Minimum-Time Quadrotor Waypoint Flight in Cluttered Environments

We tackle the problem of planning a minimum-time trajectory for a quadrotor over a sequence of specified waypoints in the presence of obstacles while exploiting the full quadrotor dynamics. This problem is crucial for autonomous search and rescue and drone racing scenarios but was, so far, unaddressed by the robotics community \emph{in its entirety} due to the challenges of minimizing time in the presence of the non-convex constraints posed by collision avoidance. Early works relied on simplified dynamics or polynomial trajectory representations that did not exploit the full actuator potential of a quadrotor and, thus, did not aim at minimizing time. We address this challenging problem by using a hierarchical, sampling-based method with an incrementally more complex quadrotor model. Our method first finds paths in different topologies to guide subsequent trajectory search for a kinodynamic point-mass model. Then, it uses an asymptotically-optimal, kinodynamic sampling-based method based on a full quadrotor model on top of the point-mass solution to find a feasible trajectory with a time-optimal objective. The proposed method is shown to outperform all related baselines in cluttered environments and is further validated in real-world flights at over 60km/h in one of the world's largest motion capture systems. We release the code open source.Comment: Accepted in IEEE Robotics and Automation Letter

CTopPRM: Clustering Topological PRM for Planning Multiple Distinct Paths in 3D Environments

In this paper, we propose a new method called Clustering Topological PRM (CTopPRM) for finding multiple homotopically distinct paths in 3D cluttered environments. Finding such distinct paths, e.g., going around an obstacle from a different side, is useful in many applications. Among others, using multiple distinct paths is necessary for optimization-based trajectory planners where found trajectories are restricted to only a single homotopy class of a given path. Distinct paths can also be used to guide sampling-based motion planning and thus increase the effectiveness of planning in environments with narrow passages. Graph-based representation called roadmap is a common representation for path planning and also for finding multiple distinct paths. However, challenging environments with multiple narrow passages require a densely sampled roadmap to capture the connectivity of the environment. Searching such a dense roadmap for multiple paths is computationally too expensive. Therefore, the majority of existing methods construct only a sparse roadmap which, however, struggles to find all distinct paths in challenging environments. To this end, we propose the CTopPRM which creates a sparse graph by clustering an initially sampled dense roadmap. Such a reduced roadmap allows fast identification of homotopically distinct paths captured in the dense roadmap. We show, that compared to the existing methods the CTopPRM improves the probability of finding all distinct paths by almost 20% in tested environments, during same run-time. The source code of our method is released as an open-source package.Comment: in IEEE Robotics and Automation Letter

Learning Minimum-Time Flight in Cluttered Environments

We tackle the problem of minimum-time flight for a quadrotor through a sequence of waypoints in the presence of obstacles while exploiting the full quadrotor dynamics. Early works relied on simplified dynamics or polynomial trajectory representations that did not exploit the full actuator potential of the quadrotor, and, thus, resulted in suboptimal solutions. Recent works can plan minimum-time trajectories; yet, the trajectories are executed with control methods that do not account for obstacles. Thus, a successful execution of such trajectories is prone to errors due to model mismatch and in-flight disturbances. To this end, we leverage deep reinforcement learning and classical topological path planning to train robust neural-network controllers for minimum-time quadrotor flight in cluttered environments. The resulting neural network controller demonstrates significantly better performance of up to 19% over state-of-the-art methods. More importantly, the learned policy solves the planning and control problem simultaneously online to account for disturbances, thus achieving much higher robustness. As such, the presented method achieves 100% success rate of flying minimum-time policies without collision, while traditional planning and control approaches achieve only 40%. The proposed method is validated in both simulation and the real world

The MRS UAV System: Pushing the Frontiers of Reproducible Research, Real-world Deployment, and Education with Autonomous Unmanned Aerial Vehicles

We present a multirotor Unmanned Aerial Vehicle control (UAV) and estimation system for supporting replicable research through realistic simulations and real-world experiments. We propose a unique multi-frame localization paradigm for estimating the states of a UAV in various frames of reference using multiple sensors simultaneously. The system enables complex missions in GNSS and GNSS-denied environments, including outdoor-indoor transitions and the execution of redundant estimators for backing up unreliable localization sources. Two feedback control designs are presented: one for precise and aggressive maneuvers, and the other for stable and smooth flight with a noisy state estimate. The proposed control and estimation pipeline are constructed without using the Euler/Tait-Bryan angle representation of orientation in 3D. Instead, we rely on rotation matrices and a novel heading-based convention to represent the one free rotational degree-of-freedom in 3D of a standard multirotor helicopter. We provide an actively maintained and well-documented open-source implementation, including realistic simulation of UAV, sensors, and localization systems. The proposed system is the product of years of applied research on multi-robot systems, aerial swarms, aerial manipulation, motion planning, and remote sensing. All our results have been supported by real-world system deployment that shaped the system into the form presented here. In addition, the system was utilized during the participation of our team from the CTU in Prague in the prestigious MBZIRC 2017 and 2020 robotics competitions, and also in the DARPA SubT challenge. Each time, our team was able to secure top places among the best competitors from all over the world. On each occasion, the challenges has motivated the team to improve the system and to gain a great amount of high-quality experience within tight deadlines.Comment: 28 pages, 20 figures, submitted to Journal of Intelligent & Robotic Systems (JINT), for the provided open-source software see http://github.com/ctu-mr

One-year outcomes after transcatheter insertion of an interatrial shunt device for the management of heart failure with preserved ejection fraction

Background—Heart failure with preserved ejection fraction has a complex pathophysiology and remains a therapeutic challenge. Elevated left atrial pressure, particularly during exercise, is a key contributor to morbidity and mortality. Preliminary analyses have demonstrated that a novel interatrial septal shunt device that allows shunting to reduce the left atrial pressure provides clinical and hemodynamic benefit at 6 months. Given the chronicity of heart failure with preserved ejection fraction, evidence of longer-term benefit is required. Methods and Results—Patients (n=64) with left ventricular ejection fraction ≥40%, New York Heart Association class II–IV, elevated pulmonary capillary wedge pressure (≥15 mm Hg at rest or ≥25 mm Hg during supine bicycle exercise) participated in the open-label study of the interatrial septal shunt device. One year after interatrial septal shunt device implantation, there were sustained improvements in New York Heart Association class (P<0.001), quality of life (Minnesota Living with Heart Failure score, P<0.001), and 6-minute walk distance (P<0.01). Echocardiography showed a small, stable reduction in left ventricular end-diastolic volume index (P<0.001), with a concomitant small stable increase in the right ventricular end-diastolic volume index (P<0.001). Invasive hemodynamic studies performed in a subset of patients demonstrated a sustained reduction in the workload corrected exercise pulmonary capillary wedge pressure (P<0.01). Survival at 1 year was 95%, and there was no evidence of device-related complications. Conclusions—These results provide evidence of safety and sustained clinical benefit in heart failure with preserved ejection fraction patients 1 year after interatrial septal shunt device implantation. Randomized, blinded studies are underway to confirm these observations

MRS Drone: A Modular Platform for Real-World Deployment of Aerial Multi-Robot Systems

This paper presents a modular autonomous Unmanned Aerial Vehicle (UAV) platform called the Multi-robot Systems (MRS) Drone that can be used in a large range of indoor and outdoor applications. The MRS Drone features unique modularity with respect to changes in actuators, frames, and sensory configuration. As the name suggests, the platform is specially tailored for deployment within a MRS group. The MRS Drone contributes to the state-of-the-art of UAV platforms by allowing smooth real-world deployment of multiple aerial robots, as well as by outperforming other platforms with its modularity. For real-world multi-robot deployment in various applications, the platform is easy to both assemble and modify. Moreover, it is accompanied by a realistic simulator to enable safe pre-flight testing and a smooth transition to complex real-world experiments. In this manuscript, we present mechanical and electrical designs, software architecture, and technical specifications to build a fully autonomous multi UAV system. Finally, we demonstrate the full capabilities and the unique modularity of the MRS Drone in various real-world applications that required a diverse range of platform configurations.Comment: 49 pages, 39 figures, accepted for publication to the Journal of Intelligent & Robotic System

Utilizing longitudinal data in assessing all‐cause mortality in patients hospitalized with heart failure

Abstract Aims Risk stratification in patients with a new onset or worsened heart failure (HF) is essential for clinical decision making. We have utilized a novel approach to enrich patient level prognostication using longitudinally gathered data to develop ML‐based algorithms predicting all‐cause 30, 90, 180, 360, and 720 day mortality. Methods and results In a cohort of 2449 HF patients hospitalized between 1 January 2011 and 31 December 2017, we utilized 422 parameters derived from 151 451 patient exams. They included clinical phenotyping, ECG, laboratory, echocardiography, catheterization data or percutaneous and surgical interventions reflecting the standard of care as captured in individual electronic records. The development of predictive models consisted of 101 iterations of repeated random subsampling splits into balanced training and validation sets. ML models yielded area under the receiver operating characteristic curve (AUC‐ROC) performance ranging from 0.83 to 0.89 on the outcome‐balanced validation set in predicting all‐cause mortality at aforementioned time‐limits. The 1 year mortality prediction model recorded an AUC of 0.85. We observed stable model performance across all HF phenotypes: HFpEF 0.83 AUC, HFmrEF 0.85 AUC, and HFrEF 0.86 AUC, respectively. Model performance improved when utilizing data from more hospital contacts compared with only data collected at baseline. Conclusions Our findings present a novel, patient‐level, comprehensive ML‐based algorithm for predicting all‐cause mortality in new or worsened heart failure. Its robust performance across phenotypes throughout the longitudinal patient follow‐up suggests its potential in point‐of‐care clinical risk stratification

Propagation of onset and peak time of myocardial shortening in time of myocardial shortening in ischemic versus nonischemic cardiomyopathy: assessment by magnetic resonance imaging myocardial tagging

ObjectivesWe aimed to study the relation between onset and peak time of circumferential shortening and the direction of propagation of these parameters in both ischemic and nonischemic patients.BackgroundPeak time is often used to select patients for cardiac resynchronization therapy, whereas pacing influences only the onset times directly. Furthermore, it is unclear whether there is a consistent direction of propagation delay and whether this depends on the etiology.MethodsMagnetic resonance imaging myocardial tagging with high temporal resolution (14 ms) was applied to 29 patients (18 nonischemic, 11 ischemic) and 17 healthy control subjects. Time to onset (Tonset), to first peak (Tpeak,first), and to maximum peak (Tpeak,max) of circumferential shortening were determined. Three-dimensional vectors were calculated to denote the main direction of asynchrony.ResultsIn both patient groups, Tonsetshowed a significant positive relation with both Tpeak,firstand Tpeak,max; however, Tpeak,firstcorrelated considerably better with Tonsetthan did Tpeak,max(p < 0.0001 for nonischemic, and p < 0.01 for ischemic patients). Moreover, the relations between Tpeakand Tonsetwere stronger in the nonischemic patients than in the ischemic patients (p < 0.001). In nonischemic patients, the propagation of Tonsetwas consistently from septum to lateral wall. In the ischemic patients, however, no consistent direction of propagation was found. For both groups, the longitudinal propagation delays (between apex and base) were negligible compared with the short-axis delays.ConclusionsThe relation between peak time and onset time of shortening is strongest in nonischemic patients and is most consistent when time to first peak is used (instead of time to maximum peak)