首页/论文/1202.4465

arXiv 1202.44652012-02-20cs.ROcs.AI

MAV Stabilization using Machine Learning and Onboard Sensors

Cornell（康奈尔大学）

Jason Yosinski, Cooper Bills

备注：: 9 pages, 7 figures

阅读 PDF ↗

arXiv 页面 ↗HTML 原文 ↗

英文摘要相关论文

英文摘要：

In many situations, Miniature Aerial Vehicles (MAVs) are limited to using only on-board sensors for navigation. This limits the data available to algorithms used for stabilization and localization, and current control methods are often insufficient to allow reliable hovering in place or trajectory following. In this research, we explore using machine learning to predict the drift (flight path errors) of an MAV while executing a desired flight path. This predicted drift will allow the MAV to adjust it's flightpath to maintain a desired course.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

阅读原文

阅读 PDF ↗

arXiv 页面 ↗HTML 原文 ↗

本文内容

英文摘要相关论文

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

继续探索

相关论文

Obstacle Avoidance Strategy using Onboard Stereo Vision on a Flapping Wing MAV

Delft University of Technology（代尔夫特理工大学）

The development of autonomous lightweight MAVs, capable of navigating in unknown indoor environments, is one of the major challenges in robotics. The complexity of this challenge comes from constraints on weight and power consumption of onboard sensing and processing devices. In this paper we propose the "Droplet" strategy, an avoidance strategy based on stereo vision inputs that outperforms reactive avoidance strategies by allowing constant speed maneuvers while being computationally extremely efficient, and which does not need to store previous images or maps. The strategy deals with nonholo…

2017-01-02cs.RO

Obstacle Avoidance onboard MAVs using a FMCW RADAR

Faculty of Aerospace Engineering, Delft University of Technology（代尔夫特理工大学航空航天工程学院）

Micro Air Vehicles (MAVs) are increasingly being used for complex or hazardous tasks in enclosed and cluttered environments such as surveillance or search and rescue. With this comes the necessity for sensors that can operate in poor visibility conditions to facilitate with navigation and avoidance of objects or people. Radar sensors in particular can provide more robust sensing of the environment when traditional sensors such as cameras fail in the presence of dust, fog or smoke. While extensively used in autonomous driving, miniature FMCW radars on MAVs have been relatively unexplored. This …

2021-03-02cs.RO

LiDARFlow：未知环境中基于面板的微型飞行器实时引导

LiDARFlow: Real-Time Panel-Based MAV Guidance in Unknown Environments

Centre national de la recherche scientifique（法国国家科学研究中心）

本文提出LiDARFlow算法，基于面板公式和机载LiDAR在线构建障碍物表示，生成避障场与引导场集成，实现未知环境中微型飞行器的实时无碰撞引导，经室内飞行实验验证其轻量高效。

2026-10-02cs.RO

Vision-based Multi-MAV Localization with Anonymous Relative Measurements Using Coupled Probabilistic Data Association Filter

University of Pennsylvania（宾夕法尼亚大学）；Autel Robotics（道通智能航空技术有限公司）

We address the localization of robots in a multi-MAV system where external infrastructure like GPS or motion capture systems may not be available. Our approach lends itself to implementation on platforms with several constraints on size, weight, and power (SWaP). Particularly, our framework fuses the onboard VIO with the anonymous, visual-based robot-to-robot detection to estimate all robot poses in one common frame, addressing three main challenges: 1) the initial configuration of the robot team is unknown, 2) the data association between each vision-based detection and robot targets is unkno…

2020-02-21cs.RO

Visually Guided Balloon Popping with an Autonomous MAV at MBZIRC 2020

Institute for Computer Science VI, Autonomous Intelligent Systems, University of Bonn（波恩大学计算机科学VI研究所自主智能系统研究所）

Visually guided control of micro aerial vehicles (MAV) demands for robust real-time perception, fast trajectory generation, and a capable flight platform. We present a fully autonomous MAV that is able to pop balloons, relying only on onboard sensing and computing. The system is evaluated with real robot experiments during the Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2020 where it showed its resilience and speed. In all three competition runs we were able to pop all five balloons in less than two minutes flight time with a single MAV.

2020-10-28cs.RO

Air Bumper：一种用于自主微型飞行器导航的碰撞检测与反应框架

Air Bumper: A Collision Detection and Reaction Framework for Autonomous MAV Navigation

Chinese University of Hong Kong（香港中文大学）

本文提出 Air Bumper 框架，仅利用机载 IMU 实现三维环境中 MAV 的碰撞检测、估计与快速恢复控制，并通过碰撞感知建图将碰撞信息融入 LiDAR 感知与规划，提升无人机飞行安全性。

2023-09-16cs.RO

更多相关论文收起更多论文

Adaptive Lidar Scan Frame Integration: Tracking Known MAVs in 3D Point Clouds

University of Turku（图尔库大学）

Micro-aerial vehicles (MAVs) are becoming ubiquitous across multiple industries and application domains. Lightweight MAVs with only an onboard flight controller and a minimal sensor suite (e.g., IMU, vision, and vertical ranging sensors) have potential as mobile and easily deployable sensing platforms. When deployed from a ground robot, a key parameter is a relative localization between the ground robot and the MAV. This paper proposes a novel method for tracking MAVs in lidar point clouds. In lidar point clouds, we consider the speed and distance of the MAV to actively adapt the lidar's frame…

2021-04-29cs.RO

Design and Autonomous Stabilization of a Ballistically Launched Multirotor

California Institute of Technology（加州理工学院）；Olin College of Engineering（奥林工程学院）；Jet Propulsion Laboratory, California Institute of Technology（加州理工学院喷气推进实验室）

Aircraft that can launch ballistically and convert to autonomous, free flying drones have applications in many areas such as emergency response, defense, and space exploration, where they can gather critical situational data using onboard sensors. This paper presents a ballistically launched, autonomously stabilizing multirotor prototype (SQUID, Streamlined Quick Unfolding Investigation Drone) with an onboard sensor suite, autonomy pipeline, and passive aerodynamic stability. We demonstrate autonomous transition from passive to vision based, active stabilization, confirming the ability of the …

2020-05-18cs.ROcs.SYeess.SY

Search-based 3D Planning and Trajectory Optimization for Safe Micro Aerial Vehicle Flight Under Sensor Visibility Constraints

Fraunhofer Institute for Communication, Information Processing and Ergonomics FKIE（弗劳恩霍夫通信、信息处理与工效学研究所FKIE）；University of Bonn（波恩大学）

Safe navigation of Micro Aerial Vehicles (MAVs) requires not only obstacle-free flight paths according to a static environment map, but also the perception of and reaction to previously unknown and dynamic objects. This implies that the onboard sensors cover the current flight direction. Due to the limited payload of MAVs, full sensor coverage of the environment has to be traded off with flight time. Thus, often only a part of the environment is covered. We present a combined allocentric complete planning and trajectory optimization approach taking these sensor visibility constraints into acco…

2019-08-28cs.RO

Learning to estimate UAV created turbulence from scene structure observed by onboard cameras

INSA-Lyon（里昂国立应用科学学院）；Université Claude Bernard Lyon 1（里昂第一大学）；Ecole Centrale de Lyon（里昂中央理工学院）；Naver Labs Europe（Naver欧洲实验室）

Controlling UAV flights precisely requires a realistic dynamic model and accurate state estimates from onboard sensors like UAV, GPS and visual observations. Obtaining a precise dynamic model is extremely difficult, as important aerodynamic effects are hard to model, in particular ground effect and other turbulences. While machine learning has been used in the past to estimate UAV created turbulence, this was restricted to flat grounds or diffuse in-flight air turbulences, both without taking into account obstacles. In this work we address the complex problem of estimating in-flight turbulence…

2022-03-28cs.RO

具有安全保证的四旋翼飞行器稳定：一种通用公式方法

Quadrotor Stabilization with Safety Guarantees: A Universal Formula Approach

Eindhoven Artificial Intelligence Systems Institute（埃因霍温人工智能系统研究所）

针对四旋翼飞行器安全稳定问题，提出基于 Sontag 通用公式的解析控制策略，结合 CLFs 和 CBFs 避免机载优化，并引入 ISS/ISSf 处理扰动及投影方法应对输入约束，实现快速机载计算与安全保障。

2024-01-07cs.ROcs.SYeess.SY

3D Reactive Control and Frontier-Based Exploration for Unstructured Environments

University of Colorado Boulder（科罗拉多大学博尔德分校）

The paper proposes a reliable and robust planning solution to the long range robotic navigation problem in extremely cluttered environments. A two-layer planning architecture is proposed that leverages both the environment map and the direct depth sensor information to ensure maximal information gain out of the onboard sensors. A frontier-based pose sampling technique is used with a fast marching cost-to-go calculation to select a goal pose and plan a path to maximize robot exploration rate. An artificial potential function approach, relying on direct depth measurements, enables the robot to f…

2021-08-01cs.ROcs.SYeess.SY