ICRA 2019 Papers

In this paper the first robot-based prototype of a semi-autonomous upper-limb exoprosthesis is introduced, unifying exoskeletons and prostheses [1]. A central goal of this work is to minimize unnecessary interaction forces on the residual limb by compensating gravity effects via a upper body grounded exoskeleton. Furthermore, the exoskeleton provides the residual limb's kinematic data that allows to design more intelligent coordinated control concepts. The soft-robotics design of a prototype consisting of a transhumeral prosthesis and a robot-based exoskeleton substitute is outlined. For this class of hybrid systems a human embodied dynamics model and semi-autonomous coordinated motion strategies are derived. Here, in contrast to established standard sequential strategies all joints are moved simultaneously according to a desired task. In combination with an app-based programming framework the strategy goals are set either user-based via kinesthetic teaching or autonomously via 3D visual perception. This enables the user to execute tasks faster and more intuitive. First experimental evaluations show promising performance with a healthy subject.

Advances in sensor technologies, object detection algorithms, planning frameworks and hardware designs have motivated the deployment of robots in warehouse automation. A variety of such applications, like order fulfillment or packing tasks, require picking objects from unstructured piles and carefully arranging them in bins or containers. Desirable solutions need to be low-cost, easily deployable and controllable, making minimalistic hardware choices desirable. The challenge in designing an effective solution to this problem relates to appropriately integrating multiple components, so as to achieve a robust pipeline that minimizes failure conditions. The current work proposes a complete pipeline for solving such packing tasks, given access only to RGB-D data and a single robot arm with a vacuum-based end-effector, which is also used as a pushing finger. To achieve the desired level of robustness, three key manipulation primitives are identified, which take advantage of the environment and simple operations to successfully pack multiple cubic objects. The overall approach is demonstrated to be robust to execution and perception errors. The impact of each manipulation primitive is evaluated by considering different versions of the proposed pipeline, which incrementally introduce reasoning about object poses and corrective manipulation actions.

Nowadays, robots are expected to enter in various application scenarios and interact with unknown and dynamically changing environments. This highlights the need for creating autonomous robot behaviours to explore such environments, identify their characteristics and adapt, and build knowledge for future interactions. To respond to this need, in this paper we present a novel framework that integrates multiple components to achieve a context-aware and adaptive interaction between the robot and uncertain environments. The core of this framework is a novel self-tuning impedance controller that regulates robot quasi-static parameters, i.e., stiffness and damping, based on the robot sensory data and vision. The tuning of the parameters is achieved only in the direction(s) of interaction or movement, by distinguishing expected interactions from external disturbances. A vision module is developed to recognize the environmental characteristics and to associate them to the previously/newly identified interaction parameters, with the robot always being able to adapt to the new changes or unexpected situations. This enables a faster robot adaptability, starting from better initial interaction parameters. The framework is evaluated experimentally in an agricultural task, where the robot effectively interacts with various deformable environments.

Ground robots which are able to navigate a variety of terrains are needed in many domains. One of the key aspects is the capability to adapt to the ground structure, which can be realized through movable body parts coming along with additional degrees of freedom (DoF). However, planning respective locomotion is challenging since suitable representations result in large state spaces. Employing an additional abstract representation-which is coarser, lower-dimensional, and semantically enriched-can support the planning. While a desired robot representation and action set of such an abstract representation can be easily defined, the cost function requires large tuning efforts. We propose a method to represent the cost function as a CNN. Training of the network is done on generated artificial data, while it generalizes well to the abstraction of real world scenes. We further apply our method to the problem of search-based planning of hybrid driving-stepping locomotion. The abstract representation is used as a powerful informed heuristic which accelerates planning by multiple orders of magnitude.

We propose a fully dense direct filter-based visual-inertial odometry method estimating both pixel depth for all pixels and robot state simultaneously, having all uncertainties in the same state vector. Due to the fully dense method, our approach works even in low-textured areas with very low, smooth gradients (i.e. scenes where feature based or semi-dense approaches fail). Our algorithm performs in real-time on a CPU with a time complexity linearly dependent on the amount of pixels in the provided image. To achieve this, we propose complexity reduction methods for fast matrix inversion, exploiting specific structures of the covariance matrix. We provide both simulated and real-world results in low-textured areas with a smooth gradient.

The sense of touch is arguably the first human sense to develop. Empowering robots with the sense of touch may augment their understanding of interacted objects and the environment beyond standard sensory modalities (e.g., vision). This paper investigates the effect of hybridizing touch and sliding movements for tactile-based texture classification. We develop three machine-learning methods within a framework to discriminate between surface textures; the first two methods use hand-engineered features, whilst the third leverages convolutional and recurrent neural network layers to learn feature representations from raw data. To compare these methods, we constructed a dataset comprising tactile data from 23 textures gathered using the iCub platform under a loosely constrained setup, i.e., with nonlinear motion. In line with findings from neuroscience, our experiments show that a good initial estimate can be obtained via touch data, which can be further refined via sliding; combining both touch and sliding data results in 98% classification accuracy over unseen test data.

In this paper, we show how a planning algorithm can be used to automatically create and update a Behavior Tree (BT), controlling a robot in a dynamic environment. The planning part of the algorithm is based on the idea of back chaining. Starting from a goal condition we iteratively select actions to achieve that goal, and if those actions have unmet preconditions, they are extended with actions to achieve them in the same way. The fact that BTs are inherently modular and reactive makes the proposed solution blend acting and planning in a way that enables the robot to effectively react to external disturbances. If an external agent undoes an action the robot reexecutes it without re-planning, and if an external agent helps the robot, it skips the corresponding actions, again without replanning. We illustrate our approach in two different robotics scenarios.

The current standard of intra-operative navigation during Fenestrated Endovascular Aortic Repair (FEVAR) calls for the need of 3D alignments between inserted devices and aortic branches. The navigation commonly via 2D fluoroscopic images, lacks anatomical information, resulting in longer operation hours and radiation exposure. In this paper, a skeleton instantiation framework of Abdominal Aortic Aneurysm (AAA) from a single 2D fluoroscopic image is introduced for real-time 3D robotic path planning. A graph matching method is proposed to establish the correspondences between the 3D preoperative and 2D intra-operative AAA skeletons, and then the two skeletons are registered by skeleton deformation and regularization in respect to skeleton length and smoothness. Furthermore, deep learning was used to segment 3D preoperative AAA from Computed Tomography (CT) scans to facilitate the framework automation. Simulation, phantom and patient AAA data sets have been used to validate the proposed framework. 3D distance error of 2mm was achieved in the phantom setup. Performance advantages were also achieved in terms of accuracy, robustness and time-efficiency.

This paper is an initial step toward the realization of an aerial robot that can perform lateral physical work, such as drilling a hole or fastening a screw in a wall. Aerial robots are capable of high maneuverability and can provide access to locations that would be difficult or impossible for ground-based robots to reach. However, to fully utilize this mobility, systems would ideally be able to perform functional work in those locations, requiring the ability to exert lateral forces. To substantially improve a hovering vehicle's ability to stably deliver large lateral forces, we propose the use of a versatile suction-based gripper that can establish pulling contact on featureless surfaces. Such contact enables access to environmental forces that can be used to further stabilize the vehicle and also increase the lateral force delivered to the surface through a possible secondary mechanism. This paper introduces the concept, describes the design of a new self-sealing suction cup based on a previous design, details the design of a gripper using those cups, and describes the arm and flight vehicle. It then evaluates the cup and gripper performance in several ways, culminating in physical grasping demonstrations using the arm and gripper, including one in the presence of simulated flight noise based on data from preliminary indoor flight experiments.

We report the feasibility study of a new acoustic and optical bi-modal distance & material sensor for robotic grasping. The new sensor is designed to be mounted on the robot fingertip to provide last-moment perception before contact happens. It is based on both pulse-echo ultrasound and optoacoustic effects enabled by single-element air-coupled transducers. In contrast to conventional contact-based and recent pre-touch approaches, this new method overcomes their disadvantages and provides robotic fingers with the capability to detect the distance and material type of the target at a near distance before contact occurs, which is crucial for robust and nimble grasping. The proposed sensor has been tested with different materials, shapes, and porous properties. The experimental results show that this sensor design is functional and practical.

Jumping can be an effective way of locomotion to overcome small terrain gaps or obstacles. In this paper we propose two different approaches to perform jumps with a humanoid robot. Specifically, starting from a pre-defined CoM trajectory we develop the theory for a velocity controller and for a torque controller based on an optimization technique for the evaluation of the joints input. The controllers have been tested both in simulation and on the humanoid robot iCub. In simulation the robot was able to jump using both controllers, while the real system jumped with the velocity controller only. The results highlight the importance of controlling the centroidal angular momentum and they suggest that the joint performances, namely maximum power, of the legs and torso joints, and the low level control performances are fundamental to achieve acceptable results.

MacGyvering is defined as creating or repairing something in an inventive or improvised way by utilizing objects that are available at hand. In this paper, we explore a subset of Macgyvering problems involving tool construction, i.e., creating tools from parts available in the environment. We formalize the overall problem domain of tool Macgyvering, introducing three levels of complexity for tool construction and substitution problems, and presenting a novel computational framework aimed at solving one level of the tool Macgyvering problem, specifically contributing a novel algorithm for tool construction based on geometric reasoning. We validate our approach by constructing three tools using a 7-DOF robot arm.

This paper presents a tightly-coupled aided inertial navigation system (INS) with point and plane features, a general sensor fusion framework applicable to any visual and depth sensor (e.g., RGBD, LiDAR) configuration, in which the camera is used for point feature tracking and depth sensor for plane extraction. The proposed system exploits geometrical structures (planes) of the environments and adopts the closest point (CP) for plane parameterization. Moreover, we distinguish planar point features from non-planar point features in order to enforce point-on-plane constraints which are used in our state estimator, thus further exploiting structural information from the environment. We also introduce a simple but effective plane feature initialization algorithm for feature-based simultaneous localization and mapping (SLAM). In addition, we perform online spatial calibration between the IMU and the depth sensor as it is difficult to obtain this critical calibration parameter in high precision. Both Monte-Carlo simulations and real-world experiments are performed to validate the proposed approach.

Ego-motion estimation is a fundamental requirement for most mobile robotic applications. By sensor fusion, we can compensate the deficiencies of stand-alone sensors and provide more reliable estimations. We introduce a tightly coupled lidar-IMU fusion method in this paper. By jointly minimizing the cost derived from lidar and IMU measurements, the lidarIMU odometry (LIO) can perform well with considerable drifts after long-term experiment, even in challenging cases where the lidar measurement can be degraded. Besides, to obtain more reliable estimations of the lidar poses, a rotation-constrained refinement algorithm (LIO-mapping) is proposed to further align the lidar poses with the global map. The experiment results demonstrate that the proposed method can estimate the poses of the sensor pair at the IMU update rate with high precision, even under fast motion conditions or with insufficient features.

Three-dimensionally (3D) maneuverable robotic fish are highly desirable due to their abilities to explore and survey the underwater environment. Existing depth control mechanism is focused on using compressed air or piston to generate volume change, which makes the system bulky and impractical in a small size underwater robot. In this paper, a small and compact 3D maneuverable robotic fish is developed. Instead of using a compressed air tank, the robot is equipped with an on-board water electrolyzer to generate the gases for depth change. The fabricated robotic fish shows fast diving and rising performance. A servo motor is used to generate asymmetric flapping motion on the caudal fin, which leads to a two-dimensionally (2D) planar motion. A 3D dynamic model is then derived for the fabricated robotic fish. Several open-loop control experiments have been conducted to validate the model as well as the design. It has been demonstrated in the experimental results that the robot is capable of generating 3D motion. The robot can achieve 0.13 m/s forward velocity, 30.6 degree/s turning rate, and it takes about 5.5 s to dive to 0.55 m and 10 s to rise.

This work proposes a new strategy for autonomous navigation of mobile robots in sugarcane plantations based on thermal imaging. Unlike ordinary agricultural fields, sugarcane farms are generally vast and accommodates numerous arrangements of row crop tunnels, which are very tall, dense and hard-to-access. Moreover, sugarcane crops lie in harsh regions, which hinder the logistics for employing staff and heavy machinery for mapping, monitoring, and sampling. One solution for this problem is TIBA (Tankette for Intelligent BioEnergy Agriculture), a low-cost skid-steering mobile robot capable of infiltrating the crop tunnels with several sensing/sampling systems. The project concept is to reduce the product cost for making the deployment of a robot swarm feasible over a larger area. A prototype was built and tested in a bioenergy farm in order to improve the understanding of the environment and bring about the challenges for the next development steps. The major problem is the navigation through the crop tunnels, since most of the developed systems are suitable for open field operations and employ laser scanners and/or GPS/IMU, which in general are expensive technologies. In this context, we propose a low-cost solution based on infrared (IR) thermal imaging. IR cameras are simple and inexpensive devices, which do not pose risks to the user health, unlike laser-based sensors. This idea was highly motivated by the data collected in the field, which have shown a significant temperature difference between the ground and the crop. From the image analysis, it is possible to clearly visualize a distinguishable corridor and, consequently, generate a straight path for the robot to follow by using computationally efficient approaches. A rigorous analysis of the collected thermal data, numerical simulations and preliminary experiments in the real environment were included to illustrate the efficiency and feasibility of the proposed navigation methodology. Show More

The stochastic Canadian Traveler Problem (CTP), which finds application in robot route selection under uncertainty, aims to find the traversal policy with the minimum expected cost. This paper extends the CTP to what we call the Robust Canadian Traveler Problem (RCTP), in which the variability of the policy cost is also part of the evaluation criteria. An optimal (offline) algorithm and an approximate (online) algorithm are then proposed to compute the policy that has a good balance of both mean and variation of the traversal cost. The benefit of the proposed framework versus traditional approaches is shown by doing simulations in randomly generated worlds as well as on a map of 5 km of paths built from robot field trials. Specifically, the RCTP framework is able to search for sub-optimal policy alternatives with significantly lower worst-case cost and less computational time compared to the optimal policy, but with little sacrifice on the expected cost.

In this paper, we introduce the Phoenix drone: the first completely open-source tail-sitter micro aerial vehicle (MAV) platform. The vehicle has a highly versatile, dual-rotor design and is engineered to be low-cost and easily extensible/modifiable. Our open-source release includes all of the design documents, software resources, and simulation tools needed to build and fly a high-performance tail-sitter for research and educational purposes.The drone has been developed for precision flight with a high degree of control authority. Our design methodology included extensive testing and characterization of the aerodynamic properties of the vehicle. The platform incorporates many off-the-shelf components and 3D-printed parts, in order to keep the cost down. Nonetheless, the paper includes results from flight trials which demonstrate that the vehicle is capable of very stable hovering and accurate trajectory tracking.Our hope is that the open-source Phoenix reference design will be useful to both researchers and educators. In particular, the details in this paper and the available open-source materials should enable learners to gain an understanding of aerodynamics, flight control, state estimation, software design, and simulation, while experimenting with a unique aerial robot.

In this paper we describe the Open Vision Computer (OVC) which was designed to support high speed, vision guided autonomous drone flight. In particular our aim was to develop a system that would be suitable for relatively small-scale flying platforms where size, weight, power consumption and computational performance were all important considerations. This manuscript describes the primary features of our OVC system and explains how they are used to support fully autonomous indoor and outdoor exploration and navigation operations on our Falcon 250 quadrotor platform.