IROS 2019 论文列表

Applications like disaster management and industrial inspection often require experts to enter contaminated places. To circumvent the need for physical presence, it is desirable to generate a fully immersive individual live teleoperation experience. However, standard video-based approaches suffer from a limited degree of immersion and situation awareness due to the restriction to the camera view, which impacts the navigation. In this paper, we present a novel VR-based practical system for immersive robot teleoperation and scene exploration. While being operated through the scene, a robot captures RGB-D data that is streamed to a SLAM-based live multiclient telepresence system. Here, a global 3D model of the already captured scene parts is reconstructed and streamed to the individual remote user clients where the rendering for e.g. head-mounted display devices (HMDs) is performed. We introduce a novel lightweight robot client component which transmits robot-specific data and enables a quick integration into existing robotic systems. This way, in contrast to first- person exploration systems, the operators can explore and navigate in the remote site completely independent of the current position and view of the capturing robot, complementing traditional input devices for teleoperation. We provide a proof-of-concept implementation and demonstrate the capabilities as well as the performance of our system regarding interactive object measurements and bandwidth-efficient data streaming and visualization. Furthermore, we show its benefits over purely video-based teleoperation in a user study revealing a higher degree of situation awareness and a more precise navigation in challenging environments.

Monocular Odometry systems can be broadly categorized as being either Direct, Indirect, or a hybrid of both. While Indirect systems process an alternative image representation to compute geometric residuals, Direct methods process the image pixels directly to generate photometric residuals. Both paradigms have distinct but often complementary properties. This paper presents a Unified Formulation for Visual Odometry, referred to as UFVO, with the following key contributions: (1) a tight coupling of photometric (Direct) and geometric (Indirect) measurements using a joint multi-objective optimization, (2) the use of a utility function as a decision maker that incorporates prior knowledge on both paradigms, (3) descriptor sharing, where a feature can have more than one type of descriptor and its different descriptors are used for tracking and mapping, (4) the depth estimation of both corner features and pixel features within the same map using an inverse depth parametrization, and (5) a corner and pixel selection strategy that extracts both types of information, while promoting a uniform distribution over the image domain. Experiments show that our proposed system can handle large inter-frame motions, inherits the sub-pixel accuracy of direct methods, can run efficiently in real-time, can generate an Indirect map representation at a marginal computational cost when compared to traditional Indirect systems, all while outperforming state of the art in Direct, Indirect and hybrid systems.

To actively assist human walking and balance recovery, a unified active assistance control framework of the hip exoskeleton is proposed in this paper. At the beginning of this paper, the condition of active assistance is analyzed. And then, a novel virtual stiffness model is proposed based on the analysis of human hip joint torque during walking and balance recovery. The virtual stiffness model is utilized to generate the desired active assistance torque profile for active walking and balance assistance. Next, the unified active assistance control framework is established based on the virtual stiffness model. Finally, the effectiveness of the proposed control framework is demonstrated by walking experiments. The results of the forward walking experiment show that the muscle effort of iliopsoas is reduced and the amplitude of hip joint motion is enlarged with the assistance of hip exoskeleton. The walking forward pull experiments and the walking backward pull experiments show that when the human suffers from a forward or backward disturbance force during forward walking, exoskeleton can help human regain balance faster and can enlarge the margin of stability (MoS) of the human.

This paper presents the design, modeling, analysis, and experimental results of a bipedal robotic system that utilizes two interconnected single degree-of-freedom leg mechanisms to produce stable forward locomotion and steering. The legs are composed of double four-bar mechanism connected in series that maintain a parallel orientation of a flat foot, relative to the biped body, that is actuated via a Reuleaux triangle cam-follower system to produce a desirable foot trajectory. The mechanical design of the leg mechanism is presented followed by kinematic analysis of the cam-follower system to select the optimal foot trajectory and synthesize the mechanism dimensions and produce a desired step height and step length. The concept of leg sequencing is then presented to maintain a constant body height above the ground and a constant forward walking velocity. Experimental results using an integrated prototype indicate that the proposed biped robot is capable of maintaining quasi-static stability during locomotion, maintaining a constant robot body height, maintaining a constant body orientation, move forward with a constant maximum velocity of 27.4 cm/s, and steer.

As robots and other autonomous agents are increasingly incorporated into complex domains, characterizing interaction within heterogeneous teams that include both humans and machines becomes more necessary. Previous literature has addressed the task of characterizing human-robot interaction from different perspectives and in multiple contexts. However, the numerous factors behind interaction work in conjunction, and the insights gained from one perspective can inadvertently affect another, creating a need for unification of these taxonomies and frameworks within an overarching taxonomy that systematically defines these relationships. In this paper we review existing taxonomies related to human-robot interaction, the behavioral sciences, and social and algorithmic taxonomies, and propose an overarching ontology for the factors from these works. We identify three main components characterizing the structure of an interaction (environment, task, and team), and structure them over two levels: contextual factors and factors driven by local dynamics. Finally, we present an analysis of how these factors affect decisions about levels of robot automation and level of information abstraction in an interaction, and discuss curent gaps in the literature that can motivate future research.

This paper provides a survey of the different means of expression employed by robots conveying affective states to human recipients. The paper introduces a model of affective expression modalities (MOAM) that describes and compares the emphasis on specific means of expression and applies it to the surveyed robots. Using the model entails viewing the effect of applied expression modalities in light of how well the robot responds to external stimuli and with attention to how aligned the robot's means of affective expressions are with the intended working scenario. The model-based survey shows that a majority (85%) of the surveyed robots contain a category with room for additional affective means of expression, and a quarter (25.6%) of the robots use a single or two affective expression modalities to convey affective states. The result of the survey indicates there is an under-researched opportunity in exploring synergies between the different expression modalities to amplify the overall affective impact of a robot.

Successful sampling on the surface of asteroids is difficult because of their weightless environment and unknown material mechanical property. This work presents an asteroid sampler based on sweeping and grinding methods to improve the success rate of sampling. The sampler uses two brushes rotating clockwise and counter-clockwise to collect sample particles on the surface of asteroids. When encountering the hard rock or sample particles with large cohesion, the sampler adopts a drill bit to grind them to loose samples suitable for collecting by the brushes. The interaction between the brushes and the regolith is modeled and the sweeping mechanism is designed. A simple grinding mechanism is also designed. Numerical simulation and prototype experiments, at different parameters including blades number, rotational speed, and feeding speed of the brushes, mechanical property of the sample, and gravity, were conducted for validating the proposed methods. The 280g sampler prototype with 8 blades of brushes could collect about 19g regolith simulant in 25s in earth environment. The drill bits could work together with the brushes to improve the sampling efficiency through DEM simulation in case of large cohesion among sample particles. The sampler will be a good choice for installing into an asteroid rover in exploration.

In this paper we present a study of a specific class of vibration-driven robots: the brushbots. In a bottom-up fashion, we start by deriving dynamic models of the brushes and we discuss the conditions under which these models can be employed to describe the motion of brushbots. Then, we present two designs of brushbots: a fully-actuated platform and a differential-drive-like one. The former is employed to experimentally validate both the developed theoretical models and the devised motion control algorithms. Finally, a coordinated-control algorithm is implemented on a swarm of differential-drive-like brushbots in order to demonstrate the design simplicity and robustness that can be achieved by employing a vibration-based locomotion strategy.

This paper presents a distributed six-link two-dimensional electromechanical actuator that emulates a biological spine. The gearless actuator is made by stacking modules of E-shaped cores along with integrated springs. A coil excitation induces magnetic flux in the core, which produces electromechanical force in the air gap between two adjacent cores. The proposed actuator is driven by dc-dc converters with current control. Electromechanical force analysis for the proposed spine architecture with different air-gap distances and spring analysis that improve the actuator's performance are discussed. Experimental results show different biological motions with a prototype design. The prototype can produce a maximum torque of 1.4 N-m.

This paper presents an approach to show global controllability of a class of underactuated mechanical systems. This class of systems include gymnastic robots and the classic cart-pole system. For this class of systems, we can define a connection point, and design a damping controller u(t) with the Lyapunov method to drive all states to the connection point. Further by exploiting the time reversal symmetry of the system and spline interpolation, a controller u(-t) can be obtained to drive the system from the connection point to any other state. The system is thus shown completely controllable, i.e., there exists an admissible trajectory from any given state to any given final state. Swing-up control designs for a pendubot, cart-pole system and triple pendulum are given as illustrative examples.

We propose a hybrid pyramid based approach to fuse the direct and indirect methods in visual SLAM, to allow robust localization under various situations including large-baseline motion, low-texture environment, and various illumination changes. In our approach, we first calculate coarse inter-frame pose estimation by matching the feature points. Subsequently, we use both direct image alignment and a multiscale pyramid method, for refining the previous estimation to attain better precision. Furthermore, we perform online photometric calibration along with pose estimation, to reduce un-modelled errors. To evaluate our approach, we conducted various real-world experiments on both public datasets and self-collected ones, by implementing a full SLAM system with the proposed methods. The results show that our system improves both localization accuracy and robustness by a wide margin.

Automatic control of mobile robots and robot arms requires techniques for estimating the position and orientation of objects with high accuracy and robustness. Although methods using markers or machine learning have been developed, general-purpose and highly accurate estimations have not been realized. Our team developed a high-accuracy visual marker “LentiMark” for high-accuracy estimations of position and posture, but data are lost when the camera cannot obtain marker images. We therefore developed the Marker-IMU system for integrating visual markers with an inertia measurement unit (IMU). When cameras cannot acquire the image of a visual marker, any missing data are restored from IMU data. However, when calculating positions from acceleration sensor values, sensor error increases estimation error. Therefore, we developed a method for error correction using measurements from before and after the missing data. Evaluation experiments confirm that missing data can be estimated using the proposed method.

In this paper, we propose a novel laser-inertial odometry and mapping method to achieve real-time, low-drift and robust pose estimation in large-scale highway environments. The proposed method is mainly composed of four sequential modules, namely scan pre-processing module, dynamic object detection module, laser-inertial odometry module and laser mapping module. Scan pre-processing module uses inertial measurements to compensate the motion distortion of each laser scan. Then, the dynamic object detection module is used to detect and remove dynamic objects from each laser scan by applying CNN segmentation network. After obtaining the undistorted point cloud without moving objects, the laser-inertial odometry module uses an Error State Kalman Filter to fuse the data of laser and IMU and output the coarse pose estimation at high frequency. Finally, the laser mapping module performs a fine processing step and the “Frame-to-Model'' scan matching strategy is used to create a static global map. We compare the performance of our method with two state-of-the-art methods, LOAM and SuMa, using KITTI dataset and real highway scene dataset. Experiment results show that our method performs better than the state-of-the-art methods in real highway environments and achieves competitive accuracy on the KITTI dataset.

Accurate extrinsic sensor calibration is essential for both autonomous vehicles and robots. Traditionally this is an involved process requiring calibration targets, known fiducial markers and is generally performed in a lab. Moreover, even a small change in the sensor layout requires recalibration. With the anticipated arrival of consumer autonomous vehicles, there is demand for a system which can do this automatically, after deployment and without specialist human expertise. To solve these limitations, we propose a flexible framework which can estimate extrinsic parameters without an explicit calibration stage, even for sensors with unknown scale. Our first contribution builds upon standard hand-eye calibration by jointly recovering scale. Our second contribution is that our system is made robust to imperfect and degenerate sensor data, by collecting independent sets of poses and automatically selecting those which are most ideal. We show that our approach's robustness is essential for the target scenario. Unlike previous approaches, ours runs in real time and constantly estimates the extrinsic transform. For both an ideal experimental setup and a real use case, comparison against these approaches shows that we outperform the state-of-the-art. Furthermore, we demonstrate that the recovered scale may be applied to the full trajectory, circumventing the need for scale estimation via sensor fusion.

Generating robust locomotion for a humanoid robot in the presence of disturbances is difficult because of its high number of degrees of freedom and its unstable nature. In this paper, we used the concept of Divergent Component of Motion (DCM) and propose an optimal closed-loop controller based on Linear-Quadratic-Gaussian to generate a robust and stable walking for humanoid robots. The biped robot dynamics has been approximated using the Linear Inverted Pendulum Model (LIPM). Moreover, we propose a controller to adjust the landing location of the swing leg to increase the withstanding level of the robot against a severe external push. The performance and also the robustness of the proposed controller is analyzed and verified by performing a set of simulations using MATLAB. The simulation results showed that the proposed controller is capable of providing a robust walking even in the presence of disturbances and in challenging situations.

Introducing alterations in the mtDNA sequence is challenging but needed for potential therapies and basic studies. Direct microinjection of mitochondria into small cells has been considered inefficient and impractical. To address this issue, we present a highly efficient and precise robotic approach for automatically transferring mitochondria from one single cell to another. A microfluidic cell positioning device is used to pattern two different types of cells in one dimensional array, and an image processing algorithm is applied to identify the location of the mitochondria and cell. A visual feedback control mechanism is developed to enhance the mitochondrial extraction efficiency. A robust adaptive sliding control algorithm is developed to precisely control an X-Y stage to accomplish the extraction of mitochondria from A type cell followed by injection of the mitochondria into B type cell automatically. The system can transfer mitochondria from one cell to another with an average duration of 15 s/mitochondria. Experiments of mitochondrial transfer from THPI and NB4 cells to THPI cells and fibroblasts are conducted to show the effectiveness of the developed approach.

Articulated robots such as the humanoid robot often have multiple joints implemented in its limbs. It is common for sensor and controller cables to be routed inside of the limb, impacting mobility. A network protocol with high efficiency, low latency, fault tolerance is needed to facilitate communication of sensor and controller data over a more compact communications channel. In this paper, we propose a ring network protocol with fault tolerance in low latency, high efficiency communication over a compact channel. The performance of the proposed network protocol was verified in simulation and experiment, showing that low latency, high efficiency, and fault tolerance can be achieved over a single communications channel.

Robot Dynamic Simulators offer convenient implementation and testing of physical robots, thus accelerating research and development. While existing simulators support most real-world robots with serially linked kinematic and dynamic chains, they offer limited or conditional support for complex closed-loop robots. On the other hand, many of the underlying physics computation libraries that these simulators employ support closed-loop kinematic chains and redundant mechanisms. Such mechanisms are often utilized in surgical robots to achieve constrained motions (e.g., the remote center of motion (RCM)). To deal with such robots, we propose a new simulation framework based on a front-end description format and a robust real-time dynamic simulator. Although this study focuses on surgical robots, the proposed format and simulator are applicable to any type of robot. In this manuscript, we describe the philosophy and implementation of the front-end description format and demonstrate its performance and the simulator’s capabilities using simulated models of real-world surgical robots.

Research in autonomous driving has benefited from a number of visual datasets collected from mobile platforms, leading to improved visual perception, greater scene understanding, and ultimately higher intelligence. However, this set of existing data collectively represents only highly structured, urban environments. Operation in unstructured environments, e.g., humanitarian assistance and disaster relief or off-road navigation, bears little resemblance to these existing data. To address this gap, we introduce the Robot Unstructured Ground Driving (RUGD) dataset with video sequences captured from a small, unmanned mobile robot traversing in unstructured environments. Most notably, this data differs from existing autonomous driving benchmark data in that it contains significantly more terrain types, irregular class boundaries, minimal structured markings, and presents challenging visual properties often experienced in off road navigation, e.g., blurred frames. Over 7, 000 frames of pixel-wise annotation are included with this dataset, and we perform an initial benchmark using state-of-the-art semantic segmentation architectures to demonstrate the unique challenges this data introduces as it relates to navigation tasks.