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    ๋ถ„์‚ฐ๋œ ๋กœํ„ฐ๋กœ ๊ตฌ๋™๋˜๋Š” ๋น„ํ–‰ ์Šค์ผˆ๋ ˆํ†ค ์‹œ์Šคํ…œ์˜ ๋””์ž์ธ ์ƒํƒœ์ถ”์ • ๋ฐ ์ œ์–ด

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€,2020. 2. ์ด๋™์ค€.In this thesis, we present key theoretical components for realizing flying aerial skeleton system called LASDRA (large-size aerial skeleton with distributed rotor actuation). Aerial skeletons are articulated aerial robots actuated by distributed rotors including both ground connected type and flying type. These systems have recently attracted interest and are being actively researched in several research groups, with the expectation of applying those for aerial manipulation in distant/narrow places, or for the performance with entertaining purpose such as drone shows. Among the aerial skeleton systems, LASDRA system, proposed by our group has some significant advantages over the other skeleton systems that it is capable of free SE(3) motion by omni-directional wrench generation of each link, and also the system can be operated with wide range of configuration because of the 3DOF (degrees of freedom) inter-link rotation enabled by cable connection among the link modules. To realize this LASDRA system, following three components are crucial: 1) a link module that can produce omni-directional force and torque and enough feasible wrench space; 2) pose and posture estimation algorithm for an articulated system with high degrees of freedom; and 3) a motion generation framework that can provide seemingly natural motion while being able to generate desired motion (e.g., linear and angular velocity) for the entire body. The main contributions of this thesis is theoretically developing these three components, and verifying these through outdoor flight experiment with a real LASDRA system. First of all, a link module for the LASDRA system is designed with proposed constrained optimization problem, maximizing the guaranteed feasible force and torque for any direction while also incorporating some constraints (e.g., avoiding inter-rotor air-flow interference) to directly obtain feasible solution. Also, an issue of ESC-induced (electronic speed control) singularity is first introduced in the literature which is inevitably caused by bi-directional thrust generation with sensorless actuators, and handled with a novel control allocation called selective mapping. Then for the state estimation of the entire LASDRA system, constrained Kalman filter based estimation algorithm is proposed that can provide estimation result satisfying kinematic constraint of the system, also along with a semi-distributed version of the algorithm to endow with system scalability. Lastly, CPG-based motion generation framework is presented that can generate natural biomimetic motion, and by exploiting the inverse CPG model obtained with machine learning method, it becomes possible to generate certain desired motion while still making CPG generated natural motion.๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋น„ํ–‰ ์Šค์ผˆ๋ ˆํ†ค ์‹œ์Šคํ…œ LASDRA (large-size aerial skeleton with distributed rotor actuation) ์˜ ๊ตฌํ˜„์„ ์œ„ํ•ด ์š”๊ตฌ๋˜๋Š” ํ•ต์‹ฌ ๊ธฐ๋ฒ•๋“ค์„ ์ œ์•ˆํ•˜๋ฉฐ, ์ด๋ฅผ ์‹ค์ œ LASDRA ์‹œ์Šคํ…œ์˜ ์‹ค์™ธ ๋น„ํ–‰์„ ํ†ตํ•ด ๊ฒ€์ฆํ•œ๋‹ค. ์ œ์•ˆ๋œ ๊ธฐ๋ฒ•์€ 1) ์ „๋ฐฉํ–ฅ์œผ๋กœ ํž˜๊ณผ ํ† ํฌ๋ฅผ ๋‚ผ ์ˆ˜ ์žˆ๊ณ  ์ถฉ๋ถ„ํ•œ ๊ฐ€์šฉ ๋ Œ์น˜๊ณต๊ฐ„์„ ๊ฐ€์ง„ ๋งํฌ ๋ชจ๋“ˆ, 2) ๋†’์€ ์ž์œ ๋„์˜ ๋‹ค๊ด€์ ˆ๊ตฌ์กฐ ์‹œ์Šคํ…œ์„ ์œ„ํ•œ ์œ„์น˜ ๋ฐ ์ž์„ธ ์ถ”์ • ์•Œ๊ณ ๋ฆฌ์ฆ˜, 3) ์ž์—ฐ์Šค๋Ÿฌ์šด ์›€์ง์ž„์„ ๋‚ด๋Š” ๋™์‹œ์— ์ „์ฒด ์‹œ์Šคํ…œ์ด ์†๋„, ๊ฐ์†๋„ ๋“ฑ ์›ํ•˜๋Š” ์›€์ง์ž„์„ ๋‚ด๋„๋ก ํ•  ์ˆ˜ ์žˆ๋Š” ๋ชจ์…˜ ์ƒ์„ฑ ํ”„๋ ˆ์ž„์›Œํฌ๋กœ ๊ตฌ์„ฑ๋œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์šฐ์„  ๋งํฌ ๋ชจ๋“ˆ์˜ ๋””์ž์ธ์„ ์œ„ํ•ด ์ „๋ฐฉํ–ฅ์œผ๋กœ ๋ณด์žฅ๋˜๋Š” ํž˜๊ณผ ํ† ํฌ์˜ ํฌ๊ธฐ๋ฅผ ์ตœ๋Œ€ํ™”ํ•˜๋Š” ๊ตฌ์† ์ตœ์ ํ™”๋ฅผ ์‚ฌ์šฉํ•˜๊ณ , ์‹ค์ œ ์ ์šฉ๊ฐ€๋Šฅํ•œ ํ•ด๋ฅผ ์–ป๊ธฐ ์œ„ํ•ด ๋ช‡๊ฐ€์ง€ ๊ตฌ์†์กฐ๊ฑด(๋กœํ„ฐ ๊ฐ„ ๊ณต๊ธฐ ํ๋ฆ„ ๊ฐ„์„ญ์˜ ํšŒํ”ผ ๋“ฑ)์„ ๊ณ ๋ คํ•œ๋‹ค. ๋˜ํ•œ ์„ผ์„œ๊ฐ€ ์—†๋Š” ์•ก์ธ„์—์ดํ„ฐ๋กœ ์–‘๋ฐฉํ–ฅ ์ถ”๋ ฅ์„ ๋‚ด๋Š” ๊ฒƒ์—์„œ ์•ผ๊ธฐ๋˜๋Š” ESC ์œ ๋ฐœ ํŠน์ด์  (ESC-induced singularity) ์ด๋ผ๋Š” ๋ฌธ์ œ๋ฅผ ์ฒ˜์Œ์œผ๋กœ ์†Œ๊ฐœํ•˜๊ณ , ์ด๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ์„ ํƒ์  ๋งตํ•‘ (selective mapping) ์ด๋ผ๋Š” ๊ธฐ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. ์ „์ฒด LASDRA ์‹œ์Šคํ…œ์˜ ์ƒํƒœ์ถ”์ •์„ ์œ„ํ•ด ์‹œ์Šคํ…œ์˜ ๊ธฐ๊ตฌํ•™์  ๊ตฌ์†์กฐ๊ฑด์„ ๋งŒ์กฑํ•˜๋Š” ๊ฒฐ๊ณผ๋ฅผ ์–ป์„ ์ˆ˜ ์žˆ๋„๋ก ๊ตฌ์† ์นผ๋งŒ ํ•„ํ„ฐ ๊ธฐ๋ฐ˜์˜ ์ƒํƒœ์ถ”์ • ๊ธฐ๋ฒ•์„ ์ œ์‹œํ•˜๊ณ , ์‹œ์Šคํ…œ ํ™•์žฅ์„ฑ์„ ๊ณ ๋ คํ•˜์—ฌ ๋ฐ˜ ๋ถ„์‚ฐ (semi-distributed) ๊ฐœ๋…์˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ํ•จ๊ป˜ ์ œ์‹œํ•œ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ž์—ฐ์Šค๋Ÿฌ์šด ์›€์ง์ž„์˜ ์ƒ์„ฑ์„ ์œ„ํ•˜์—ฌ CPG ๊ธฐ๋ฐ˜์˜ ๋ชจ์…˜ ์ƒ์„ฑ ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ์ œ์•ˆํ•˜๋ฉฐ, ๊ธฐ๊ณ„ ํ•™์Šต ๋ฐฉ๋ฒ•์„ ํ†ตํ•ด CPG ์—ญ์—ฐ์‚ฐ ๋ชจ๋ธ์„ ์–ป์Œ์œผ๋กœ์จ ์ „์ฒด ์‹œ์Šคํ…œ์ด ์›ํ•˜๋Š” ์›€์ง์ž„์„ ๋‚ผ ์ˆ˜ ์žˆ๋„๋ก ํ•œ๋‹ค.1 Introduction 1 1.1 Motivation and Background 1 1.2 Research Problems and Approach 3 1.3 Preview of Contributions 5 2 Omni-Directional Aerial Robot 7 2.1 Introduction 7 2.2 Mechanical Design 12 2.2.1 Design Description 12 2.2.2 Wrench-Maximizing Design Optimization 13 2.3 System Modeling and Control Design 20 2.3.1 System Modeling 20 2.3.2 Pose Trajectory Tracking Control 22 2.3.3 Hybrid Pose/Wrench Control 22 2.3.4 PSPM-Based Teleoperation 24 2.4 Control Allocation with Selective Mapping 27 2.4.1 Infinity-Norm Minimization 27 2.4.2 ESC-Induced Singularity and Selective Mapping 29 2.5 Experiment 38 2.5.1 System Setup 38 2.5.2 Experiment Results 41 2.6 Conclusion 49 3 Pose and Posture Estimation of an Aerial Skeleton System 51 3.1 Introduction 51 3.2 Preliminary 53 3.3 Pose and Posture Estimation 55 3.3.1 Estimation Algorithm via SCKF 55 3.3.2 Semi-Distributed Version of Algorithm 59 3.4 Simulation 62 3.5 Experiment 65 3.5.1 System Setup 65 3.5.2 Experiment of SCKF-Based Estimation Algorithm 66 3.6 Conclusion 69 4 CPG-Based Motion Generation 71 4.1 Introduction 71 4.2 Description of Entire Framework 75 4.2.1 LASDRA System 75 4.2.2 Snake-Like Robot & Pivotboard 77 4.3 CPG Model 79 4.3.1 LASDRA System 79 4.3.2 Snake-Like Robot 80 4.3.3 Pivotboard 83 4.4 Target Pose Calculation with Expected Physics 84 4.5 Inverse Model Learning 86 4.5.1 LASDRA System 86 4.5.2 Snake-Like Robot 89 4.5.3 Pivotboard 90 4.6 CPG Parameter Adaptation 93 4.7 Simulation 94 4.7.1 LASDRA System 94 4.7.2 Snake-Like Robot & Pivotboard 97 4.8 Conclusion 101 5 Outdoor Flight Experiment of the F-LASDRA System 103 5.1 System Setup 103 5.2 Experiment Results 104 6 Conclusion 111 6.1 Summary 111 6.2 Future Works 112Docto

    The use of modern tools for modelling and simulation of UAV with Haptic

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    Unmanned Aerial Vehicle (UAV) is a research field in robotics which is in high demand in recent years, although there still exist many unanswered questions. In contrast, to the human operated aerial vehicles, it is still far less used to the fact that people are dubious about flying in or flying an unmanned vehicle. It is all about giving the control right to the computer (which is the Artificial Intelligence) for making decisions based on the situation like human do but this has not been easy to make people understand that itโ€™s safe and to continue the enhancement on it. These days there are many types of UAVs available in the market for consumer use, for applications like photography to play games, to map routes, to monitor buildings, for security purposes and much more. Plus, these UAVs are also being widely used by the military for surveillance and for security reasons. One of the most commonly used consumer product is a quadcopter or quadrotor. The research carried out used modern tools (i.e., SolidWorks, Java Net Beans and MATLAB/Simulink) to model controls system for Quadcopter UAV with haptic control system to control the quadcopter in a virtual simulation environment and in real time environment. A mathematical model for the controlling the quadcopter in simulations and real time environments were introduced. Where, the design methodology for the quadcopter was defined. This methodology was then enhanced to develop a virtual simulation and real time environments for simulations and experiments. Furthermore, the haptic control was then implemented with designed control system to control the quadcopter in virtual simulation and real time experiments. By using the mathematical model of quadcopter, PID & PD control techniques were used to model the control setup for the quadcopter altitude and motion controls as work progressed. Firstly, the dynamic model is developed using a simple set of equations which evolves further by using complex control & mathematical model with precise function of actuators and aerodynamic coefficients Figure5-7. The presented results are satisfying and shows that flight experiments and simulations of the quadcopter control using haptics is a novel area of research which helps perform operations more successfully and give more control to the operator when operating in difficult environments. By using haptic accidents can be minimised and the functional performance of the operator and the UAV will be significantly enhanced. This concept and area of research of haptic control can be further developed accordingly to the needs of specific applications

    The use of modern tools for modelling and simulation of UAV with Haptic

    Get PDF
    Unmanned Aerial Vehicle (UAV) is a research field in robotics which is in high demand in recent years, although there still exist many unanswered questions. In contrast, to the human operated aerial vehicles, it is still far less used to the fact that people are dubious about flying in or flying an unmanned vehicle. It is all about giving the control right to the computer (which is the Artificial Intelligence) for making decisions based on the situation like human do but this has not been easy to make people understand that itโ€™s safe and to continue the enhancement on it. These days there are many types of UAVs available in the market for consumer use, for applications like photography to play games, to map routes, to monitor buildings, for security purposes and much more. Plus, these UAVs are also being widely used by the military for surveillance and for security reasons. One of the most commonly used consumer product is a quadcopter or quadrotor. The research carried out used modern tools (i.e., SolidWorks, Java Net Beans and MATLAB/Simulink) to model controls system for Quadcopter UAV with haptic control system to control the quadcopter in a virtual simulation environment and in real time environment. A mathematical model for the controlling the quadcopter in simulations and real time environments were introduced. Where, the design methodology for the quadcopter was defined. This methodology was then enhanced to develop a virtual simulation and real time environments for simulations and experiments. Furthermore, the haptic control was then implemented with designed control system to control the quadcopter in virtual simulation and real time experiments. By using the mathematical model of quadcopter, PID & PD control techniques were used to model the control setup for the quadcopter altitude and motion controls as work progressed. Firstly, the dynamic model is developed using a simple set of equations which evolves further by using complex control & mathematical model with precise function of actuators and aerodynamic coefficients Figure5-7. The presented results are satisfying and shows that flight experiments and simulations of the quadcopter control using haptics is a novel area of research which helps perform operations more successfully and give more control to the operator when operating in difficult environments. By using haptic accidents can be minimised and the functional performance of the operator and the UAV will be significantly enhanced. This concept and area of research of haptic control can be further developed accordingly to the needs of specific applications

    Aerial Robotics for Inspection and Maintenance

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    Aerial robots with perception, navigation, and manipulation capabilities are extending the range of applications of drones, allowing the integration of different sensor devices and robotic manipulators to perform inspection and maintenance operations on infrastructures such as power lines, bridges, viaducts, or walls, involving typically physical interactions on flight. New research and technological challenges arise from applications demanding the benefits of aerial robots, particularly in outdoor environments. This book collects eleven papers from different research groups from Spain, Croatia, Italy, Japan, the USA, the Netherlands, and Denmark, focused on the design, development, and experimental validation of methods and technologies for inspection and maintenance using aerial robots

    The AEROARMS Project: Aerial Robots with Advanced Manipulation Capabilities for Inspection and Maintenance

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    This article summarizes new aerial robotic manipulation technologies and methodsโ€”aerial robotic manipulators with dual arms and multidirectional thrustersโ€”developed in the AEROARMS project for outdoor industrial inspection and maintenance (I&M). Our report deals with the control systems, including the control of the interaction forces and the compliance the teleoperation, which uses passivity to tackle the tradeoff between stability and performance the perception methods for localization, mapping, and inspection the planning methods, including a new control-aware approach for aerial manipulation. Finally, we describe a novel industrial platform with multidirectional thrusters and a new arm design to increase the robustness in industrial contact inspections. In addition, the lessons learned in applying the platform to outdoor aerial manipulation for I&M are pointed out

    Ball-Shaped Robots

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    Aerial Vehicles

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    This book contains 35 chapters written by experts in developing techniques for making aerial vehicles more intelligent, more reliable, more flexible in use, and safer in operation.It will also serve as an inspiration for further improvement of the design and application of aeral vehicles. The advanced techniques and research described here may also be applicable to other high-tech areas such as robotics, avionics, vetronics, and space
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