Showing papers by "Giuseppe Loianno published in 2018"

Cooperative Transportation Using Small Quadrotors Using Monocular Vision and Inertial Sensing

Abstract: Micro aerial vehicles have the potential to assist humans in tasks such as manipulation and transportation for construction and humanitarian missions, beyond simply acquiring data and building maps. In this letter, we address the state estimation, control, and trajectory planning in cooperative transportation of structures, which are either too heavy or too big to be carried by small microvehicles. Specifically, we consider small quadrotors, each equipped only with a single camera and inertial measurement unit as a sensor. The key contributions are 1) a new approach to coordinated control, which allows independent control of each vehicle while guaranteeing the system's stability and 2) a new cooperative localization scheme that allows each vehicle to benefit from measurements acquired by other vehicles. The latter relies on the vehicles exploiting the inherent rigid structure information to infer additional constraints between the vehicles’ poses allowing us to formulate the pose estimation problem as an optimization problem on the Lie group $\mathbf {SE(3)}$ . The proposed approach is validated through experimental results with multiple quadrotors grasping and transporting a rigid structure.

Fast, autonomous flight in GPS-denied and cluttered environments

Abstract: Author(s): Mohta, K.; Mulgaonkar, Y.; Watterson, M.; Liu, S.; Qu, C.; Makineni, A.; Saulnier, K.; Sun, K.; Zhu, A.; Delmerico, J.; Karydis, K.; Atanasov, N.; Loianno, G.; Scaramuzza, D.; Daniilidis, K.; Taylor, C. J.; Kumar, V.

Model Predictive Trajectory Tracking and Collision Avoidance for Reliable Outdoor Deployment of Unmanned Aerial Vehicles

Abstract: We propose a novel approach for optimal trajectory tracking for unmanned aerial vehicles (UAV), using a linear model predictive controller (MPC) in combination with non-linear state feedback. The solution relies on fast onboard simulation of the translational dynamics of the UAV, which is guided by a linear MPC. By sampling the states of the virtual UAV, we create a control command for fast non-linear feedback, which is capable of performing agile maneuvers with high precision. In addition, the proposed pipeline provides an interface for a decentralized collision avoidance system for multi-UAY scenarios. Our solution makes use of the long prediction horizon of the linear MPC and allows safe outdoors execution of multi-UAV experiments without the need for in-advance collision-free planning. The practicality of the tracking mechanism is shown in combination with priority-based collision resolution strategy, which performs sufficiently in experiments with up to 5 UAVs. We present a statistical and experimental evaluation of the platform in both simulation and real-world examples, demonstrating the usability of the approach.

Visual Inertial Odometry Swarm: An Autonomous Swarm of Vision-Based Quadrotors

Abstract: In this letter, we present the system infrastructure for a swarm of quadrotors, which perform all estimation on board using monocular visual inertial odometry. This is a novel system since it does not require an external motion capture system or GPS and is able to execute formation tasks without inter-robot collisions. The swarm can be deployed in nearly any indoor or outdoor scenario and is scalable to higher numbers of robots. We discuss the system architecture, estimation, planning, and control for the multirobot system. The robustness and scalability of the approach is validated in both indoor and outdoor environments with up to 12 quadrotors.

Localization, Grasping, and Transportation of Magnetic Objects by a team of MAVs in Challenging Desert like Environments

Abstract: Autonomous Micro Aerial Vehicles (MAVs) have the potential to assist in real-life tasks involving grasping and transportation, but not before solving several difficult research challenges. In this work, we address the design, control, estimation, and planning problems for cooperative localization, grasping, and transportation of objects in challenging outdoor scenarios. We demonstrate an autonomous team of MAVs able to plan safe trajectories for manipulation of ferrous objects, while guaranteeing interrobot collision avoidance and automatically creating a map of the objects in the environment. Our solution is predominantly distributed, allowing the team to pick and transport ferrous disks to a final destination without collisions. This result is achieved using a new magnetic gripper with a novel feedback approach, enabling the detection of successful grasping. The gripper design and all the components to build a platform are clearly provided as open-source hardware for reuse by the community. Finally, the proposed solution is validated through experimental results, where difficulties include inconsistent wind, uneven terrain, and sandy conditions.

Autonomous navigation of micro aerial vehicles using high-rate and low-cost sensors

Abstract: The combination of visual and inertial sensors for state estimation has recently found wide echo in the robotics community, especially in the aerial robotics field, due to the lightweight and complementary characteristics of the sensors data. However, most state estimation systems based on visual-inertial sensing suffer from severe processor requirements, which in many cases make them impractical. In this paper, we propose a simple, low-cost and high rate method for state estimation enabling autonomous flight of micro aerial vehicles, which presents a low computational burden. The proposed state estimator fuses observations from an inertial measurement unit, an optical flow smart camera and a time-of-flight range sensor. The smart camera provides optical flow measurements up to a rate of 200 Hz, avoiding the computational bottleneck to the main processor produced by all image processing requirements. To the best of our knowledge, this is the first example of extending the use of these smart cameras from hovering-like motions to odometry estimation, producing estimates that are usable during flight times of several minutes. In order to validate and defend the simplest algorithmic solution, we investigate the performances of two Kalman filters, in the extended and error-state flavors, alongside with a large number of algorithm modifications defended in earlier literature on visual-inertial odometry, showing that their impact on filter performance is minimal. To close the control loop, a non-linear controller operating in the special Euclidean group SE(3) is able to drive, based on the estimated vehicle’s state, a quadrotor platform in 3D space guaranteeing the asymptotic stability of 3D position and heading. All the estimation and control tasks are solved on board and in real time on a limited computational unit. The proposed approach is validated through simulations and experimental results, which include comparisons with ground-truth data provided by a motion capture system. For the benefit of the community, we make the source code public.

Special Issue on High‐Speed Vision‐Based Autonomous Navigation of UAVs

Abstract: This first special issue of the Journal of Field Robotics (JFR) on vision-based high speed autonomous navigation of UAVs aims to establish a baseline in the field of autonomous navigation of UAVs using vision and IMU as the main sensing modalities. The goal of the research reported in this special issue is to show the improvements and present the most recent state of the art to execute fast autonomous operations with MAVs. The proposed approaches will contribute to inform the community with the most recent and innovative approaches and to extend the capabilities of current and future robotic missions.

Nuclear Environments Inspection with Micro Aerial Vehicles: Algorithms and Experiments

Abstract: In this work, we address the estimation, planning, control and mapping problems to allow a small quadrotor to autonomously inspect the interior of hazardous damaged nuclear sites. These algorithms run onboard on a computationally limited CPU. We investigate the effect of varying illumination on the system performance. To the best of our knowledge, this is the first fully autonomous system of this size and scale applied to inspect the interior of a full scale mock-up of a Primary Containment Vessel (PCV). The proposed solution opens up new ways to inspect nuclear reactors and to support nuclear decommissioning, which is well known to be a dangerous, long and tedious process. Experimental results with varying illumination conditions show the ability to navigate a full scale mock-up PCV pedestal and create a map of the environment, while concurrently avoiding obstacles.

Inertial Velocity and Attitude Estimation for Quadrotors

Abstract: This work addresses the design and implementation of a filter that estimates the orientation of the body-fixed $z$ axis and the velocity of a quadrotor UAV from the inertial measurement unit (IMU)given a known yaw. The velocity and attitude estimation is possible since the filter employs a linear drag model measuring the drag forces on the quadrotor through the IMU. These forces are functions of the robot's velocity and attitude. In addition, the filter estimates the linear drag parameters and thrust coefficient for the propellers. These parameters may be fed back into a controller to improve tracking performance. Experimental results are used to validate the proposed approach.

Spatio-Temporally Smooth Local Mapping and State Estimation Inside Generalized Cylinders With Micro Aerial Vehicles

Abstract: In this letter, we consider state estimation and local mapping with a micro aerial vehicle inside a tunnel that can be modeled as a generalized cylinder, using a three-dimensional lidar and an inertial measurement unit. This axisymmetric environment poses unique challenges in terms of localization and mapping. The point cloud data returned by the sensor consists of indiscriminate partial cylindrical patches complicating data association. The proposed method reconstructs an egocentric local map through an optimization process on a nonlinear manifold, which is then fed into a constrained unscented Kalman filter. The proposed method easily adapts to different diameters, cross sections, and changes in center line curves. The proposed approach outperforms our previous contribution [T. Ozaslan, G. Loianno, J. Keller, C. J. Taylor, V. Kumar, J. M. Wozencraft, and T. Hood, “Autonomous navigation and mapping for inspection of penstocks and tunnels with MAVs,” IEEE Robotics Automation Letter , vol. 2, no. 3, pp. 1740–1747, Jul. 2017] in terms of mapping quality and robustness to noncylindrical cross sections. Our motivation is to automate the labor intensive, dangerous, and the expensive inspection of penstocks with the least operator intervention. We present experimental results obtained in Center Hill Dam, TN, USA, to validate the proposed approach.

U-Net for MAV-based Penstock Inspection: an Investigation of Focal Loss in Multi-class Segmentation for Corrosion Identification

Abstract: Periodical inspection and maintenance of critical infrastructure such as dams, penstocks, and locks are of significant importance to prevent catastrophic failures Conventional manual inspection methods require inspectors to climb along a penstock to spot corrosion, rust and crack formation which is unsafe, labor-intensive, and requires intensive training This work presents an alternative approach using a Micro Aerial Vehicle (MAV) that autonomously flies to collect imagery which is then fed into a pretrained deep-learning model to identify corrosion Our simplified U-Net trained with less than 40 image samples can do inference at 12 fps on a single GPU We analyze different loss functions to solve the class imbalance problem, followed by a discussion on choosing proper metrics and weights for object classes Results obtained with the dataset collected from Center Hill Dam, TN show that focal loss function, combined with a proper set of class weights yield better segmentation results than the base loss, Softmax cross entropy Our method can be used in combination with planning algorithm to offer a complete, safe and cost-efficient solution to autonomous infrastructure inspection

Semi-Dense Visual-Inertial Odometry and Mapping for Quadrotors with SWAP Constraints

Abstract: Micro Aerial Vehicles have the potential to assist humans in real life tasks involving applications such as smart homes, search and rescue, and architecture construction. To enhance autonomous navigation capabilities these vehicles need to be able to create dense 3D maps of the environment, while concurrently estimating their own motion. In this paper, we are particularly interested in small vehicles that can navigate cluttered indoor environments. We address the problem of visual inertial state estimation, control and 3D mapping on platforms with Size, Weight, And Power (SWAP) constraints. The proposed approach is validated through experimental results on a 250 g, 22 cm diameter quadrotor equipped only with a stereo camera and an IMU with a computationally-limited CPU showing the ability to autonomously navigate, while concurrently creating a 3D map of the environment.

Autonomous flight and cooperative control for reconstruction using aerial robots powered by smartphones

Abstract: Advances in consumer electronics products and the technology seen in personal computers, digital cameras, and smartphones phones have led to the price/performance ratio of sensors and processors fa...

Inertial Velocity and Attitude Estimation for Quadrotors: Supplementary Material

Abstract: Inertial Velocity and Attitude Estimation for Quadrotors: Supplementary Material James B. Svacha Jr University of Pennsylvania, jsvacha@seas.upenn.edu Kartik Mohta University of Pennsylvania, kmohta@seas.upenn.edu Michael Watterson University of Pennsylvania, wami@seas.upenn.edu Giuseppe Loianno University of Pennsylvania, loiannog@seas.upenn.edu Vijay Kumar University of Pennsylvania, kumar@seas.upenn.edu