IROS 2022 论文列表

Through the efforts of robotic engineers and inspired by the flapping flight of smaller creatures (e.g., insects and hummingbirds), the untethered stable hovering of flapping micro-aerial vehicles (FMAVs) has been achieved. Now, engineers are evaluating how to improve the mobility of these vehicles. The maneuverability of insects and birds in flight, such as their sharp turns and their takeoffs and landings from vertical walls, is what researchers originally expected from FMAVs. However, in previous studies, just one active (or one active plus one passive) degree of freedom (DoF) was given to the main actuation of the wing, and the range of movement of the wings was small. In addition, the magnitude of the attitude control torque that could be generated was relatively small when compared with multi-rotors. However, with the recent developments of small motors and drive-circuit technology, the realization of untethered flight by FMAVs equipped with four or more motors seems possible. This study utilized numerical calculation to investigate the advantage of a flapping-flight robot equipped with two pairs of left and right wings capable of stroke and twisting movements with two independent DoFs (2\times 22\times 2-DoF FMAV). The results of the numerical studies confirmed that, compared with the split-factor method widely used in past studies, the 2\times 22\times 2-DoF FMAV can generate higher yaw torque without the need for additional large driving torque. This shows that various agile flight functions are possible.

Soft microrobotics have recently been an active field that advances microrobotics with new robot design, locomotion, and applications. In this paper, we study the ferrofluid robot (FR), which has soft nature and exhibits paramagnetism. Currently, the FR locomotion is usually realized by magnetic force. To enable the FR with more locomotion modes for environment and task adaptability, we program three dynamic field forms and realize three corresponding torque-actuated locomotion modes: Rolling, Wobbling, and Oscillating. The torque actuation of the FR is formulated, and the three locomotion modes are characterized. With the implementation of automated tracking and control algorithms, the controllability of these modes is testified. We then fabricate different environments to validate the adaptability of the FR that can switch its locomotion mode accordingly. Finally, utilizing the oscillating mode and wobbling mode, we demonstrate the transport of lipophilic and hydrophilic cargoes, respectively, showing the task adaptability.

Non-linear dynamics, model uncertainties due to hydraulic fluid, and disturbances in hydraulic systems make it difficult to obtain accurate torque tracking performance. In this study, a learning-based torque-tracking method is proposed, which does not require approximating the torque dynamics. The proposed method can capture disturbances and model uncertainties of system. The applied neural network comprises a nonlinear autoregressive model with exogenous inputs (NARX) and a long shortterm neural network (LSTM). NARX is employed due to its ability to predict time series control input from the states of system, and LSTM is used to overcome the vanishing and exploding gradient, which causes long-term memory loss in NARX, leading to inaccurate torque tracking performance. LSTM with NARX achieved a better prediction performance with a mean square error and standard deviation of 0.0015 \pm 0.4\times 1030.0015 \pm 0.4\times 103 compared to only NARX with a mean square error of 0.004 \pm 1.0\times 1030.004 \pm 1.0\times 103 at 10 K training data size.

We propose a novel toroidal origami monotrack capable of smooth-skin driving and bending for closed-skin-drive robots. Monotracks are a promising solution for achieving high mobility in unstructured environments. Toroidal-drive mechanisms enable whole skin drive; however, conventional methods experience unexpected wrinkling and buckles that lead to a large resistance. In this study, we propose an origami bellows structure with multiple rollers that can maintain the skin tension and deal with the cause of large friction between the skin and the body. The origami structure design method is presented, and the bending angle range and required drive force were derived through a theoretical analysis. The validity of the effectiveness of the concept was verified through prototype testing.

Recent advances in soft robotics in academia have led to the adoption of soft grippers in industrial settings. Due to their soft bending actuators, these grippers can handle delicate objects with great care. However, due to their flexibility, the actuators are prone to out-of-plane deformations upon asymmetric loading. These undesired deformations lead to reduced grasp performance and may cause instability or failure of the grip. While the state-of-the-art contributions describe complex designs to limit those deformations, this work focuses on a complementary path investigating the material distribution. In this paper, a novel bending actuator is developed with improved out-of-plane deformation resistance by optimizing the material distribution in multi-material designs composed of two polymers with different mechanical properties. This is made possible by the strong interfacial strength of Diels-Alder chemical bonds in the used polymers, which have a self-healing capability. A Solid Isotropic Material with Penalization (SIMP) topology optimization is performed to increase the out-of-plane resistance. The actuator is simulated using FEA COMSOL in which the (hyper) elastic materials are simulated by Mooney-Rivlin models, fitted on experimental uniaxial tensile test data. This multi-material actuator and a reference single material actuator were manufactured and modeled. Via experimental characterization and validation in FEA simulations, it is shown that the actuator out-of-plane stiffness, characterized by the in-plane bending angle and out-of-plane bending angle, can be increased by an optimized multi-material composition, without changing the geometrical shape of the actuator.

This paper presents the WiFi-Sensor-for-Robotics (WSR) open-source toolbox111Code: https://github.com/Harvard-REACT/WSR-Toolbox Dataset: https://github.com/Harvard-REACT/WSR-Toolbox-Dataset Demo: https://github.com/Harvard-REACT/WSR-Toolbox/wiki/Demo. It enables robots in a team to obtain relative bearing to each other, even in nonline-of-sight (NLOS) settings which is a very challenging problem in robotics. It does so by analyzing the phase of their communicated WiFi signals as the robots traverse the environment. This capability, based on the theory developed in our prior works, is made available for the first time as an open-source toolbox. It is motivated by the lack of easily deployable solutions that use robots' local resources (e.g WiFi) for sensing in NLOS. This has implications for multi-robot mapping and rendezvous, ad-hoc robot networks, and security in multi-robot teams, amongst other applications. The toolbox is designed for distributed and online deployment on robot platforms using commodity hardware and on-board sensors. We also release datasets demonstrating its performance in NLOS and line-of-sight (LOS) settings and for a multi-robot localization use case. Empirical results for hardware experiments show that the bearing estimation from our toolbox achieves accuracy with mean and standard deviation of 1.13 degrees, 11.07 degrees in LOS and 6.04 degrees, 26.4 degrees for NLOS, respectively, in an indoor office environment.

We present a predictive system for non-prehensile, physics-based motion planning in clutter with a human-in-the-loop. Recent shared-autonomous systems present motion planning performance improvements when high-level reasoning is provided by a human. Humans are usually good at quickly identifying high-level actions in high-dimensional spaces, and robots are good at converting high-level actions into valid robot trajectories. In this paper, we present a novel framework that permits a single human operator to effectively guide a fleet of robots in a virtual warehouse. The robots are tackling the problem of Reaching Through Clutter (RTC), where they are reaching onto cluttered shelves to grasp a goal object while pushing other obstacles out of the way. We exploit information from the motion planning algorithm to predict which robot requires human help the most and assign that robot to the human. With twenty virtual robots and a single human-operator, the results suggest that this approach improves the system's overall performance compared to a baseline with no predictions. The results also show that there is a cap on how many robots can effectively be guided simultaneously by a single human operator.

We introduce a novel approach for temporal activity segmentation with timestamp supervision. Our main contribution is a graph convolutional network, which is learned in an end-to-end manner to exploit both frame features and connections between neighboring frames to generate dense framewise labels from sparse timestamp labels. The gener-ated dense framewise labels can then be used to train the segmentation model. In addition, we propose a framework for alternating learning of both the segmentation model and the graph convolutional model, which first initializes and then iteratively refines the learned models. Detailed experiments on four public datasets, including 50 Salads, GTEA, Breakfast, and Desktop Assembly, show that our method is superior to the multi-layer perceptron baseline, while performing on par with or better than the state of the art in temporal activity segmentation with timestamp supervision.

The robotic flexible endoscope is developed rapidly in the field of surgery robots due to its high flexibility and safety. However, some inherent features, e.g., high nonlinearity, material creep, complex dynamic hysteresis behaviors, and the unknown coupling effects between bending and twisting motions, can lead to the significant degradation on three-dimensional (3-D) positioning performance of the endoscope. Aiming at these challenges, this paper built a practical multi-motion hysteresis phenomenon model for the bending and twisting motions of the robotic flexible endoscope with consideration of the coupling effects. Then, the time-optimal synchronous terminal motion planner is first proposed for the 3-D motions of the robotic endoscope to decouple the coupling effects in an intuitive separate control scheme. Finally, a series of hardware experiments are conducted on a robotic flexible ureteroscope platform. The accuracy of the proposed model and the trajectory-planning-based decoupling strategy is comprehensively validated. Particularly, the experimental results with the proposed trajectory planner show the satisfactory performance of vibration suppression and over-shoot suppression.

Multicopter Unmanned Aerial Vehicles (UAV) are small and agile robots with the potential to become prominent in performing autonomous tasks in various Global Navigation Satellite System (GNSS)-denied environments. These environments can potentially be rendered even more challenging due to external factors impairing the robot's perception, such as low or too bright light, permeation with aerosols or smoke. A precondition of autonomous operation, though, is the ability of a robot to accurately localize itself in the surrounding environment. Millimeter-wave Frequency Modulated Continuous Wave (FMCW) radar sensors are resilient to the aforementioned factors while being lightweight, inexpensive and highly accurate. In this paper, we present a Radar-Inertial Odometry (RIO) method for estimating the full 6DoF pose and 3D velocity of a UAV. In an Extended Kalman Filter (EKF) framework, we fuse range measurements and velocity measurements of 3D points detected by an FMCW radar sensor together with Inertial Measurement Unit (IMU) readings. In real experiments we show that our approach enables accurate state estimation of a UAV and that it exhibits improvements over similar existing state-of-the-art method.

This paper presents a novel framework for point- foot biped running in three-dimensional space. The proposed approach generates center of mass (CoM) reference trajectories based on a differentially flat spring-loaded inverted pendulum (SLIP) model. A foothold planner is used to select touch down location that renders optimal CoM trajectory for upcoming step in real time. Dynamically feasible trajectories of CoM and orientation are subsequently generated by a simplified single rigid body (SRB) model based model predictive control (MPC). A task-space controller is then applied online to compute whole- body joint torques which embeds these target dynamics into the robot. The proposed approach is evaluated on physical simulation of a 12 degree-of-freedom (DoF), 7.95 kg point-foot bipedal robot. The robot achieves stable running at at varying speeds with maximum value of 1.1 m/s. The proposed scheme is shown to be able to reject vertical disturbances of 8 N. s and lateral disturbance of 6.5 N. s applied at the robot base.

A spherical robotic probe has several advantages in rough environments and has therefore raised interest for application in planetary exploration. A sphere is well-suited to protect high-sensitive payloads, however, the locomotion system for planetary surfaces raises several challenges. This paper presents a novel locomotion system consisting of linear actuators which are usable in a multi-functional fashion. Apart from pushing and bringing leverage for locomotion the extendable rods enable a tripod mode for improved sensing. The developed solutions offer a mathematical-physical system description, simple algorithms for the control of locomotion and balancing as well as general calculations for determining the maximum achievable performance parameters of such a robot. The first built prototype shows the basic suitability of the system and reveals directions for further research.

In this paper, we present a method for updating robotic state belief through contact with uncertain surfaces and apply this update to a Kalman filter for more accurate state estimation. Examining how guard surface uncertainty affects the time spent in each mode, we derive a novel guard saltation matrix- which maps perturbations prior to hybrid events to perturbations after - accounting for additional variation in the resulting state. Additionally, we propose the use of parame-terized reset functions - capturing how unknown parameters change how states are mapped from one mode to the next - the Jacobian of which accounts for additional uncertainty in the resulting state. The accuracy of these mappings is shown by simulating sampled distributions through uncertain transition events and comparing the resulting covariances. Finally, we integrate these additional terms into the “uncertainty aware Salted Kalman Filter”, uaSKF, and show a peak reduction in average estimation error by 24–60% on a variety of test conditions and systems.

A long-standing question in robot hand design is how accurate tactile sensing must be. This paper uses simulated tactile signals and the reinforcement learning (RL) framework to study the sensing needs in grasping systems. Our first experiment investigates the need for rich tactile sensing in the rewards of RL-based grasp refinement algorithms for multi-fingered robotic hands. We systematically integrate different levels of tactile data into the rewards using analytic grasp stability metrics. We find that combining information on contact positions, normals, and forces in the reward yields the highest average success rates of 95.4% for cuboids, 93.1% for cylinders, and 62.3% for spheres across wrist position errors between 0 and 7 centimeters and rotational errors between 0 and 14 degrees. This contact-based reward outperforms a non-tactile binary-reward baseline by 42.9%. Our follow-up experiment shows that when training with tactile-enabled rewards, the use of tactile information in the control policy's state vector is drastically reducible at only a slight performance decrease of at most 6.6% for no tactile sensing in the state. Since policies do not require access to the reward signal at test time, our work implies that models trained on tactile-enabled hands are deployable to robotic hands with a smaller sensor suite, potentially reducing cost dramatically.

The fingerprint effect, wherein vibrations are produced with frequencies related to the speed of a surface sliding across fingerprint ridges and the period of those ridges, has been studied for use in both slip detection and texture recognition. Here, we use a simple bioinspired sensor with parallel, straight, fingerprint-like ridges and a single ferroelectric ceramic transducer to show that the fingerprint effect is orientation dependent and that, if the orientation is known, it can be used to estimate slip speed. Our results, obtained at sliding speeds of 15 mm/s, 20 mm/s, and 25 mm/s and orientations from 0° – 90°, clearly demonstrate this dependence. Additionally, we use our results to run a simulation, using MATLAB software, of real-time slip speed estimation. The simulation shows that the fingerprint effect can be used for real-time slip-speed estimation.

Robots with elasticity in structural components can suffer from undesired end-effector positioning imprecision, which exceeds the accuracy requirements for successful manipulation. We present the Probabilistic-Product-Of-Exponentials robot model, a novel approach for kinematic modeling of robots. It does not only consider the robot's deterministic geometry but additionally models time-varying and configuration-dependent errors in a probabilistic way. Our robot model allows to propagate the errors along the kinematic chain and to compute their influence on the end-effector pose. We apply this model in the context of sensor fusion for manipulator pose correction for two different robotic systems. The results of a simulation study, as well as of an experiment, demonstrate that probabilistic configuration-dependent error modeling of the robot kinematics is crucial in improving pose estimation results.

When teleoperating complex robotic manipula-tors, operators often find it most natural to issue commands that dictate end effector movements in task space. If the robot has redundant degrees of freedom, the translation of this com-mand from task space into configuration space can affect the robot's maneuverability, smoothness of motion, and the general precision of the teleoperated system. In this paper, we propose a novel method for performing this translation that predicts future operator commands in order to choose joint motions that maintain maneuverability in future timesteps. We introduce a Predictive Kinematic Control Tree (PrediKCT) that optimizes joint movement in the nullspace of the Jacobian over multiple future timesteps by reasoning over probabilistic models of the human operator. In essence, PrediKCT builds out and evaluates a tree of possible future commands. We implement this system on two simulated and one physical 7 -degree-of-freedom robotic arms and characterize performance by analyzing robot motions produced through multiple command trajectories with differing user model accuracies and tree parameters, demonstrating benefits to path accuracy over both a minimum-norm joint velocity solution and local optimization of joint movement.

Research around tactile sensing for grasp stability prediction in robotic manipulators continues to be popular, however few works are able to achieve a high classification accuracy. Due to simulation complexity, data-driven methods are often forced to rely on experimental data, yielding small, often unbalanced, data sets. In this work, the authors use a 3972 sample data set to explore the effects of the data set composition on the performance of a classifier. While maintaining a similar overall accuracy, the ability to recognize a grasp failure was significantly impacted by the composition of the data set. The authors propose an autonomous pipeline designed to generate more diverse failure grasps. On failure-rich data, a tactile-based classifier with a balanced training set achieved a classification accuracy of 84.68% while maintaining a recall of the grasp failure class of 76%. This represents a 71.79% improvement in recall over a model trained on a larger but unbalanced data set.

WORMESH is a unique robot concept inspired by flatworm locomotion and its key feature is the use of multiple traveling waves for locomotion. This paper presents the steering method for anisotropic module configuration (AMC) of WORMESH-II based on the kinematics of skid steering of mobile robots. AMC of WORMESH-II used two parallel pedal waves to generate locomotion. The kinematic model of WORMESH-II shows its longitudinal and angular velocities depend on the summation and the difference of two synchronous left and right pedal waves amplitudes A_{l}A_{l} and A_{r}A_{r} respectively. When both pedal waves have the same amplitude, the robot moves on a straight line, whereas the trajectory becomes a curve for different wave amplitudes. The radius of curve trajectory is inversely proportional to \vert A_{l}-A_{r}\vert\vert A_{l}-A_{r}\vert. The proposed method was ineffective when A_{i}\approx 0(i=l,\ r)A_{i}\approx 0(i=l,\ r). The proposed method was confirmed by the dynamic simulation of WORMESH-II using a physics engine. Moreover, the recommended skid steering method was tested using the prototype and verified.