IROS 2020 论文列表

UAVs or Unmanned Aerial Vehicles are an upcoming technology which has eased human lifestyles in many ways. Due to this trend future skies have a risk of getting congested. In such a situation time optimal collision avoidance would be extremely vital to travel in a shortest possible time by avoiding collisions. The paper proposes a novel method for time optimal collision avoidance for UAVs. The proposed algorithm is constructed as a three-stage approach based on the Collision Cone method with slight modifications. A sliding mode controller is used as the control law for the navigation. Mathematical proofs are included to verify the time optimality of the proposed method. The efficiency and the applicability of the work carried out is confirmed by both simulation and experimental results. An automated Matrice 600 Pro hexacopter has been used for the experiments.

Novel robotic grippers have captured increasing interests recently because of their abilities to adapt to varieties of circumstances and their powerful functionalities. Differing from traditional gripper with mechanical components-made fingers, novel robotic grippers are typically made of novel structures and materials, using a novel manufacturing process. In this paper, a novel robotic gripper with external frame and internal thermoplastic elastomer belt-made net is proposed. The gripper grasps objects using the friction between the net and objects. It has the ability of adaptive gripping through flexible contact surface. Stress simulation has been used to explore the regularity between the normal stress on the net and the deformation of the net. Experiments are conducted on a variety of objects to measure the force needed to reliably grip and hold the object. Test results show that the gripper can successfully grip objects with varying shape, dimensions, and textures. It is promising that the gripper can be used for grasping fragile objects in the industry or out in the field, and also grasping the marine organisms without hurting them.

In this work, we present a novel method for reconstructing particular 3D surface points using an imaging sonar sensor. We derive the two-dimensional Fermat flow equation, which may be applied to the planes defined by each discrete azimuth angle in the sonar image. We show that the Fermat flow equation applies to boundary points and surface points which correspond to specular reflections within the 2D plane defined by their azimuth angle measurement. The Fermat flow equation can be used to resolve the 2D location of these surface points within the plane, and therefore also their full 3D location. This is achieved by translating the sensor to estimate the spatial gradient of the range measurement. This method does not rely on the precise image intensity values or the reflectivity of the imaged surface to solve for the surface point locations. We demonstrate the effectiveness of our proposed method by reconstructing 3D object points on both simulated and real-world datasets.

With the booming development in aerospace technology, the video satellite which observes the live phenomena on the ground by video shooting has gradually emerged as a new Earth observation method. And remote sensing comes into a "dynamic" era with the demand for new processing techniques, especially the near-real-time tracking and geo-positioning algorithm for ground moving targets. However, many researchers merely extract pixel-level trajectories in post-processed video products, resulting in fairly limited applications. We regard the video satellite as a robot flying in space and adopt the SLAM framework for the positioning of ground moving targets. The designed framework is based on the representative ORB-SLAM and we make improvements mainly in feature extraction, satellite pose estimation, moving target tracking and positioning. We coordinate a moving fishing boat with GPS-RTK (Real-time Kinematic) devices and a video satellite observing it simultaneously for verification and evaluation of our method. Experiments demonstrate that our framework provides reasonable geolocation of the moving target in satellite videos. Finally, some open problems and potential research directions are discussed.

Synthetic fibre ropes have high tensile strength, a lower friction coefficient and are more flexible than steel ropes, and are therefore increasingly used in robotics. However, their characteristics are not well studied. In particular, previous work investigated the long-term behaviour only under static loading. In this paper, we investigate the elongation behaviour of synthetic fibre ropes under cyclic loading. In particular, we use ropes made from Dyneema DM20 (UHMWPE) and Zylon AS (PBO), which according to prior work have low creep. While Dyneema is more widely used, Zylon has higher tensile strength. We could show that under cyclic loading the Dyneema DM20 rope elongated more than 9% and kept on extending even after 500 cycles. Zylon exhibited a more stable and lower elongation of less than 3%.

Movement in bio-inspired robots typically relies on the use of a series of actuators and transmissions with one or more degrees of freedom (DOF), allowing asymmetrical ellipsoidal gaits for use in walking, running, swimming, and crawling. In an effort to simplify these multi-component systems, we present a novel, modular, soft, bi-stable, one DOF dome actuator platform that is capable of complex gaits through mechanical programming, driven by simple periodic fluid input. With a modular, reconfigurable design, the end effectors of these bi-stable dome actuators can be quickly modified for use on a variety of surfaces for specific applications. In the present study, we describe the finite element modeling, manufacturing, and characterization of different end effectors and outline a workflow for the implementation of these soft bi-stable dome actuators for the production of functional robotic prototypes.

We present a novel underactued humanoid five finger soft hand, the KIT Finger-Vision Soft Hand, which is equipped with cameras in the fingertips and integrates a high performance embedded system for visual processing and control. We describe the actuation mechanism of the hand and the tendon-driven soft finger design with internally routed high-bandwidth flat-flex cables. For efficient on-board parallel processing of visual data from the cameras in each fingertip, we present a hybrid embedded architecture consisting of a field programmable logic array (FPGA) and a microcontroller that allows the realization of visual object segmentation based on convolutional neural networks. We evaluate the hand design by conducting durability experiments with one finger and quantify the grasp performance in terms of grasping force, speed and grasp success. The results show that the hand exhibits a grasp force of 31.8 ± 1.2 N and a mechanical durability of the finger of more than 15.000 closing cycles. Finally, we evaluate the accuracy of visual object segmentation during the different phases of the grasping process using five different objects. Hereby, an accuracy above 90% can be achieved.

Automated driving requires highly precise and robust vehicle state estimation for its environmental perception, motion planning and control functions. Using GPS and environmental sensors can compensate for the deficits of the estimation based on traditional vehicle dynamics sensors. However, each type of sensor has specific strengths and limitations in accuracy and robustness due to their different properties regarding the quality of detection and robustness in diverse environmental conditions. For these reasons, we present a scalable concept for vehicle state estimation using an error-state extended Kalman filter (ESEKF) to fuse classical vehicle sensors with environmental sensors. The state variables, i.e., position, velocity and orientation, are predicted by a 6-degree-of-freedom (DoF) vehicle kinematic model that uses a low-cost inertial measurement unit (IMU) on a customer vehicle. The Error of the 6-DoF rigid body motion model is estimated using observations of global position using the global navigation satellite system (GNSS) and of the environment using radar, a camera and low-cost lidar. Our concept is scalable such that it is compatible with different sensor setups on different vehicle configurations. The experimental results compare various sensor combinations with measurement data in scenarios such as dynamic driving maneuvers on a test field. The results show that our approach ensures accuracy and robustness with redundant sensor data under regular and dynamic driving conditions.

In this paper we present a novel multi-stereo visual-inertial odometry (VIO) framework which aims to improve the robustness of a robot's state estimate during aggressive motion and in visually challenging environments. Our system uses a fixed-lag smoother which jointly optimizes for poses and landmarks across all stereo pairs. We propose a 1-point RANdom SAmple Consensus (RANSAC) algorithm which is able to perform outlier rejection across features from all stereo pairs. To handle the problem of noisy extrinsics, we account for uncertainty in the calibration of each stereo pair and model it in both our front-end and back-end. The result is a VIO system which is able to maintain an accurate state estimate under conditions that have typically proven to be challenging for traditional state-of-the-art VIO systems. We demonstrate the benefits of our proposed multi-stereo algorithm by evaluating it with both simulated and real world data. We show that our proposed algorithm is able to maintain a state estimate in scenarios where traditional VIO algorithms fail.

Creative design of grippers on their configurations, mechatronics control system, and multi-component collaborative algorithms is often utilized to realize complex operations in industrial applications, due to the environmental constraints or specific task requirements. Firstly, this paper introduces the background problems. As the main automatic equipment -- the shield machine -- in the field of tunnel boring construction, needs frequent tool (cutter) replacement during underground process, but has no practical automatic method yet, due to heavy payload, complex environment and work procedure. Thus, an integrated solution was proposed by developing a specific gripper and a snake-like manipulator to accomplish tool replacement in a cooperative way. Through simple and unique design of relative components, the solution realizes a fully automatic and precise approach including heavy load tool grasping and regrasping, posture adjustment, unlocking and disassembly, and installation and locking. Finally, this paper also describes the experimental process of tool replacement by the prototype under a real working condition, and discusses the feasibility of putting the scheme into practical application through comparison.

Full teleoperation of mobile robots during the execution of complex tasks not only demands high cognitive and physical effort but also generates less optimal trajectories compared to autonomous controllers. However, the use of the latter in cluttered and dynamically varying environments is still an open and challenging topic. This is due to several factors such as sensory measurement failures and rapid changes in task requirements. Shared-control approaches have been introduced to overcome these issues. However, these either present a strong decoupling that makes them still sensitive to unexpected events, or highly complex interfaces only accessible to expert users. In this work, we focus on the development of a novel and intuitive shared-control framework for target detection and control of mobile robots. The proposed framework merges the information coming from a teleoperation device with a stochastic evaluation of the desired goal to generate autonomous trajectories while keeping a human-in-control approach. This allows the operator to react in case of goal changes, sensor failures, or unexpected disturbances. The proposed approach is validated through several experiments both in simulation and in a real environment where the users try to reach a chosen goal in the presence of obstacles and unexpected disturbances. Operators receive both visual feedback of the environment and voice feedback of the goal estimation status while teleoperating a mobile robot through a control-pad. Results of the proposed method are compared to pure teleoperation proving a better time-efficiency and easiness-of-use of the presented approach.

This paper introduces a new technique for learning probabilistic models of mass and friction distributions of unknown objects, and performing robust sliding actions by using the learned models. The proposed method is executed in two consecutive phases. In the exploration phase, a table-top object is poked by a robot from different angles. The observed motions of the object are compared against simulated motions with various hypothesized mass and friction models. The simulation-to-reality gap is then differentiated with respect to the unknown mass and friction parameters, and the analytically computed gradient is used to optimize those parameters. Since it is difficult to disentangle the mass from the friction coefficients in low-data and quasi-static motion regimes, our approach retains a set of locally optimal pairs of mass and friction models. A probability distribution on the models is computed based on the relative accuracy of each pair of models. In the exploitation phase, a probabilistic planner is used to select a goal configuration and waypoints that are stable with a high confidence. The proposed technique is evaluated on real objects and using a real manipulator. The results show that this technique can not only identify accurately mass and friction coefficients of non-uniform heterogeneous objects, but can also be used to successfully slide an unknown object to the edge of a table and pick it up from there, without any human assistance or feedback.

Point cloud registration is a means of achieving loop closure correction within a simultaneous localization and mapping (SLAM) algorithm. Data association is a critical component in point cloud registration, and can be very challenging in feature-depleted environments such as seabed. This paper presents a point cloud registration pipeline for performing loop closure correction in feature-depleted subsea environments using data collected from an optical scanner. The pipeline uses Gaussian process regression to extract keypoint sets, and a weighted network alignment algorithm to propose point correspondences. A variant of the iterative closest point (ICP) registration algorithm is used to perform fine alignment, with point correspondences informed by the mappings determined following the network alignment step. The developed registration pipeline is deployed with success on a challenging section of field data containing topography that cannot be resolved using conventional imaging sonar.

Bilateral teleoperation systems allow the telepresence of an operator while working remotely. Such ability becomes crucial when dealing with critical environments like space, nuclear plants, rescue, and surgery. The main properties of a teleoperation system are the stability and the transparency which, in general, are in contrast and they cannot be fully achieved at the same time. In this paper, we will present a novel model predictive controller that implements a passivity-based bilateral teleoperation algorithm. Our solution mitigates the chattering issue arising when resorting to the energy tank (or reservoir) mechanism by forcing the passivity as a hard constraint on the system evolution.

Drivers and other road users often encounter situations (e.g., arriving at an intersection simultaneously) where priority is ambiguous or unclear but must be resolved via communication to reach agreement. This poses a challenge for autonomous vehicles, for which no direct means for expressing intent and acknowledgment has yet been established. This paper contributes a minimal model to manage ambiguity and produce actions that are expressive and encode aspects of intent. Specifically, intent is treated as a latent variable, communicated implicitly through a partially observable Markov decision process (POMDP). We validate the model in a simple setting: a simulation of a prototypical crossing with a vehicle and one pedestrian at an unsignalized intersection. We further report use of our self-driving Ford Lincoln MKZ platform, through which we conducted experimental trials of the method involving real-time interaction. The experiment shows the method achieves safe and efficient navigation.

Trajectory optimization problems under affine motion model and convex cost function are often solved through the convex-concave procedure (CCP), wherein the non-convex collision avoidance constraints are replaced with its affine approximation. Although mathematically rigorous, CCP has some critical limitations. First, it requires a collision-free initial guess of solution trajectory which is difficult to obtain, especially in dynamic environments. Second, at each iteration, CCP involves solving a convex constrained optimization problem which becomes prohibitive for real-time computation even with a moderate number of obstacles, if long planning horizons are used.In this paper, we propose a novel trajectory optimizer which like CCP involves solving convex optimization problems but can work with an arbitrary initial guess. Moreover, the proposed optimizer can be computationally upto a few orders of magnitude faster than CCP while achieving similar or better optimal cost. The reduced computation time, in turn, stems from some interesting mathematical structures in the optimizer which allows for distributed computation and obtaining solutions in symbolic form. We validate our claims on difficult benchmarks consisting of static and dynamic obstacles.

In this study, we address the inverse kinematics problem for an upper-limb exoskeleton by presenting a novel method that guarantees the satisfaction of joint-space constraints, and solves closed-chain mechanisms in a serial robot configuration. Starting from the conventional differential kinematics method based on the inversion of the Jacobian matrix, we describe and test two improved algorithms based on the Projected-Gradient method, that take into account joint-space equality constraints. We use the Harmony exoskeleton as a platform to demonstrate the method. Specifically, we address the joint constraints that the robot maintains in order to match anatomical shoulder movement and the closed-chain mechanisms used for the robot's joint control. Results show good performances of the proposed algorithms, which are confirmed by the ability of the robot to follow the desired task-space trajectory while ensuring the fulfilment of joint-space constraints, with a maximum error of about 0.05 degrees.

Gastrointestinal (GI) endoscopy is a conventional and prevalent procedure used to diagnose and treat diseases in the digestive tract. This procedure requires inserting an endoscope equipped with a camera and instruments inside a patient to the target of interest. To manoeuvre the endoscope, an endoscopist would rotate the knob at the handle to change the direction of the distal tip and apply the feeding force to advance the endoscope. However, due to the nature of the design, this often causes a looping problem during insertion making it difficult to be further advanced to the deeper section of the tract such as the transverse and ascending colon. To this end, in this paper, we propose a novel robotic endoscope which is covered by a rotating screw-like sheath and uses a spiral insertion technique to generate 'pull' forces at the distal tip of the endoscope to facilitate insertion. The whole shaft of the endoscope can be actively rotated, providing the crawling ability from the attached spiral sheath. With the redundant control on a spring-like continuum joint, the bending tip is capable of maintaining its orientation to assist endoscope navigation. To test its functions and feasibility to address the looping problem, three experiments were carried out. The first two experiments were to analyse the kinematic of the device and test the ability of the device to hold its distal tip at different orientation angles during spiral insertion. In the third experiment, we inserted the device in the bent colon phantom to evaluate the effectiveness of the proposed design against looping when advancing through a curved section of a colon. Results show the moving ability using spiral technique and verify its potential of clinical application.

To obtain synchronized gait assistance, this paper presents a new delayless adaptive dual-oscillator (ADO) scheme to address the inherent delay issue. In the ADO structure, a new oscillator is coupled with the primitive one but the phase is adaptively feed-forward compensated. It’s remarkable that the compensated phase is determined by the proposed extended phase lag observer, in which both the phase lag and phase leading can be properly estimated and eliminated in the steady and non-steady gait. Moreover, a unified exoskeleton control scheme based on ADO is further proposed to improve the gait segmentation, velocity/acceleration estimation, intention estimation, and assistance generation performances, which further enhances the assistance synergy and reduces the safety risks. Experimental results demonstrate better alignment assistance and consequently reduced muscle efforts with ADO-based assistance control.