1,062 research outputs found
Motion planning with dynamics awareness for long reach manipulation in aerial robotic systems with two arms
Human activities in maintenance of industrial plants pose elevated risks as well as significant costs due to the required shutdowns of the facility. An aerial robotic system with two arms for long reach manipulation in cluttered environments is presented to alleviate these constraints. The system consists of a multirotor with a long bar extension that incorporates a lightweight dual arm in the tip. This configuration allows aerial manipulation tasks even in hard-to-reach places. The objective of this work is the development of planning strategies to move the aerial robotic system with two arms for long reach manipulation in a safe and efficient way for both navigation and manipulation tasks. The motion planning problem is addressed considering jointly the aerial platform and the dual arm in order to achieve wider operating conditions. Since there exists a strong dynamical coupling between the multirotor and the dual arm, safety in obstacle avoidance will be assured by introducing dynamics awareness in the operation of the planner. On the other hand, the limited maneuverability of the system emphasizes the importance of energy and time efficiency in the generated trajectories. Accordingly, an adapted version of the optimal Rapidly-exploring Random Tree algorithm has been employed to guarantee their optimality. The resulting motion planning strategy has been evaluated through simulation in two realistic industrial scenarios, a riveting application and a chimney repairing task. To this end, the dynamics of the aerial robotic system with two arms for long reach manipulation has been properly modeled, and a distributed control scheme has been derived to complete the test bed. The satisfactory results of the simulations are presented as a first validation of the proposed approach.Uniรณn Europea H2020-644271Ministerio de Ciencia, Innovaciรณn y Universidades DPI2014-59383-C2-1-
๊ฒฝ์ฒฉ๋ฌธ์ ์ฌ๋ ๋นํ ๋งค๋ํฐ๋ ์ดํฐ์ ๋ํ ๋ชจ๋ธ ์์ธก ์ ์ด
ํ์๋
ผ๋ฌธ (์์ฌ) -- ์์ธ๋ํ๊ต ๋ํ์ : ๊ณต๊ณผ๋ํ ๊ธฐ๊ณํญ๊ณต๊ณตํ๋ถ, 2020. 8. ๊นํ์ง.From aerial pick-and-place to aerial transportation, aerial manipulation has been extensively studied in recent years thanks to its mobility and dexterity, each of which is a merit of an aerial vehicle and a robotic arm. However, to fully harness the concept of aerial manipulation, more complex tasks including interaction with movable structures should also be considered. Among various types of movable structures, this paper presents a multirotor-based aerial manipulator opening a daily-life moving structure, a hinged door. Two additional issues that would arise in interaction with a movable structure are addressed: 1) a constrained motion of the structure, and 2) collision avoidance with a moving structure. To handle these issues, we formulate a model predictive control (MPC) problem with a system dynamics constraint and state constraints for collision avoidance. A coupled system dynamics of a multirotor-based aerial manipulator and a hinged door is derived and later simplified for faster computation in MPC. State constraints for collision avoidance with itself, a door, and a doorframe are generated. By implementing a constrained version of differential dynamic programming (DDP), we can generate reference trajectories through MPC in real-time. The proposed method is validated through simulation results and experiments with a real-like hinged door in which a disturbance observer (DOB) based robust motion controller is employed.๋นํ ๋งค๋ํฐ๋ ์ดํฐ๋ 3์ฐจ์ ๊ณต๊ฐ ์์ ๋น ๋ฅด๊ฒ ์์นํ ์ ์๋ ๋นํ์ฒด์ ์ฅ์ ๊ณผ ์ธ๋ถ์์ ์ํธ์์ฉ์ด ๊ฐ๋ฅํ ๋ก๋ดํ์ ์ฅ์ ์ด ๊ฒฐํฉ๋ ๋นํ์ฒด๋ก, ์ต๊ทผ ๋ฌผ๊ฑด ์ง๊ณ ์ฎ๊ธฐ๊ธฐ๋ถํฐ ๋ฌผํ ์ด์ก๊น์ง ๋ค์ํ ์๋ฌด๋ฅผ ์ํํ๊ธฐ ์ํด ํ๋ฐํ๊ฒ ์ฐ๊ตฌ๋์ด ์๋ค. ๊ทธ๋ฌ๋, ์จ์ ํ ๋นํ ๋งค๋ํฐ๋ ์ดํฐ์ ๊ฐ๋ฅ์ฑ์ ํ์ฉํ๊ธฐ ์ํด์๋ ์์ง์ผ ์ ์๋ ์ธ๋ถ ๊ตฌ์กฐ์์ ์ํธ์์ฉ๊ณผ ๊ฐ์ด ๋์ฑ ๋ณต์กํ ์๋ฌด ๋ํ ์ํํ ์ ์์ด์ผ ํ ๊ฒ์ด๋ค. ์ฌ๋ฌ ์ข
๋ฅ์ ์์ง์ผ ์ ์๋ ๊ตฌ์กฐ๋ฌผ ์ค ๋ณธ ๋
ผ๋ฌธ์์๋ ์ผ์ ์์์ ์ฝ๊ฒ ๋ง์ฃผ์น ์ ์๋ ๊ฒฝ์ฒฉ๋ฌธ์ ์ฌ๋ ๋ฉํฐ๋กํฐ ๊ธฐ๋ฐ์ ๋นํ ๋งค๋ํฐ๋ ์ดํฐ์ ๋ํด ์ ์ํ๋ค.
์ ์ ์ธ ๊ตฌ์กฐ๋ฌผ๊ณผ์ ์ํธ์์ฉ๊ณผ๋ ๋ฌ๋ฆฌ ๋์ ์ธ ๊ตฌ์กฐ๋ฌผ๊ณผ์ ์ํธ์์ฉ์ ์์ด์ ๋ฐ์ํ ์ ์๋ 1) ๊ตฌ์กฐ๋ฌผ์ ์ ์ฝ๋ ์์ง์, ๊ทธ๋ฆฌ๊ณ 2) ์์ง์ด๋ ๊ตฌ์กฐ๋ฌผ๊ณผ์ ์ถฉ๋ ํํผ์ 2๊ฐ์ง ์ถ๊ฐ์ ์ธ ๋ฌธ์ ์ ๋ํด ๋ค๋ฃจ์๋ค. ์ด๋ฌํ ๋ฌธ์ ๋ฅผ ๋ค๋ฃจ๊ธฐ ์ํด ๋ชจ๋ธ ์์ธก ์ ์ด (MPC)๋ฅผ ์ ์ฉํ์์ผ๋ฉฐ, ์์คํ
๋์ญํ์ ๋ํ ์ ์ฝ์กฐ๊ฑด ๋ฐ ์ถฉ๋ ํํผ์ ๋ํ ์ ์ฝ ์กฐ๊ฑด์ ๋ถ์ฌํ์๋ค. ๋ฉํฐ๋กํฐ ๊ธฐ๋ฐ์ ๋นํ ๋งค๋ํฐ๋ ์ดํฐ์ ๊ฒฝ์ฒฉ๋ฌธ์ ๊ฒฐํฉ ์์คํ
์ ๋ํ ๋์ญํ์ ์ ๋ํ์์ผ๋ฉฐ, ์ดํ ๋ชจ๋ธ ์์ธก ์ ์ด์์์ ๋น ๋ฅธ ๊ณ์ฐ ์๋๋ฅผ ์ํด ๋จ์ํ๋์๋ค. ์ถฉ๋ ํํผ์ ๋ํ ์ ์ฝ ์กฐ๊ฑด์ ๋ชจ๋ ์ํ ๋ณ์๋ก ํํ๋์์ผ๋ฉฐ, ๋นํ ๋งค๋ํฐ๋ ์ดํฐ์ ๋ฉํฐ๋กํฐ ํ๋ ์๊ณผ ๋ก๋ดํ ์ฌ์ด์ ์ถฉ๋ (์๊ธฐ ์ถฉ๋), ๋ฌธ๊ณผ์ ์ถฉ๋, ๊ทธ๋ฆฌ๊ณ ๋ฌธํ๊ณผ์ ์ถฉ๋์ ๊ณ ๋ คํ์๋ค. ๋ฏธ๋ถ ๊ธฐ๋ฐ์ ๋์ ํ๋ก๊ทธ๋๋ฐ ๊ธฐ๋ฒ (differential dynamic programming)์ ์ ์ฝ์กฐ๊ฑด์ด ๊ณ ๋ ค๋ ์๊ณ ๋ฆฌ์ฆ์ ๊ตฌํํจ์ผ๋ก์จ ๋ชจ๋ธ ๊ธฐ๋ฐ ์์ธก ์ ์ด๋ฅผ ํตํด ์ค์๊ฐ์ผ๋ก ๊ฒฝ๋ก๋ฅผ ๊ณํํ ์ ์๋ค. ์ ์๋ ๋ฐฉ๋ฒ์ ์๋ฎฌ๋ ์ด์
๊ณผ ์ค์ ํฌ๊ธฐ์ ๋ฌธ์ ํ์ฉํ ์คํ์ ํตํด ๊ฒ์ฆ๋์์ผ๋ฉฐ, ์ธ๋ ๊ด์ธก๊ธฐ ๊ธฐ๋ฐ์ ๊ฐ๊ฑด ์ ์ด ๊ธฐ๋ฒ์ด ์คํ์ ํ์ฉ๋์๋ค.1 Introduction 1
1.1 Literature review 2
1.2 Thesis contribution 3
1.3 Thesis outline 3
2 Equations of motion 4
2.1 Assumption 4
2.2 Kinematics 5
2.3 Dynamics 6
2.4 Simpli ed dynamics 8
3 Model predictive control 10
3.1 Problem formulation 10
3.1.1 Objective function 11
3.1.2 Hard constraints 11
3.2 Collision avoidance constraints 11
3.2.1 Self collision avoidance 13
3.2.2 Door collision avoidance 13
3.2.3 Doorframe collision avoidance 14
3.3 Optimal control solver 14
3.3.1 Differential dynamic programming 14
3.3.2 DDP with augmented Lagrangian method 15
4 Experimental setup 17
4.1 Door state estimation 17
4.2 Multirotor robust controller 18
4.3 Hardware setup 19
5 Results 20
5.1 Simulation results 20
5.2 Experimental results 25
6 Conclusion 29Maste
A Nonlinear Model Predictive Control Scheme for Cooperative Manipulation with Singularity and Collision Avoidance
This paper addresses the problem of cooperative transportation of an object
rigidly grasped by robotic agents. In particular, we propose a Nonlinear
Model Predictive Control (NMPC) scheme that guarantees the navigation of the
object to a desired pose in a bounded workspace with obstacles, while complying
with certain input saturations of the agents. Moreover, the proposed
methodology ensures that the agents do not collide with each other or with the
workspace obstacles as well as that they do not pass through singular
configurations. The feasibility and convergence analysis of the NMPC are
explicitly provided. Finally, simulation results illustrate the validity and
efficiency of the proposed method.Comment: Simulation results with 3 agents adde
Design of an Anthropomorphic, Compliant, and Lightweight Dual Arm for Aerial Manipulation
This paper presents an anthropomorphic, compliant and lightweight dual arm manipulator designed and developed for aerial manipulation applications with multi-rotor platforms. Each arm provides four degrees of freedom in a human-like kinematic configuration for end effector positioning: shoulder pitch, roll and yaw, and elbow pitch. The dual arm, weighting 1.3 kg in total, employs smart servo actuators and a customized and carefully designed aluminum frame structure manufactured by laser cut. The proposed
design reduces the manufacturing cost as no computer numerical control machined part is used. Mechanical joint compliance is provided in all the joints, introducing a compact spring-lever transmission mechanism between the servo shaft and the links, integrating a potentiometer for measuring the deflection of the joints.
The servo actuators are partially or fully isolated against impacts and overloads thanks to the ange bearings attached to the frame structure that support the rotation of the links and the deflection of the joints. This simple mechanism increases the robustness of the arms and safety in the physical interactions between the aerial
robot and the environment. The developed manipulator has been validated through different experiments in fixed base test-bench and in outdoor flight tests.Uniรณn Europea H2020-ICT-2014- 644271Ministerio de Economรญa y Competitividad DPI2015-71524-RMinisterio de Economรญa y Competitividad DPI2017-89790-
๋ชจ์ ํ๋ฆฌ๋จธํฐ๋ธ๋ฅผ ์ด์ฉํ ๋ณต์กํ ๋ก๋ด ์๋ฌด ํ์ต ๋ฐ ์ผ๋ฐํ ๊ธฐ๋ฒ
ํ์๋
ผ๋ฌธ (๋ฐ์ฌ) -- ์์ธ๋ํ๊ต ๋ํ์ : ๊ณต๊ณผ๋ํ ํญ๊ณต์ฐ์ฃผ๊ณตํ๊ณผ, 2020. 8. ๊นํ์ง.Learning from demonstrations (LfD) is a promising approach that enables robots to perform a specific movement. As robotic manipulations are substituting a variety of tasks, LfD algorithms are widely used and studied for specifying the robot configurations for the various types of movements.
This dissertation presents an approach based on parametric dynamic movement primitives (PDMP) as a motion representation algorithm which is one of relevant LfD techniques. Unlike existing motion representation algorithms, this work not only represents a prescribed motion but also computes the new behavior through a generalization of multiple demonstrations in the actual environment. The generalization process uses Gaussian process regression (GPR) by representing the nonlinear relationship between the PDMP parameters that determine motion and the corresponding environmental variables. The proposed algorithm shows that it serves as a powerful optimal and real-time motion planner among the existing planning algorithms when optimal demonstrations are provided as dataset.
In this dissertation, the safety of motion is also considered. Here, safety refers to keeping the system away from certain configurations that are unsafe. The safety criterion of the PDMP internal parameters are computed to check the safety. This safety criterion reflects the new behavior computed through the generalization process, as well as the individual motion safety of the demonstration set. The demonstrations causing unsafe movement are identified and removed. Also, the demolished demonstrations are replaced by proven demonstrations upon this criterion.
This work also presents an extension approach reducing the number of required demonstrations for the PDMP framework. This approach is effective where a single mission consists of multiple sub-tasks and requires numerous demonstrations in generalizing them. The whole trajectories in provided demonstrations are segmented into multiple sub-tasks representing unit motions. Then, multiple PDMPs are formed independently for correlated-segments. The phase-decision process determines which sub-task and associated PDMPs to be executed online, allowing multiple PDMPs to be autonomously configured within an integrated framework. GPR formulations are applied to obtain execution time and regional goal configuration for each sub-task.
Finally, the proposed approach and its extension are validated with the actual experiments of mobile manipulators. The first two scenarios regarding cooperative aerial transportation demonstrate the excellence of the proposed technique in terms of quick computation, generation of efficient movement, and safety assurance. The last scenario deals with two mobile manipulations using ground vehicles and shows the effectiveness of the proposed extension in executing complex missions.์์ฐ ํ์ต ๊ธฐ๋ฒ(Learning from demonstrations, LfD)์ ๋ก๋ด์ด ํน์ ๋์์ ์ํํ ์ ์๋๋ก ํ๋ ์ ๋งํ ๋์ ์์ฑ ๊ธฐ๋ฒ์ด๋ค. ๋ก๋ด ์กฐ์๊ธฐ๊ฐ ์ธ๊ฐ ์ฌํ์์ ๋ค์ํ ์
๋ฌด๋ฅผ ๋์ฒดํด ๊ฐ์ ๋ฐ๋ผ, ๋ค์ํ ์๋ฌด๋ฅผ ์ํํ๋ ๋ก๋ด์ ๋์์ ์์ฑํ๊ธฐ ์ํด LfD ์๊ณ ๋ฆฌ์ฆ๋ค์ ๋๋ฆฌ ์ฐ๊ตฌ๋๊ณ , ์ฌ์ฉ๋๊ณ ์๋ค.
๋ณธ ๋
ผ๋ฌธ์ LfD ๊ธฐ๋ฒ ์ค ๋ชจ์
ํ๋ฆฌ๋จธํฐ๋ธ ๊ธฐ๋ฐ์ ๋์ ์ฌ์์ฑ ์๊ณ ๋ฆฌ์ฆ์ธ Parametric dynamic movement primitives(PDMP)์ ๊ธฐ์ดํ ์๊ณ ๋ฆฌ์ฆ์ ์ ์ํ๋ฉฐ, ์ด๋ฅผ ํตํด ๋ค์ํ ์๋ฌด๋ฅผ ์ํํ๋ ๋ชจ๋ฐ์ผ ์กฐ์๊ธฐ์ ๊ถค์ ์ ์์ฑํ๋ค. ๊ธฐ์กด์ ๋์ ์ฌ์์ฑ ์๊ณ ๋ฆฌ์ฆ๊ณผ ๋ฌ๋ฆฌ, ์ด ์ฐ๊ตฌ๋ ์ ๊ณต๋ ์์ฐ์์ ํํ๋ ๋์์ ๋จ์ํ ์ฌ์์ฑํ๋ ๊ฒ์ ๊ทธ์น์ง ์๊ณ , ์๋ก์ด ํ๊ฒฝ์ ๋ง๊ฒ ์ผ๋ฐํ ํ๋ ๊ณผ์ ์ ํฌํจํ๋ค. ์ด ๋
ผ๋ฌธ์์ ์ ์ํ๋ ์ผ๋ฐํ ๊ณผ์ ์ PDMPs์ ๋ด๋ถ ํ๋ผ๋ฏธํฐ ๊ฐ์ธ ์คํ์ผ ํ๋ผ๋ฏธํฐ์ ํ๊ฒฝ ๋ณ์ ์ฌ์ด์ ๋น์ ํ ๊ด๊ณ๋ฅผ ๊ฐ์ฐ์ค ํ๊ท ๊ธฐ๋ฒ (Gaussian process regression, GPR)์ ์ด์ฉํ์ฌ ์์์ ์ผ๋ก ํํํ๋ค. ์ ์๋ ๊ธฐ๋ฒ์ ๋ํ ์ต์ ์์ฐ๋ฅผ ํ์ตํ๋ ๋ฐฉ์์ ํตํด ๊ฐ๋ ฅํ ์ต์ ์ค์๊ฐ ๊ฒฝ๋ก ๊ณํ ๊ธฐ๋ฒ์ผ๋ก๋ ์์ฉ๋ ์ ์๋ค.
๋ณธ ๋
ผ๋ฌธ์์๋ ๋ํ ๋ก๋ด์ ๊ตฌ๋ ์์ ์ฑ๋ ๊ณ ๋ คํ๋ค. ๊ธฐ์กด ์ฐ๊ตฌ๋ค์์ ๋ค๋ฃจ์ด์ง ์์ฐ ๊ด๋ฆฌ ๊ธฐ์ ์ด ๋ก๋ด์ ๊ตฌ๋ ํจ์จ์ฑ์ ๊ฐ์ ํ๋ ๋ฐฉํฅ์ผ๋ก ์ ์๋ ๊ฒ๊ณผ ๋ฌ๋ฆฌ, ์ด ์ฐ๊ตฌ๋ ๊ฐํ ๊ตฌ์์กฐ๊ฑด์ผ๋ก ๋ก๋ด์ ๊ตฌ๋ ์์ ์ฑ์ ํ๋ณดํ๋ ์์ฐ ๊ด๋ฆฌ ๊ธฐ์ ์ ํตํด ์์ ์ฑ์ ๊ณ ๋ คํ๋ ์๋ก์ด ๋ฐฉ์์ ์ ์ํ๋ค. ์ ์๋ ๋ฐฉ์์ ์คํ์ผ ํ๋ผ๋ฏธํฐ ๊ฐ ์์์ ์์ ์ฑ ๊ธฐ์ค์ ๊ณ์ฐํ๋ฉฐ, ์ด ์์ ๊ธฐ์ค์ ํตํด ์์ฐ์ ์ ๊ฑฐํ๋ ์ผ๋ จ์ ์์
์ ์ํํ๋ค. ๋ํ, ์ ๊ฑฐ๋ ์์๋ฅผ ์์ ๊ธฐ์ค์ ๋ฐ๋ผ ์
์ฆ๋ ์์๋ก ๋์ฒดํ์ฌ ์ผ๋ฐํ ์ฑ๋ฅ์ ์ ํ์ํค์ง ์๋๋ก ์์๋ฅผ ๊ด๋ฆฌํ๋ค. ์ด๋ฅผ ํตํด ๋ค์์ ์์ฐ ๊ฐ๊ฐ ๊ฐ๋ณ ๋์ ์์ ์ฑ ๋ฟ ์๋๋ผ ์จ๋ผ์ธ ๋์์ ์์ ์ฑ๊น์ง ๊ณ ๋ คํ ์ ์์ผ๋ฉฐ, ์ค์๊ฐ ๋ก๋ด ์กฐ์๊ธฐ ์ด์ฉ์ ์์ ์ฑ์ด ํ๋ณด๋ ์ ์๋ค. ์ ์๋ ์์ ์ฑ์ ๊ณ ๋ คํ ์์ฐ ๊ด๋ฆฌ ๊ธฐ์ ์ ๋ํ ํ๊ฒฝ์ ์ ์ ์ค์ ์ด ๋ณ๊ฒฝ๋์ด ๋ชจ๋ ์์ฐ์ ๊ต์ฒดํด์ผ ํ ์ ์๋ ์ํฉ์์ ์ฌ์ฉํ ์ ์๋ ์์ฐ๋ค์ ํ๋ณํ๊ณ , ํจ์จ์ ์ผ๋ก ์ฌ์ฌ์ฉํ๋ ๋ฐ ์์ฉํ ์ ์๋ค.
๋ํ ๋ณธ ๋
ผ๋ฌธ์ ๋ณต์กํ ์๋ฌด์์ ์ ์ฉ๋ ์ ์๋ PDMPs์ ํ์ฅ ๊ธฐ๋ฒ์ธ seg-PDMPs๋ฅผ ์ ์ํ๋ค. ์ด ์ ๊ทผ๋ฐฉ์์ ๋ณต์กํ ์๋ฌด๊ฐ ์ผ๋ฐ์ ์ผ๋ก ๋ณต์๊ฐ์ ๊ฐ๋จํ ํ์ ์์
์ผ๋ก ๊ตฌ์ฑ๋๋ค๊ณ ๊ฐ์ ํ๋ค. ๊ธฐ์กด PDMPs์ ๋ฌ๋ฆฌ seg-PDMPs๋ ์ ์ฒด ๊ถค์ ์ ํ์ ์์
์ ๋ํ๋ด๋ ์ฌ๋ฌ ๊ฐ์ ๋จ์ ๋์์ผ๋ก ๋ถํ ํ๊ณ , ๊ฐ ๋จ์๋์์ ๋ํด ์ฌ๋ฌ๊ฐ์ PDMPs๋ฅผ ๊ตฌ์ฑํ๋ค. ๊ฐ ๋จ์ ๋์ ๋ณ๋ก ์์ฑ๋ PDMPs๋ ํตํฉ๋ ํ๋ ์์ํฌ๋ด์์ ๋จ๊ณ ๊ฒฐ์ ํ๋ก์ธ์ค๋ฅผ ํตํด ์๋์ ์ผ๋ก ํธ์ถ๋๋ค. ๊ฐ ๋จ๊ณ ๋ณ๋ก ๋จ์ ๋์์ ์ํํ๊ธฐ ์ํ ์๊ฐ ๋ฐ ํ์ ๋ชฉํ์ ์ ๊ฐ์ฐ์ค ๊ณต์ ํ๊ท(GPR)๋ฅผ ์ด์ฉํ ํ๊ฒฝ๋ณ์์์์ ๊ด๊ณ์์ ํตํด ์ป๋๋ค. ๊ฒฐ๊ณผ์ ์ผ๋ก, ์ด ์ฐ๊ตฌ๋ ์ ์ฒด์ ์ผ๋ก ์๊ตฌ๋๋ ์์ฐ์ ์๋ฅผ ํจ๊ณผ์ ์ผ๋ก ์ค์ผ ๋ฟ ์๋๋ผ, ๊ฐ ๋จ์๋์์ ํํ ์ฑ๋ฅ์ ๊ฐ์ ํ๋ค.
์ ์๋ ์๊ณ ๋ฆฌ์ฆ์ ํ๋ ๋ชจ๋ฐ์ผ ๋ก๋ด ์กฐ์๊ธฐ ์คํ์ ํตํ์ฌ ๊ฒ์ฆ๋๋ค. ์ธ ๊ฐ์ง์ ์๋๋ฆฌ์ค๊ฐ ๋ณธ ๋
ผ๋ฌธ์์ ๋ค๋ฃจ์ด์ง๋ฉฐ, ํญ๊ณต ์ด์ก๊ณผ ๊ด๋ จ๋ ์ฒซ ๋ ๊ฐ์ง ์๋๋ฆฌ์ค๋ PDMPs ๊ธฐ๋ฒ์ด ๋ก๋ด ์กฐ์๊ธฐ์์ ๋น ๋ฅธ ์ ์์ฑ, ์๋ฌด ํจ์จ์ฑ๊ณผ ์์ ์ฑ ๋ชจ๋ ๋ง์กฑํ๋ ๊ฒ์ ์
์ฆํ๋ค. ๋ง์ง๋ง ์๋๋ฆฌ์ค๋ ์ง์ ์ฐจ๋์ ์ด์ฉํ ๋ ๊ฐ์ ๋ก๋ด ์กฐ์๊ธฐ์ ๋ํ ์คํ์ผ๋ก ๋ณต์กํ ์๋ฌด ์ํ์ ํ๊ธฐ ์ํด ํ์ฅ๋ ๊ธฐ๋ฒ์ธ seg-PDMPs๊ฐ ํจ๊ณผ์ ์ผ๋ก ๋ณํํ๋ ํ๊ฒฝ์์ ์ผ๋ฐํ๋ ๋์์ ์์ฑํจ์ ๊ฒ์ฆํ๋ค.1 Introduction 1
1.1 Motivations 1
1.2 Literature Survey 3
1.2.1 Conventional Motion Planning in Mobile Manipulations 3
1.2.2 Motion Representation Algorithms 5
1.2.3 Safety-guaranteed Motion Representation Algorithms 7
1.3 Research Objectives and Contributions 7
1.3.1 Motion Generalization in Motion Representation Algorithm 9
1.3.2 Motion Generalization with Safety Guarantee 9
1.3.3 Motion Generalization for Complex Missions 10
1.4 Thesis Organization 11
2 Background 12
2.1 DMPs 12
2.2 Mobile Manipulation Systems 13
2.2.1 Single Mobile Manipulation 14
2.2.2 Cooperative Mobile Manipulations 14
2.3 Experimental Setup 17
2.3.1 Test-beds for Aerial Manipulators 17
2.3.2 Test-beds for Robot Manipulators with Ground Vehicles 17
3 Motion Generalization in Motion Representation Algorithm 22
3.1 Parametric Dynamic Movement Primitives 22
3.2 Generalization Process in PDMPs 26
3.2.1 Environmental Parameters 26
3.2.2 Mapping Function 26
3.3 Simulation Results 29
3.3.1 Two-dimensional Hurdling Motion 29
3.3.2 Cooperative Aerial Transportation 30
4 Motion Generalization with Safety Guarantee 36
4.1 Safety Criterion in Style Parameter 36
4.2 Demonstration Management 39
4.3 Simulation Validation 42
4.3.1 Two-dimensional Hurdling Motion 46
4.3.2 Cooperative Aerial Transportation 47
5 Motion Generalization for Complex Missions 51
5.1 Overall Structure of Seg-PDMPs 51
5.2 Motion Segments 53
5.3 Phase-decision Process 54
5.4 Seg-PDMPs for Single Phase 54
5.5 Simulation Results 55
5.5.1 Initial/terminal Offsets 56
5.5.2 Style Generalization 59
5.5.3 Recombination 61
6 Experimental Validation and Results 63
6.1 Cooperative Aerial Transportation 63
6.2 Cooperative Mobile Hang-dry Mission 70
6.2.1 Demonstrations 70
6.2.2 Simulation Validation 72
6.2.3 Experimental Results 78
7 Conclusions 82
Abstract (in Korean) 93Docto
Actuator Trajectory Planning for UAVs with Overhead Manipulator using Reinforcement Learning
In this paper, we investigate the operation of an aerial manipulator system,
namely an Unmanned Aerial Vehicle (UAV) equipped with a controllable arm with
two degrees of freedom to carry out actuation tasks on the fly. Our solution is
based on employing a Q-learning method to control the trajectory of the tip of
the arm, also called end-effector. More specifically, we develop a motion
planning model based on Time To Collision (TTC), which enables a quadrotor UAV
to navigate around obstacles while ensuring the manipulator's reachability.
Additionally, we utilize a model-based Q-learning model to independently track
and control the desired trajectory of the manipulator's end-effector, given an
arbitrary baseline trajectory for the UAV platform. Such a combination enables
a variety of actuation tasks such as high-altitude welding, structural
monitoring and repair, battery replacement, gutter cleaning, skyscrapper
cleaning, and power line maintenance in hard-to-reach and risky environments
while retaining compatibility with flight control firmware. Our RL-based
control mechanism results in a robust control strategy that can handle
uncertainties in the motion of the UAV, offering promising performance.
Specifically, our method achieves 92% accuracy in terms of average displacement
error (i.e. the mean distance between the target and obtained trajectory
points) using Q-learning with 15,000 episode
Enabling technologies for precise aerial manufacturing with unmanned aerial vehicles
The construction industry is currently experiencing a revolution with automation techniques
such as additive manufacturing and robot-enabled construction. Additive Manufacturing (AM)
is a key technology that can o er productivity improvement in the construction industry by
means of o -site prefabrication and on-site construction with automated systems. The key
bene t is that building elements can be fabricated with less materials and higher design freedom
compared to traditional manual methods.
O -site prefabrication with AM has been investigated for some time already, but it has limitations
in terms of logistical issues of components transportation and due to its lack of design
exibility on-site. On-site construction with automated systems, such as static gantry systems
and mobile ground robots performing AM tasks, can o er additional bene ts over o -site
prefabrication, but it needs further research before it will become practical and economical.
Ground-based automated construction systems also have the limitation that they cannot extend
the construction envelope beyond their physical size. The solution of using aerial robots
to liberate the process from the constrained construction envelope has been suggested, albeit
with technological challenges including precision of operation, uncertainty in environmental
interaction and energy e ciency.
This thesis investigates methods of precise manufacturing with aerial robots. In particular,
this work focuses on stabilisation mechanisms and origami-based structural elements that allow
aerial robots to operate in challenging environments. An integrated aerial self-aligning delta
manipulator has been utilised to increase the positioning accuracy of the aerial robots, and
a Material Extrusion (ME) process has been developed for Aerial Additive Manufacturing
(AAM). A 28-layer tower has been additively manufactured by aerial robots to demonstrate the
feasibility of AAM. Rotorigami and a bioinspired landing mechanism demonstrate their abilities
to overcome uncertainty in environmental interaction with impact protection capabilities and
improved robustness for UAV. Design principles using tensile anchoring methods have been
explored, enabling low-power operation and explores possibility of low-power aerial stabilisation.
The results demonstrate that precise aerial manufacturing needs to consider not only just the
robotic aspects, such as
ight control algorithms and mechatronics, but also material behaviour
and environmental interaction as factors for its success.Open Acces
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