IROS 2018 论文列表

Emerging applications of robotic manipulation of deformable objects have opened up new challenges in robot control. While several control techniques have been developed to manipulate deformable objects, the performance of existing methods is commonly limited by two issues: 1) implicit assumption that the physical contact between the end-effector and the object is always maintained, and 2) requirements of exact parameters of deformation model, which are difficult to obtain. This paper presents a new control scheme for robotic manipulation of deformable objects, which allows the robot to automatically contact then actively deform the deformable object by assessing the status of deformation in real time. Instead of designing multiple controllers and switching among them, the proposed method smoothly and stably integrates two control phases (i.e. region reaching and active deforming) into a single controller. The stability of the closed-loop system is rigorously proved with the consideration of the uncertain deformation model and uncalibrated cameras. Hence, the proposed control scheme enhances the autonomous capability of active deformable object manipulation. Experimental studies are conducted with different initial conditions to demonstrate the performance of the proposed controller.

In this tutorial, we provide principled methods to quantitatively evaluate the quality of an estimated trajectory from visual(-inertial) odometry (VO/VIO), which is the foundation of benchmarking the accuracy of different algorithms. First, we show how to determine the transformation type to use in trajectory alignment based on the specific sensing modality (i.e., monocular, stereo and visual-inertial). Second, we describe commonly used error metrics (i.e., the absolute trajectory error and the relative error) and their strengths and weaknesses. To make the methodology presented for VO/VIO applicable to other setups, we also generalize our formulation to any given sensing modality. To facilitate the reproducibility of related research, we publicly release our implementation of the methods described in this tutorial.

Robots deployed in large human-populated indoor environments such as shopping malls, airports etc., inadvertently communicate via wireless networks for enhanced perception and decision making capabilities. Owing to highly dynamic signal attenuation characteristics in such environments, connectivity issues may arise during robotic navigation, leading to disruption in information flow causing potential danger. Exact modeling of signal propagation for estimating spatial signal variation is usually challenging. Moreover, the presence of dynamic humans also add a layer of temporal signal variation complexities. Thus, this paper introduces a generative approach for embedding radio signal strength constraints within networked service/social robot navigation in large human-populated environments. Initially, we propose a Gaussian Process based online spatio-temporal signal strength prediction model that, as opposed to the current state of the art, also aims to take into account the temporal fading arising due to the presence of human crowds. We then devise a transient-goal driven navigation strategy to realize a sub-optimal path towards a goal, that is aimed at resolving both communication-aware and human-aware planning constraints. Evaluations of the proposed signal prediction model demonstrate the advantages of our approach with respect to the current state of the art. The efficacy of the navigation strategy in also demonstrated simulations and using hardware experiments conducted on a robotic wheelchair operating in a large shopping mall.

Many applications of robotic motion planning benefit from considering multiple homotopically distinct paths rather than a single path from start to goal. However, determining whether paths represent different homotopy classes can be difficult to compute. We propose metrics for efficiently approximating the homotopic similarity of two paths are, instead of verifying homotopy equivalence directly. We propose two metrics: (1) a naive application of local planning, a common subroutine of sampling-based motion planning, and (2) a novel approach that reasons about the topologically distinct portions of the workspace that a path visits. We present three applications of our metric to demonstrate its use and effectiveness: extracting topologically distinct paths from an existing roadmap, comparing paths for robot manipulators, and improving the computational efficiency of an existing sampling-based method, Path Deformation Roadmaps (PDRs), by over two orders of magnitude. We explore the trade-off between quality and computational efficiency in the proposed metrics.

Walking rehabilitation must be performed based on voluntary motion intention, and for this purpose, the development of a control method for an exoskeleton robot based on voluntary intention is investigated. This study proposes a method of exoskeleton robot control to estimate the voluntary lower limb muscle activities lost after a spinal cord injury (SCI). This method is based on the spinal cord mapping of the remaining muscle activities and its matching to the one obtained from healthy participants considering the muscle synergy of the whole body during the walking motion. By implementing the matching procedure at the spinal cord map level and incorporating information of reliable and unreliable spinal cord levels based on a diagnosis, the method has the potential to provide a maximally voluntary locomotion for people with SCI. We report an analysis of the synergy of the whole-body muscle activity during walking and its spinal cord mapping using non-negative matrix factorization and the computation of the transformation matrix to estimate the intended lower limb muscle activity from the remaining spinal cord activity. The implementation of the proposed method using the right leg of the hybrid assistive limb and walking experiments with a healthy participant are also reported.

Planning under partial observability is both challenging and critical for reliable robot operation. The past decade has seen substantial advances in this domain: The mathematically principled approach for addressing such problems, namely the Partially Observable Markov Decision Process (POMDP), has started to become practical for various robotics tasks. Good approximate solutions for problems framed as POMDPs can now be computed on-line, with a few classes of problems being solved in near real-time. However, applications of these more recent advances are often hindered by the lack of easy-to-use software tools. Implementation of state of the art algorithms exist, but most (if not all)require the POMDP model to be hard-coded inside the program, increasing the difficulty of applying them. To alleviate this problem, we propose a software toolkit, called On-line POMDP Planning Toolkit (OPPT)(downloadable from http://robotics.itee.uq.edu.au/~oppt). By providing a well-defined and general abstract solver API, OPPT enables the user to quickly implement new POMDP solvers. Furthermore, OPPT provides an easy-to-use plug-in architecture with interfaces to the high-fidelity simulator Gazebo that, in conjunction with user-friendly configuration files, allows users to specify POMDP models of a standard class of robot motion planning under partial observability problems with no additional coding effort.

Wearable robotics offers a unique opportunity to explore how biological systems interface with engineered parts. But, due to a gap in understanding of the underlying biological mechanisms at work, the state of the art in design and development is a sophisticated form of automated trial and error. Progress is hampered by the difficulty of assessing the direct impact of wearable robots on underlying muscles, tendons and bones during human experimentation. While animal models have provided an experimental platform to explore other biological mechanisms, as of yet, no animal model of a wearable robot during locomotion has been developed. To fill this gap, we have built the first ever wearable robotic device for a freely-Iocomoting, non-human, bipedal animal (Numida melaegris = Guinea fowl), a species whose gait closely mirrors human locomotion mechanics. We found that a spring-loaded soft-exosuit that passively augments the energy stored in distal tendons was both well tolerated and provided consistent torques. Preliminary data showed birds systematically change their kinematics in response to changes to exo-suit spring stiffness, adjusting the timing but not magnitude of the assistive torques. This animal model for wearable robotics allows experiments up and down the broader spatiotemporal scale that are not currently possible in humans. With it we can address questions from short-term adaptations in musculoskeletal dynamics within a single step to broader behavioral and physical changes that come with long term use.

In this paper, we describe a soft-robotic actuation concept to enable a mesoscale medical robot to navigate the natural lumens of the body, such as blood vessels and intestines. The concept comprises a simple soft robot with two embedded permanent magnets with alternating magnetic polarity, and a rotating (nonuniform) dipole magnetic field that is swept over the robot, resulting in a traveling-wave undulatory motion that propels the robot forward and backward. This soft-actuation technology can be fabricated in a wide range of sizes due to its simplicity, and has the potential to be applied in a variety of diagnostic and therapeutic contexts. We conduct experiments and numerical simulations to verify the movement of the soft robot. Then, we confirm the benefits of using nonuniform dipole fields over using uniform fields, as well as the benefits of alternating the polarity of the magnets embedded in the device.

This paper presents a control architecture readily suitable for surface treatment tasks such as polishing, grinding, finishing or deburring as carried out by a human operator, with the added benefit of accuracy, recurrence and physical strength as administered by a robotic manipulator partner. The shared strategy effectively couples the human operator propioceptive abilities and fine skills through his interactions with the autonomous physical agent. The novel proposed control scheme is based on task prioritization and a non-conventional sliding mode control, which is considered to benefit from its inherent robustness and low computational cost. The system relies on two force sensors, one located between the last link of the robot and the surface treatment tool, and the other located in some place of the robot end-effector: the former is used to suitably accomplish the conditioning task, while the latter is used by the operator to manually guide the robotic tool. When the operator chooses to cease guiding the tool, the robot motion safely switches back to an automatic reference tracking. The paper presents the theories for the novel collaborative controller, whilst its effectiveness for robotic surface treatment is substantiated by experimental results using a redundant 7R manipulator and a mock-up conditioning tool.

Parallel robots exhibit the so-called forward singularities, which complicate substantially the planning and control of their motions. Often, such complications are circumvented by restricting the motions to singularity-free regions of the workspace. However, this comes at the expense of reducing the motion range of the robot substantially. It is for this reason that, recently, efforts are underway to control singularity-crossing trajectories. This paper proposes a reliable controller to stabilize such kind of trajectories. The controller is based on the classical theory of linear quadratic regulators, which we adapt appropriately to the case of parallel robots. As opposed to traditional computed-torque methods, the obtained controller does not rely on expensive inverse dynamics computations. Instead, it uses an optimal control law that is easy to evaluate, and does not generate instabilities at forward singularities. The performance of the controller is exemplified on a five-bar parallel robot accomplishing two tasks that require the traversal of singularities.

Tactile sensing arrays are used to detect contacts of robotic systems with the environment. They are particularly useful for scenarios in which vision-based sensors cannot be used. Thanks to the presence of multiple sensing elements, tactile arrays also provide spatial information about the contact location. In this work, we present our series elastic tactile array to enable tactile exploration for position-controlled robot manipulators. Sixteen compliant sensing elements are arranged as a 4×4 array. This allows the position-controlled robot to explore objects via palpation. Tactile sensing was accomplished by measuring the change of the magnetic field caused by neodymium magnets embedded into the series elastic elements. We demonstrate the efficacy of our sensor with two sets of experiments involving physical interaction scenarios. Firstly, we show that the sensor can be used to differentiate between rigid and deformable objects. Secondly, we show that point clouds of objects can be generated quickly with our sensor module attached to a position-controlled robot manipulator as an end-effector.

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.

Catheter/guidewire manipulation in endovascu-lar intervention procedures are associated with risks of injury on vessel wall and embolization. Determination of catheter/guidewire-vessel interaction contact forces can improve the navigation process safety and efficiency which prevent injuries in both manual and robotic vascular interventions. This study proposes a sensor-less sensing solution to estimate multiple contact point forces at the side of catheter/guidewire exerted on the vasculature. This goal is achieved by using image feedback of catheter-vessel interaction and numerical finite element modeling (FEM). Real-time image processing algorithms are implemented to track interaction contact points on catheter/guidewire. Image-based deflection measurement and contact points tracking data are given to a nonlinear finite element beam model to estimate the forces. The variable equivalent bending modulus of the guidewire is found through a series of three-point-bending tests. To directly measure contact point forces, an experimental platform is prepared which simulates catheter/guidewire-vessel interaction with two, three and four contact points. The effectiveness of the proposed approach is tested in six scenarios in which force estimation accuracy of more than 87.9% is achieved. The proposed approach can be applied to various types of under-actuated catheter/guidewire in endovascular intervention procedures. This study proves that multiple catheter/guidewire side contact forces can be estimated by using the deflected shape and equivalent bending modulus property without embedding any force sensor.

Complex interactions with unstructured environments require the application of appropriate restoring forces in response to the imposed displacements. Impedance control techniques provide effective solutions to achieve this, however, their quasi-static performance is highly dependent on the choice of parameters, i.e. stiffness and damping. In most cases, such parameters are previously selected by robot programmers to achieve a desired response, which limits the adaptation capability of robots to varying task conditions. To improve the generality of interaction planning through task-dependent regulation of the parameters, this paper introduces a novel self-regulating impedance controller. The regulation of the parameters is achieved based on the robot's local sensory data, and on an interaction expectancy value. This value combines the interaction values from the robot state machine and visual feedback, to authorize the autonomous tuning of the impedance parameters in selective Cartesian axes. The effectiveness of the proposed method is validated experimentally in a debris removal task.

Artificial robotic skins are continuously in contact with their environment, and therefore highly rely on proper connections in their skin cells' network. With a static network protocol approach, the affected skin area is unusable after a connection failure. Therefore, we developed a dynamic network protocol for large-scale artificial robotic skins, which re-routes the network upon connection failures to keep the whole skin in operation. Furthermore, the protocol balances the load for driving larger skins without packet loss. For verification, we validated the protocol on a large artificial robot skin we have developed and analyzed its performance with a skin network consisting of up to 204 cells. The failure recovery of the protocol converges in at most 50ms. We showed that the balancing method achieves a packet loss reduction of over 30% compared to the previously used protocol.

An effective scheme to simulate low-speed, contact-rich manipulation tasks is to assume quasistatic physics and advance system states by solving linear complementarity problems (LCPs). However, the existing LCP-based quasistatic time-stepping scheme fails to simulate grasping-an essential motion primitive in manipulation-due to two drawbacks specific to quasistatic systems. Firstly, inputs to quasistatic systems are velocity commands instead of torques. This can lead to penetration, and thus an infeasible LCP, when two rigid bodies in contact are commanded to push against each other. Secondly, as multiple force solutions exist for a given velocity command, a grasping velocity command is not guaranteed to generate sufficient grasping forces. In this paper, we reformulate the quasistatic time-stepping scheme as an optimization problem with complementarity constraints and a quadratic objective. By minimizing the difference between actual and commanded velocities, linearized non-penetration constraints can always be satisfied. Moreover, undesirable solutions with insufficient normal forces can be removed by considering elasticity, which is modeled by comparing actual and commanded velocities. The resulting optimization problem is a mixed-integer quadratic program, which can be solved reasonably quickly for small-to-medium-sized systems. The effectiveness of the proposed reformulation is validated by simulation results of systems with different levels of complexity.

This paper presents a novel method for the control of the elbow joint of the humanoid robot TEO, based on a fractional order PD controller. Due to the graphical nature of the proposed method, a few basic operations are enough to tune the controller, offering very competitive results compared to classic methods. The experiments show a robust performance of the system to mass changes at the tip of the humanoid right arm.

Skin surface wounds due to burns, surgeries and chronic illness affect millions of people worldwide. Tissue engineering has become an increasingly popular treatment, but it is a highly manual process. Increasing the automation in tissue engineering could increase the rate of treatment for patients and improve outcomes. We present an initial investigation into an automated in-situ treatment. In our proposed method, a 3D machine vision system detects a skin wound to be treated and then determines the 3D point set corresponding to the wound. The 3D point set is then passed to path planning algorithm for a robot manipulator to move an ink-jet nozzle over the wound and fill the cavity with quick-curing/gelling fluids such collagen and other biomaterials and cell growth promoters. This paper details initial results and experimental validation of each of the proposed steps.

Quadrotor state estimation generally relies on the vehicle aerodynamics modeling to achieve improved performance. In this paper the effects of the rotors angular speeds on the quadrotor drag, and therefore on the lateral accelerations, are investigated. While these effects are usually disregarded, we analyze their modeling starting from the Blade Element Theory and flight test data. Two lateral acceleration formulations are proposed. They are adopted within a velocity and attitude state estimator and validated in real-world flights. The EKF-based estimator fuses measurements from low-cost sensors present in the majority of quadrotors (IMU, magnetometer, ultrasonic sensor, optical flow) with the accelerations of the vehicle predicted from the revisited models. Experimental results show the benefits of adopting these innovative models in the estimator when compared with the existing modeling approach.