We investigate how to utilize predictive models for selecting appropriate motion planning strategies based on perception uncertainty estimation for agile unmanned aerial vehicle (UAV) navigation tasks. Although there are variety of motion planning and perception algorithms for such tasks, the impact of perception uncertainty is not explicitly handled in many of the current motion algorithms, which leads to performance loss in real-life scenarios where the measurement are often noisy due to external disturbances. We develop a novel framework for embedding perception uncertainty to high level motion planning management, in order to select the best available motion planning approach for the currently estimated perception uncertainty. We estimate the uncertainty in visual inputs using a deep neural network (CovNet) that explicitly predicts the covariance of the current measurements. Next, we train a high level machine learning model for predicting the lowest cost motion planning algorithm given the current estimate of covariance as well as the UAV states. We demonstrate on both real-life data and drone racing simulations that our approach, named uncertainty driven motion planning switcher (UDS) yields the safest and fastest trajectories among compared alternatives. Furthermore, we show that the developed approach learns how to trade-off safety with agility by switching to motion planners that leads to more agile trajectories when the estimated covariance is high and vice versa.
Recently unmanned aerial vehicles (UAV) have been widely deployed in various real-world scenarios such as disaster rescue and package delivery. Many of these working environments are unstructured with uncertain and dynamic obstacles. UAV collision frequently happens. An UAV with high agility is highly desired to adjust its motions to adapt to these environmental dynamics. However, UAV agility is restricted by its battery power output; particularly, an UAV's power system cannot be aware of its actual power need in motion planning while the need is dynamically changing as the environment and UAV…
UAV-UGV Cooperative Trajectory Optimization and Task Allocation for Medical Rescue Tasks in Post-Disaster Environments
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（多模态人工智能系统国家重点实验室，自动化研究所，中国科学院）；Vanke School of Public Health, Institute for Healthy China, Tsinghua University（万科公共卫生学院，中国健康研究院，清华大学）；School of Automation, Beijing Institute of Technology（自动化学院，北京理工大学）；School of Civil Engineering, Tsinghua University（土木工程学院，清华大学）
The accurate Attitude Heading Reference System(AHRS) is an important apart of the UAV reliable flight system. Aiming at the application scenarios of near ground navigation of small-UAV, this paper establishes a loose couple error model of the gyroscope/accelerometer/magnetometer, and presents an improved FastEuler Double-Layer Kalman Filter algorithm. Using low-cost devices which include MEMS Inertial Measurement Units(IMU) and magnetometers, this paper constructs the AHRS hardware and software systems of UAV, and designs the offline and real-time verification platforms. Moreover, the attitude…
AWARE: Adaptive Whole-body Active Rotating Control for Enhanced LiDAR-Inertial Odometry under Human-in-the-Loop Interaction
organization= State Key Laboratory of Information Engineering in Surveying, Mapping；Remote Sensing , addressline= Wuhan University , city= Wuhan , postcode= 430079 , country= China；organization= School of Electrical；Electronic Engineering , addressline= Nanyang Technological University , postcode= 639798 , country= Singapore
Multi-robot Learning-based Informative Path Planning Using Spatio-Temporal Gaussian Process Kalman Filter
Carnegie Mellon University（卡内基梅隆大学）；Singapore University of Technology and Design（新加坡科技设计大学）；Daegu Gyeongbuk Institute of Science and Technology（大邱庆北科学技术院）
