring between sensing nodes. Mobile robots can be used to close this gap and learn local interpolation models. In this paper, we utilize Echo State Networks in order to learn the calibration and interpolation model between sensor nodes using measurements collected by a mobile robot. The use of Echo State Networks allows to deal with temporal dependencies implicitly, while the spatial mapping with a Gaussian Process estimator exploits the fact that Echo State Networks learn linear combinations of complex temporal dynamics. The resulting Echo State Map elegantly combines spatial and temporal cues into a single representation. We showcase the method in the exposure modeling task of building dust distribution maps for foundries, a challenge which is of great interest to occupational health researchers. Results from simulated data and real world experiments highlight the potential of Echo State Maps. While we focus on particulate matter measurements, the method can be applied for any other environmental variables like temperature or gas concentration.

This paper introduces a novel robot-based approach to the stroke rehabilitation scenario, in which a mobile robot companion accompanies stroke patients during their walking self-training. This assistance enables them to move freely in the clinic practicing both their mobility and spatial orientation skills. Based on a set of questions for systematic evaluating the autonomy and practicability of assistive robots and a three-stage approach in conducting function and user tests in the clinical setting, we present the results of user trials performed with N=30 stroke patients in a stroke rehabilitation center between 4/2015 and 3/2016. This allowed us to make an honest inventory of the strengths and weaknesses of the developed robot companion and its already achieved practicability for clinical use. The results of the user studies show that patients and fellow patients were very open-minded and accepted the robotic coach. The robot motivated them for independent training and leaving their room, despite severe consequences of stroke (lower limbs paralysis, speech/language problems, loss of orientation, depression), provided a very self-determined training regime, and encouraged them to expand the radius of their training in the clinic.

Microrobots are considered as potential candidates for targeted delivery of cargos, drugs and even energy with high precision. One interesting phenomenon is their collective behaviour actuated by dynamic fields, which is yet to be adequately studied. Herein, we report a novel method of using millions of magnetic nanoparticles to generate a dynamic-equilibrium particle-based vortex, which can manipulate multiple cargos simultaneously at the microscale. The governing physical laws of the generation of a particle-based vortex are explained and the experimental results are presented. The high effectiveness of this micro-vortex-based method of particle gathering is testified. Moreover, the vortex can be navigated near a solid surface in a controlled manner. The velocity and morphology of the mobile vortices with different pitch angles are investigated, showing that the vortex moving with small pitch angles is capable of maintaining the original shape and coverage area. Collecting and transporting multiple polystyrene (PS) microbeads into a channel using the vortex are also demonstrated. This method allows us to perform micromanipulation using the collective behaviour of nanoparticles and to develop new strategies for the formation and control of the microrobotic swarm.

We present a novel algorithm for an autonomous underwater robot to visually detect and follow its companion human diver. Using both spatial-domain and frequency-domain features pertaining to human swimming patterns, we devise an algorithm to visually detect the position and swimming direction of the diver. Our algorithm is unique in the way that it allows detection of arbitrary motion directions, in addition to keeping track of a diver's position through the image sequence over time. A Hidden Markov Model (HMM)-based approach prunes the search-space of all potential trajectories relying on image intensities in the spatial-domain. A diver's motion signature is subsequently detected in a sequence of non-overlapping image subwindows exhibiting human swimming patterns. The pruning step ensures efficient computation by avoiding exponentially large search-spaces, whereas the frequency-domain detection allows us to detect the diver's position and motion direction accurately. Experimental validation of the proposed approach is presented on datasets collected from open-water and closed-water environments.

This work focuses on integrated routing and motion planning for an autonomous vehicle in a road network. We consider a problem in which customer demands need to be met within desired deadlines, and the rules of the road need to be satisfied. The vehicle might not, however, be able to satisfy these two goals at the same time. We propose a systematic way to compromise between delaying the satisfaction of the given demand and violating the road rules. We utilize scLTL formulas to specify desired behavior and develop a receding horizon approach including a periodically interacting routing algorithm and a RRT*-based motion planner. The proposed solution yields a provably minimum-violation trajectory. An illustrative case study is included.

Recognition of sequential human activities, such as “sitting down” and “standing up”, is a common but challenging problem in human-robot interaction, which requires modeling their underlying temporal patterns. Although previous sequence modeling methods, such as Hidden Conditional Random Fields (HCRFs), demonstrated satisfactory recognition accuracy, they do not explicitly model the uncertainty in underlying temporal patterns, which can provide valuable information to characterize sequential activities. To address this problem, we introduce a novel Minimum Uncertainty HCRF (MU, or μHCRF). Different from traditional HCRF-based techniques that only utilize the negative log-likelihood of the categories' conditional probability as the loss function, the proposed μ-HCRF also introduces a regularization term to model the underlying temporal pattern of the latent variables. As another theoretical contribution, we provide a derivation to show that the formulated problem has a closed-form solution, and prove that inference of the proposed μHCRF is tractable. Extensive empirical study is performed to evaluate our approach, using four public benchmark datasets. Experimental results have shown that our μHCRFs outperform previous techniques and achieve state-of-the-art performance on human activity recognition, especially on sequential activities.

In this paper we introduce a design framework that permits task-specific complex geometries in robotic limbs with the minimal power consumption. Additionally we present a optimal gear ratio selection algorithm with realistic constraints, which we use as a subroutine within the geometry optimization. As a case study we optimize the a spatial, hybrid parallel-serial robotic limb structure with a large set of geometric parameters. Optimal design with respect to this mechanism produces three locally optimal families of designs. These are analyzed rigorously and a best design was chosen. A prototype has been constructed from the chosen design family, proving that the approach is practical. This serves as evidence that the design optimization method is an effective tool to minimize the electrical cost of a given task, and thus specialize the design.

Compliant actuator is widely accepted for physical human-robot interaction due to its safety aspect, dynamic performance improvements and energy saving abilities. In this paper, based on the variable ratio lever mechanism, a new kind of Serial Variable Stiffness Actuator (SVSA) is proposed by using an Archimedean Spiral Relocation Mechanism (ASRM) to change the position of the pivot, implementing large range of adjustable stiffness. The ASRM introduced here makes the SVSA design has continuous stiffness adjustment ability and simply mechanical structure. Within the Variable Stiffness Mechanism (VSM), two linear springs are assembled antagonistically on a spring shaft. Their displacements are perpendicular to the output link to transmit the spring force more efficiently. Stiffness modeling and analysis of the SVSA are carried out to cover large deflection angle. The physical implementation of the SVSA shows that the output stiffness of the VSM is changed from 1.72 to 150.56 Nm/rad using a linear spring with stiffness 1882 N/m, working range covered from 0 to 360°. Control experiments also proved the wide range of stiffness adjustment ability of the SVSA.

Motion planning for multi-target autonomous search requires efficiently gathering as much information over an area as possible with an imperfect sensor. In disaster scenarios and contested environments the spatial connectivity may unexpectedly change (due to aftershock, avalanche, flood, building collapse, adversary movements, etc.) and the flight envelope may evolve as a known function of time to ensure rescue worker safety or to facilitate other mission goals. Algorithms designed to handle both expected and unexpected changes must: (1) reason over a sufficiently long time horizon to respect expected changes, and (2) replan quickly in response to unexpected changes. These ambitions are hindered by the submodularity property of mutual information, which makes optimal solutions NP-hard to compute. We present an algorithm for autonomous search in changing environments that uses a variety of techniques to improve both the speed and time horizon, including using e-admissible heuristics to speed up the search.

A variety of end-user devices involving keypoint-based mapping systems are about to hit the market e.g. as part of smartphones, cars, robotic platforms, or virtual and augmented reality applications. Thus, the generated map data requires automated evaluation procedures that do not require experienced personnel or ground truth knowledge of the underlying environment. A particularly important question enabling commercial applications is whether a given map is of sufficient quality for localization. This paper proposes a framework for predicting localization performance in the context of visual landmark-based mapping. Specifically, we propose an algorithm for predicting performance of vision-based localization systems from different poses within the map. To achieve this, a metric is defined that assigns a score to a given query pose based on the underlying map structure. The algorithm is evaluated on two challenging datasets involving indoor data generated using a handheld device and outdoor data from an autonomous fixed-wing unmanned aerial vehicle (UAV). Using these, we are able to show that the score provided by our method is highly correlated to the true localization performance. Furthermore, we demonstrate how the predicted map quality can be used within a belief based path planning framework in order to provide reliable trajectories through high-quality areas of the map.

This paper deals with manipulation planning. First, we propose a new tool called the constraint graph to describe the various motion constraints relative to a manipulation planning problem. Then, we describe a problem arising for some manipulation planning problems called the crossed foliation issue. We propose a extension of RRT algorithm that explores the leaves of the foliations generated by motion constraints and that solves the crossed foliation problem. Finally, we show a wide variety of problem that our approach can solve.

Mutual subspace method (MSM), which is one of image-based approaches, showed strong discrimination capability in gait recognition. In general, 2D image matrices are transformed into 1D image vectors to be used as input into MSM, and then principal component analysis (PCA) is applied to 1D vectors to generate a subspace. However, due to the high dimensionalities of 1D vectors, the evaluation accuracy of the covariance matrix in PCA is not high enough. This results in a decrease in performance, especially in case that speed difference between gallery and probe dataset is big. Thus in this paper we propose a method, which expands the MSM-based method, to recognize people with higher accuracy. The proposed method divides the human body area into multiple areas, followed by adaptive choice of areas that have high discrimination capability. Moreover, the proposed method utilizes the frieze pattern, which is one of gait features, as an additional input into MSM. The use of divided areas and the frieze pattern allows us to evaluate the covariance matrix with higher accuracy. In experiments we applied the proposed method to challenging databases with speed variations, and we show the effectiveness of the proposed method.

This paper presents a magnetically actuated soft capsule endoscope for fine-needle aspiration biopsy (B-MASCE) in the upper gastrointestinal tract. A thin and hollow needle is attached to the capsule, which can penetrate deeply into tissues to obtain subsurface biopsy sample. The design utilizes a soft elastomer body as a compliant mechanism to guide the needle. An internal permanent magnet provides a means for both actuation and tracking. The capsule is designed to roll towards its target and then deploy the biopsy needle in a precise location selected as the target area. B-MASCE is controlled by multiple custom-designed electromagnets while its position and orientation are tracked by a magnetic sensor array. In in vitro trials, B-MASCE demonstrated rolling locomotion and biopsy of a swine tissue model positioned inside an anatomical human stomach model. It was confirmed after the experiment that a tissue sample was retained inside the needle.

Soft miniaturized untethered grippers can be used to manipulate and transport biological material in unstructured and tortuous environments. Previous studies on control of soft miniaturized grippers employed cameras and optical images as a feedback modality. However, the use of cameras might be unsuitable for localizing miniaturized agents that navigate within the human body. In this paper, we demonstrate the wireless magnetic motion control and planning of soft untethered grippers using feedback extracted from B-mode ultrasound images. Results show that our system employing ultrasound images can be used to control the miniaturized grippers with an average tracking error of 0.4±0.13 mm without payload and 0.36±0.05 mm when the agent performs a transportation task with a payload. The proposed ultrasound feedback magnetic control system demonstrates the ability to control miniaturized grippers in situations where visual feedback cannot be provided via cameras.

Scanning lasers increase the quality of the laser microsurgery enabling fast tissue ablation with less thermal damage. However, the possibility to perform scanning laser microsurgery in confined workspaces is restricted by the large size of currently available actuators, which are typically located outside the patient and require direct line-of-sight to the microsurgical area. Here, a magnetic scanner tool is designed to allow endoscopic scanning laser microsurgery. The tool consists of two miniature electromagnetic coil pairs and permanent magnets attached to a flexible optical fiber. The actuation mechanism is based on the interaction between the electromagnetic field and the permanent magnets. Controlled and high-speed laser scanning is achieved by bending of the optical fiber with magnetic torque. Results demonstrate the achievement of a 3×3 mm2 scanning range within the laser spot is controlled with 35μm precision. The system is also capable of automatically executing high-speed laser scanning operations over customized trajectories with a root-mean-squared-error (RMSE) in the order of 75μm. Furthermore, it can be teleoperated in real-time using any appropriate user interface device. This new technology enables laser scanning in narrow and difficult to reach workspaces, promising to bring the benefits of scanning laser microsurgery to laparoscopic or even flexible endoscopic procedures. In addition, the same technology can be potentially used for optical fiber based imaging, enabling for example the creation of new family of scanning endoscopic OCT or hyperspectral probes.

This paper presents the design of a single use disposable compliant surgical tooltip for a tele-operated surgical robot. The proposed design aims at mitigating bio-fouling of surgical tools. By implementing a monolithic design for surgical tooltip, the cleaning and sterilization processes needed after every surgery for traditional surgical robotic tools is considerably simplified. The proposed design has 3 degrees of freedom (DOF) with a modified serpentine joint for enhanced buckling strength and off-axis stiffness. A magnetic force based coupling is proposed as a means of transferring power through a hermetic barrier to maneuver the tooltip. The tooltip is thus coupled through a hermetically sealed tool shaft preventing biological material from entering the tool shaft. By having a pluggable tooltip, modularity is also achieved by interchangeable tooltip instead of replacing the whole tool during surgery. A prototype of the proposed design is developed and its functional performance is validated. Owing to the magnetic nexus which forms the magnetic coupling, the tool is named MagNex.

In this paper, we present an electromyography (EMG)-driven assistive hand exoskeleton for spinal-cord-injury (SCI) patients. We developed an active assistive orthosis, called Maestro, which is light, comfortable, compliant, and capable of providing various hand poses. The EMG signals are obtained from a subject's forearm, post-processed, and classified for operating Maestro. The performance of Maestro is evaluated by a standardized hand function test, called the Sollerman hand function test. The experimental results show that Maestro improved the hand function of the SCI patients.