國立虎尾科技大學 |

Robust Flight Control and Visual/Radar-Based Indoor Navigation for Autonomous Multicopters and Their Swarms.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Robust Flight Control and Visual/Radar-Based Indoor Navigation for Autonomous Multicopters and Their Swarms./
作者:	Jing, Yutao.
面頁冊數:	1 online resource (278 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-10, Section: B.
Contained By:	Dissertations Abstracts International85-10B.
標題:	Aerospace engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9798382320823

Robust Flight Control and Visual/Radar-Based Indoor Navigation for Autonomous Multicopters and Their Swarms.
Jing, Yutao.

Robust Flight Control and Visual/Radar-Based Indoor Navigation for Autonomous Multicopters and Their Swarms. - 1 online resource (278 pages)

Source: Dissertations Abstracts International, Volume: 85-10, Section: B.

Thesis (Ph.D.)--Northeastern University, 2024.

Includes bibliographical references

This dissertation presents a comprehensive approach to addressing the challenges associated with the control and navigation of Unmanned Aerial Vehicles (UAVs), specifically focusing on robustness under GPS-denied, turbulent, and dark indoor conditions with various disturbances. Initial efforts involved designing and theoretically validating Sliding Mode Controllers (SMC) with Disturbance Observers using MATLAB/SIMULINK, optimized using offline Particle Swarm Optimization (PSO). In order to evaluate their performance, a lightweight simulator was developed to simulate both quadcopter and hexacopter configurations under the PX4 architecture. This simulator served to validate and compare the optimized SMC against PSO-optimized PID controllers. The results of these comparisons demonstrated the superior adaptability of the SMC, especially in environments characterized by significant sensor noise and disturbances. Subsequent practical indoor tests were conducted with real quadcopters, which provided additional verification of the robustness and reliability of the enhanced control theory with the actual UAVs under dynamic disturbances. To further evaluate the compatibility and adaptability of the SMC, this dissertation establishes a framework for interfacing with high-performance STMH7 chips, serving as the UAV microcontroller unit (MCU). This framework facilitates the connection of various sensors and provides guidance for constructing different quadcopter frames. Moreover, it enables the comparison of different flight controllers on distinct air-frames. Concurrently, the optimized SMC allows for remote dynamic parameter tuning during flight. In pursuit of improved localization capacity, the dissertation also explores the integration of an Intel Realsense D435i camera with an Inertial Measurement Unit (IMU) for Visual-Inertial Odometry (VIO) fusion within the Robot Operating System (ROS) environment. This integration employs an onboard companion computer for real-time VIO estimation and flight command. The effectiveness of the VIO algorithm is verified through both simulations and real-world experiments, highlighting its ability to enhance UAV localization and navigation. Additionally, a visual inertial odometry method for UAV Simultaneous Localization and Mapping (vSLAM) is tested and validated in both simulated and real drone environments. Furthermore, the dissertation introduces an innovative hybrid filtered multi-directional radar inertial odometry solution denoted as Hybrid-MRIO. This Error State Extended Kalman Filter (ES-EKF) based Radar Inertial Odometry (RIO) approach is implemented within the open-source PX4 autopilot system, enhancing UAV navigation accuracy and performance, and serves as a robust navigation alternative, particularly in challenging environments where traditional navigation systems prove ineffective, such as dark or smoky conditions. This solution integrates data from multiple software synchronized high-resolution Frequency-Modulated Continuous-Wave (FMCW) mmWave radars with an IMU. A subsequent 4D (x, y, z, doppler) mmWave radar SLAM (rSLAM) system has been developed, including three modules: front-end, loop detection module, and back-end. In the front-end, radar ego-velocity is utilized for states estimation and dynamic object removal, and a point cloud registration-based approach known as APDGICP (Adaptive Probability Distribution-GICP) is employed for keyframe detection. The loop detection module utilizes the intensity scan context to identify potential loop closure candidates. In the back-end, a pose graph is constructed, integrating RIO estimated odometry and identified loop closures for better localization.To develop fully-autonomous UAVs with their decision making modules, this dissertation details the development of a custom AI-powered fully-autonomous quadcopter equipped with companion computers and stereo IR cameras. Additionally, this dissertation discusses a swarm of small Unmanned Aerial Systems (SUAS) interconnected through a software-defined communications network. These drones demonstrated their capacity to independently/collaboratively execute a wide range of tasks, including target search, detection, identification, classification, tracking, and following, both in simulated and real-world scenarios. The system exhibits advanced collision avoidance capabilities and the ability to strategically respond to dynamic scenarios, such as changes in target behavior, highlighting its effectiveness in managing dynamic and challenging situations without the need for continuous human intervention.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798382320823Subjects--Topical Terms:

686400
Aerospace engineering.
Subjects--Index Terms:

Sliding Mode ControllersIndex Terms--Genre/Form:

554714
Electronic books.

Robust Flight Control and Visual/Radar-Based Indoor Navigation for Autonomous Multicopters and Their Swarms.
LDR:06061ntm a22003977 4500 001 1150471
005 20241028051458.5
006 m o d
007 cr bn ---uuuuu
008 250605s2024 xx obm 000 0 eng d
020 $a 9798382320823
035 $a (MiAaPQ)AAI31146887
035 $a AAI31146887
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Jing, Yutao. $3 1476958
245 1 0 $a Robust Flight Control and Visual/Radar-Based Indoor Navigation for Autonomous Multicopters and Their Swarms.
264 0 $c 2024
300 $a 1 online resource (278 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 85-10, Section: B.
500 $a Advisor: Martinez-Lorenzo, Jose A.
502 $a Thesis (Ph.D.)--Northeastern University, 2024.
504 $a Includes bibliographical references
520 $a This dissertation presents a comprehensive approach to addressing the challenges associated with the control and navigation of Unmanned Aerial Vehicles (UAVs), specifically focusing on robustness under GPS-denied, turbulent, and dark indoor conditions with various disturbances. Initial efforts involved designing and theoretically validating Sliding Mode Controllers (SMC) with Disturbance Observers using MATLAB/SIMULINK, optimized using offline Particle Swarm Optimization (PSO). In order to evaluate their performance, a lightweight simulator was developed to simulate both quadcopter and hexacopter configurations under the PX4 architecture. This simulator served to validate and compare the optimized SMC against PSO-optimized PID controllers. The results of these comparisons demonstrated the superior adaptability of the SMC, especially in environments characterized by significant sensor noise and disturbances. Subsequent practical indoor tests were conducted with real quadcopters, which provided additional verification of the robustness and reliability of the enhanced control theory with the actual UAVs under dynamic disturbances. To further evaluate the compatibility and adaptability of the SMC, this dissertation establishes a framework for interfacing with high-performance STMH7 chips, serving as the UAV microcontroller unit (MCU). This framework facilitates the connection of various sensors and provides guidance for constructing different quadcopter frames. Moreover, it enables the comparison of different flight controllers on distinct air-frames. Concurrently, the optimized SMC allows for remote dynamic parameter tuning during flight. In pursuit of improved localization capacity, the dissertation also explores the integration of an Intel Realsense D435i camera with an Inertial Measurement Unit (IMU) for Visual-Inertial Odometry (VIO) fusion within the Robot Operating System (ROS) environment. This integration employs an onboard companion computer for real-time VIO estimation and flight command. The effectiveness of the VIO algorithm is verified through both simulations and real-world experiments, highlighting its ability to enhance UAV localization and navigation. Additionally, a visual inertial odometry method for UAV Simultaneous Localization and Mapping (vSLAM) is tested and validated in both simulated and real drone environments. Furthermore, the dissertation introduces an innovative hybrid filtered multi-directional radar inertial odometry solution denoted as Hybrid-MRIO. This Error State Extended Kalman Filter (ES-EKF) based Radar Inertial Odometry (RIO) approach is implemented within the open-source PX4 autopilot system, enhancing UAV navigation accuracy and performance, and serves as a robust navigation alternative, particularly in challenging environments where traditional navigation systems prove ineffective, such as dark or smoky conditions. This solution integrates data from multiple software synchronized high-resolution Frequency-Modulated Continuous-Wave (FMCW) mmWave radars with an IMU. A subsequent 4D (x, y, z, doppler) mmWave radar SLAM (rSLAM) system has been developed, including three modules: front-end, loop detection module, and back-end. In the front-end, radar ego-velocity is utilized for states estimation and dynamic object removal, and a point cloud registration-based approach known as APDGICP (Adaptive Probability Distribution-GICP) is employed for keyframe detection. The loop detection module utilizes the intensity scan context to identify potential loop closure candidates. In the back-end, a pose graph is constructed, integrating RIO estimated odometry and identified loop closures for better localization.To develop fully-autonomous UAVs with their decision making modules, this dissertation details the development of a custom AI-powered fully-autonomous quadcopter equipped with companion computers and stereo IR cameras. Additionally, this dissertation discusses a swarm of small Unmanned Aerial Systems (SUAS) interconnected through a software-defined communications network. These drones demonstrated their capacity to independently/collaboratively execute a wide range of tasks, including target search, detection, identification, classification, tracking, and following, both in simulated and real-world scenarios. The system exhibits advanced collision avoidance capabilities and the ability to strategically respond to dynamic scenarios, such as changes in target behavior, highlighting its effectiveness in managing dynamic and challenging situations without the need for continuous human intervention.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Aerospace engineering. $3 686400
650 4 $a Industrial engineering. $3 679492
650 4 $a Mechanical engineering. $3 557493
653 $a Sliding Mode Controllers
653 $a Machine learning
653 $a Radar inertial odometry
653 $a Particle Swarm Optimization
653 $a Visual inertial odometry
655 7 $a Electronic books. $2 local $3 554714
690 $a 0538
690 $a 0548
690 $a 0546
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Northeastern University. $b Mechanical and Industrial Engineering. $3 845717
773 0 $t Dissertations Abstracts International $g 85-10B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31146887 $z click for full text (PQDT)