1,006 research outputs found
๋น์ ํ ์ต์ ํ๋ฅผ ์ด์ฉํ ๋ฉํฐ๋กํฐ ํ์ ์ด์ก์ ๊ฒฝ๋ก ๊ณํ ๋ฐ ์ ์ด ๊ธฐ๋ฒ
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ) -- ์์ธ๋ํ๊ต๋ํ์ : ๊ณต๊ณผ๋ํ ๊ธฐ๊ณํญ๊ณต๊ณตํ๋ถ, 2021.8. ๊นํ์ง.๊ฒฝ๋ก ๊ณํ๊ณผ ์ ์ด๋ ์์ ํ๊ณ ์์ ์ ์ผ๋ก ๋ฉํฐ๋กํฐ๋ฅผ ์ด์ฉํ๊ธฐ ์ํด์ ํ์์ ์ธ ์์์ด๋ค. ์ถฉ๋์ ํํผํ๋ฉฐ ํจ์จ์ ์ธ ๊ฒฝ๋ก๋ฅผ ์์ฑํ๊ณ ์ด๋ฅผ ์ค์ ๋ก ์ถ์ข
ํ๊ธฐ ์ํด์๋ ๋์ญํ ๋ชจ๋ธ์ด ๊ณ ๋ ค๋์ด์ผ ํ๋ค. ์ผ๋ฐ ๋ฉํฐ๋กํฐ์ ๋์ญํ ๋ชจ๋ธ์ ๋์ ์ฐจ์์ ๊ฐ์ง ๋น์ ํ์์ผ๋ก ํํ๋๋๋ฐ, ํ์ ์ด์ก ๋ฌผ์ฒด๋ฅผ ์ถ๊ฐํ ๊ฒฝ์ฐ ๊ณ์ฐ์ด ๋์ฑ ๋ณต์กํด์ง๋ค. ๋ณธ ๋
ผ๋ฌธ์ ๋ฉํฐ๋กํฐ๋ฅผ ์ด์ฉํ ํ์ ์ด์ก์ ์์ด ๊ฒฝ๋ก ๊ณํ๊ณผ ์ ์ด์ ๋ํ ํจ์จ์ ์ธ ๊ธฐ๋ฒ์ ์ ์ํ๋ค.
์ฒซ ๋ฒ์งธ๋ก ๋จ์ผ ๋ฉํฐ๋กํฐ๋ฅผ ์ด์ฉํ ํ์ ์ด์ก์ ๋ค๋ฃฌ๋ค. ๋ฌผ์ฒด๊ฐ ๋ณ๋์ ์์ธ์์ดํฐ ์์ด ์ด์ก๋ ๊ฒฝ์ฐ ๋ฌผ์ฒด๋ ๊ธฐ์ฒด์ ์์ง์์ ์ํด์๋ง ์ ์ด๊ฐ ๊ฐ๋ฅํ๋ค. ํ์ง๋ง, ๋์ญํ์์ ๋์ ๋น์ ํ์ฑ์ผ๋ก ์ด์ฉ์ ์ด๋ ค์์ด ์กด์ฌํ๋ค. ์ด๋ฅผ ๊ฒฝ๊ฐ์ํค๊ธฐ ์ํด์ ํ์ ๋์ญํ์์ ๋น์ ํ์ฑ์ ์ค์ด๊ณ ์์ธ ์ ์ด์ ์กด์ฌํ๋ ์๊ฐ ์ง์ฐ์ ๊ณ ๋ คํ์ฌ ๋์ญํ์์ ๊ฐ์ํํ๋ค. ๊ฒฝ๋ก ๊ณํ์ ์์ด์๋ ์ถฉ๋ ํํผ๋ฅผ ์ํด ๊ธฐ์ฒด, ์ผ์ด๋ธ, ๊ทธ๋ฆฌ๊ณ ์ด์ก ๋ฌผ์ฒด๋ฅผ ๋ค๋ฅธ ํฌ๊ธฐ์ ๋ชจ์์ ๊ฐ์ง ํ์์ฒด๋ค๋ก ๊ฐ์ธ๋ฉฐ, ํจ๊ณผ์ ์ด๋ฉด์๋ ๋ ๋ณด์์ ์ธ ๋ฐฉ์์ผ๋ก ์ถฉ๋ ํํผ ๊ตฌ์์กฐ๊ฑด์ ๋ถ๊ณผํ๋ค. Augmented Lagrangian ๋ฐฉ๋ฒ์ ์ด์ฉํ์ฌ ๋น์ ํ ๊ตฌ์์กฐ๊ฑด์ด ๋ถ๊ณผ๋ ๋น์ ํ ๋ฌธ์ ๋ฅผ ์ค์๊ฐ ์ต์ ํํ์ฌ ๊ฒฝ๋ก๋ฅผ ์์ฑํ๋ค. ์์ฑ๋ ๊ฒฝ๋ก๋ฅผ ์ถ์ข
ํ๊ธฐ ์ํด์ Sequential linear quadratic ์๋ฒ๋ฅผ ์ด์ฉํ ๋ชจ๋ธ ์์ธก ์ ์ด๊ธฐ๋ก ์ต์ ์ ์ด ์
๋ ฅ์ ๊ณ์ฐํ๋ค. ์ ์๋ ๊ธฐ๋ฒ์ ์ฌ๋ฌ ์๋ฎฌ๋ ์ด์
๊ณผ ์คํ์ ํตํด ๊ฒ์ฆํ๋ค.
๋ค์์ผ๋ก, ๋ค์ค ๋ฉํฐ๋กํฐ๋ฅผ ์ด์ฉํ ํ์
ํ์ ์ด์ก ์์คํ
์ ๋ค๋ฃฌ๋ค. ํด๋น ์์คํ
์ ์ํ ๋ณ์๋ ๋์ญํ์์์ ์ฐ๊ฒฐ๋(coupled) ํญ์ ๊ฐ์๋ ๊ธฐ์ฒด์ ์์ ๋น๋กํ์ฌ ์ฆ๊ฐํ๊ธฐ ๋๋ฌธ์, ํจ๊ณผ์ ์ธ ๊ธฐ๋ฒ ์์ด๋ ์ต์ ํ์ ๋ง์ ์๊ฐ์ด ์์๋๋ค. ๋์ ๋น์ ํ์ฑ์ ๊ฐ์ง ๋์ญํ์์ ๋ณต์ก์ฑ์ ๋ฎ์ถ๊ธฐ ์ํ์ฌ ๋ฏธ๋ถ ํํ์ฑ์ ์ฌ์ฉํ๋ค. ๊ฒฝ๋ก ๋ํ piece-wise Bernstein ๋คํญ์์ ์ด์ฉํ์ฌ ๋งค๊ฐ๋ณ์ํํ์ฌ ์ต์ ํ ๋ณ์์ ๊ฐ์๋ฅผ ์ค์ธ๋ค. ์ต์ ํ ๋ฌธ์ ๋ฅผ ๋ถํดํ๊ณ ์ถฉ๋ ํํผ ๊ตฌ์์กฐ๊ฑด๋ค์ ๋ํด ๋ณผ๋กํ(convexification)๋ฅผ ์ํํ์ฌ ์ด์ก ๋ฌผ์ฒด์ ๊ฒฝ๋ก์ ์ฅ๋ ฅ์ ๊ฒฝ๋ก์ ๋ํ ๋ณผ๋กํ(convex) ํ์๋ฌธ์ ๋ค์ด ๋ง๋ค์ด์ง๋ค. ์ฒซ ๋ฒ์งธ ํ์๋ฌธ์ ์ธ ๋ฌผ์ฒด ๊ฒฝ๋ก ์์ฑ์์๋, ์ฅ์ ๋ฌผ ํํผ์ ๋ฉํฐ๋กํฐ์ ๊ณต๊ฐ์ ํ๋ณดํ๊ธฐ ์ํ์ฌ ์์ ๋นํ ํต๋ก(safe flight corridor, SFC)์ ์ฌ์ ๊ฐ๊ฒฉ ๊ตฌ์์กฐ๊ฑด์ ๊ณ ๋ คํ์ฌ ์ต์ ํํ๋ค. ๋ค์์ผ๋ก, ์ฅ๋ ฅ ๋ฒกํฐ๋ค์ ๊ฒฝ๋ก๋ ์ฅ์ ๋ฌผ ํํผ์ ์ํธ ์ถฉ๋์ ๋ฐฉ์งํ๊ธฐ ์ํ์ฌ ์์ ๋นํ ์นํฐ(safe flight sector, SFS)์ ์๋ ์์ ๋นํ ์นํฐ(relative safe flight sector, RSFS) ๊ตฌ์์กฐ๊ฑด์ ๋ถ๊ณผํ์ฌ ์ต์ ํํ๋ค. ์๋ฎฌ๋ ์ด์
๊ณผ ์คํ์ผ๋ก ๋ณต์กํ ํ๊ฒฝ์์ ํจ์จ์ ์ธ ๊ฒฝ๋ก ๊ณํ ๊ธฐ๋ฒ์ ์์ฐํ๋ฉฐ ๊ฒ์ฆํ๋ค.Trajectory generation and control are fundamental requirements for safe and stable operation of multi-rotors. The dynamic model should be considered to generate efficient and collision-free trajectories with feasibility. While the dynamic model of a bare multi-rotor is expressed non-linearly with high dimensions which results in computational loads, the suspended load increases the complexity further. This dissertation presents efficient algorithms for trajectory generation and control of multi-rotors with a suspended load.
A single multi-rotor with a suspended load is addressed first. Since the load is suspended through a cable without any actuator, movement of the load must be controlled via maneuvers of the multi-rotor. However, the highly non-linear dynamics of the system results in difficulties. To relive them, the rotational dynamics is simplified to reduce the non-linearity and consider the delay in attitude control. For trajectory generation, the vehicle, cable, and load are considered as ellipsoids with different sizes and shapes, and collision-free constraints are expressed in an efficient and less-conservative way. The augmented Lagrangian method is applied to solve a nonlinear optimization problem with nonlinear constraints in real-time. Model predictive control with the sequential linear quadratic solver is used to track the generated trajectories. The proposed algorithm is validated with several simulations and experiment.
A system with multiple multi-rotors for cooperative transportation of a suspended load is addressed next. As the system has more state variables and coupling terms in the dynamic equation than the system with a single multi-rotor, optimization takes a long time without an efficient method. The differential flatness of the system is used to reduce the complexity of the highly non-linear dynamic equation. The trajectories are also parameterized using piece-wise Bernstein polynomials to decrease the number of optimization variables. By decomposing an optimization problem and performing convexification, convex sub-problems are formulated for the load and the tension trajectories optimization, respectively. In each sub-problem, a light-weight sampling method is used to find a feasible and low-cost trajectory as initialization. In the first sub-problem, the load trajectory is optimized with safe flight corridor (SFC) and clearance constraints for collision avoidance and security of space for the multi-rotors. Then, the tension histories are optimized with safe flight sector (SFS) and relative safe flight sector (RSFS) constraints for obstacle and inter-agent collision avoidance. Simulations and experiments are conducted to demonstrate efficient trajectory generation in a cluttered environment and validate the proposed algorithms.Chapter 1 Introduction 1
1.1 Literature Survey 5
1.2 Contributions 9
1.3 Outline 10
Chapter 2 Single Multi-rotor with a Suspended Load 11
2.1 Dynamics 11
2.2 Trajectory Generation 23
2.3 Optimal Control 31
Chapter 3 Multiple Multi-rotors with a Suspended Load 36
3.1 Problem Setting 36
3.2 Load Trajectory Generation 45
3.3 Tension History Generation 54
Chapter 4 Experimental Validation 68
4.1 Single Multi-rotor with a Suspended Load 68
4.2 Multiple Multi-rotors with a Suspended Load 79
Chapter 5 Conclusion 100
Appendix
A Detailed Derivation of Dierential Flatness 102
B Preliminaries of Bernstein Polynomials 108
B.1 Denition of a Bernstein Polynomial 108
B.2 Convex hull property of a Bernstein Polynomial 110
B.3 Representation of a General Polynomial with Bernstein Basis Polynomials 111
B.4 Representation of the Derivative of a Bernstein Polynomial with Bernstein Basis Polynomials 112
References 113
Abstract (in Korean) 119๋ฐ
Model Predictive Control for Micro Aerial Vehicles: A Survey
This paper presents a review of the design and application of model
predictive control strategies for Micro Aerial Vehicles and specifically
multirotor configurations such as quadrotors. The diverse set of works in the
domain is organized based on the control law being optimized over linear or
nonlinear dynamics, the integration of state and input constraints, possible
fault-tolerant design, if reinforcement learning methods have been utilized and
if the controller refers to free-flight or other tasks such as physical
interaction or load transportation. A selected set of comparison results are
also presented and serve to provide insight for the selection between linear
and nonlinear schemes, the tuning of the prediction horizon, the importance of
disturbance observer-based offset-free tracking and the intrinsic robustness of
such methods to parameter uncertainty. Furthermore, an overview of recent
research trends on the combined application of modern deep reinforcement
learning techniques and model predictive control for multirotor vehicles is
presented. Finally, this review concludes with explicit discussion regarding
selected open-source software packages that deliver off-the-shelf model
predictive control functionality applicable to a wide variety of Micro Aerial
Vehicle configurations
Optimal Control of Multiple Quadrotors for Transporting a Cable Suspended Payload
In this thesis, the main aim is to improve the flight control performance for a cable suspended payload with single and two quadrotors based on optimised control techniques. The study utilised optimal controllers, such as the Linear Quadratic Regulator LQR, the Iterative based LQR (ILQR), the Model Predictive Control MPC and the dynamic game controller to solve tracking control problems in terms of stabilisation, accuracy, constraints and collision avoidance. The LQR control was applied to the system as the first control method and compared with the classical Proportional-Derivative controller PD. It was used to achieve the load
path tracking performance for single and two quadrotors with a cable slung load.
The second controller was ILQR, which was developed based on the LQR control method to deal with the model nonlinearity. The MPC technique was also applied to the linearised nonlinear model LMPC of two quadrotors with a payload suspended by cables and compared with a nonlinear MPC (NMPC). Both MPC controllers LMPC and NMPC considered the constraints imposed on the system states and control inputs. The dynamic game control method was developed based
on an incentive strategy for a leader-follower framework with the consideration of different optimal cost functions. It was applied to the linearised nonlinear model.
Selecting these control techniques led to a number of achievements. Firstly, they improved the system performance in terms of achieving the system stability and reducing the steady-state errors. Secondly, the system parameter uncertainties were taken into consideration by utilising the ILQR controller. Thirdly, the MPC controllers guaranteed the handling of constraints and external disturbances in linear and nonlinear systems. Finally, avoiding collision between the leader and follower robots was achieved by applying the dynamic game controller. The controllers were
tested in MATLAB simulation and verified for various desired predefined trajectories.
In real experiments, these controllers were used as high-level controllers, which produce the optimised trajectory points. Then a low-level controller (PD controller) was used to follow the optimised trajectory points
๋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
A Hybrid Systems Model Predictive Control Framework for AUV Motion Control
A computationally efficient architecture to control formations of Autonomous Underwater Vehicles (AUVs) is presented and discussed in this article. The proposed control structure enables the articulation of resources optimization with state feedback control while keeping the onboard computational burden very low. These properties are critical for AUVs systems as they operate in contexts of scarce resources and high uncertainty or variability. The hybrid nature of the controller enables different modes of operation, notably, in dealing with unanticipated obstacles. Optimization and feedback control are brought in by a novel Model Control Predictive (MPC) scheme constructed in such a way that time-invariant information is used as much as possible in a priori off-line computation
Nonlinear Control Strategies for Outdoor Aerial Manipulators
In this thesis, the design, validation and implementation of nonlinear control strategies for aerial manipulators
{i.e. aerial robots equipped with manipulators{ is studied, with special emphasis on the internal coupling of the
system and its resilience against external disturbances. For the rst, di erent decentralised control strategies
{i.e. using di erent control typologies for each one of the subsystems{ that indirectly take into account this
coupling have been analysed. As a result, a nonlinear strategy composed of two controllers is proposed. A higher
priority is given to the manipulation accuracy, relaxing the platform tracking, and hence obtaining a solution
improving the manipulation capabilities with the surrounding environment. To validate these results, thorough
stability and robustness analyses are provided, both theoretically and in simulation.
On the other hand, a signi cant e ort has been devoted to improving the response and applicability of
robot manipulators used in
ight via control. In particular, the design of controllers for lightweight
exible
manipulators {that reduce the consequences of incidents involving unforeseen contacts{ is analysed. Although
their inherent nature perfectly ts for aerial manipulation applications, the added
exibility produces unwanted
behaviours, such as second-order modes and uncertainties. To cope with them, an adaptable position nonlinear
control strategy is proposed. To validate this contribution, the stability of the approach is studied in theory
and its capabilities are proven in several experimental scenarios. In these, the robustness of the solution against
unforeseen impacts and contact with uncharacterised interfaces is demonstrated.
Subsequently, this strategy has been enriched with {multiaxis{ force control capabilities thanks to the
inclusion of an outer control loop modifying the manipulator reference. Accordingly, this additional applicationfocused
capability is added to the controlled system without loosing the modulated response of the inner-loop
position strategy. It is also worth noting that, thanks to the cascade-like nature of the modi cation, the transition
between position and force control modes is inherently smooth and automatic. The stability of this expanded
strategy has been theoretically analysed and the results validated in a set of experimental scenarios.
To validate the rst nonlinear approach with realistic outdoor simulations before its implementation, a
computational
uid dynamics analysis has been performed to obtain an explicit model of the aerodynamic
forces and torques applied to the blunt-body of the aerial platform in
ight. The results of this study have been
compared to the most common alternative nowadays, being highlighted that the proposed model signi cantly
surpasses this option in terms of accuracy. Moreover, it is worth underscoring that this characterisation could
be also employed in the future to develop control solutions with enhanced rejection capabilities against wind
conditions.
Finally, as the focus of this thesis is on the use of novel control strategies on real aerial manipulation outdoors
to improve their accuracy while performing complex tasks, a modular autopilot solution to be able to implement
them has been also developed. This general-purpose autopilot allows the implementation of new algorithms,
and facilitates their theory-to-experimentation transition. Taking into account this perspective, the proposed
tool employs the simple and widely-known MAS interface and the highly reliable PX4 autopilot as backup, thus
providing a redundant approach to handle unexpected incidents in
ight.En esta tesis se ha estudiado el diseรฑo, validaciรณn e implementaciรณn de estrategias de control
no lineales para robots manipuladores aรฉreos โesto es, robots aรฉreos equipados con un sistema
de manipulaciรณn robรณticaโ, dรกndose especial รฉnfasis a las interacciones internas del sistema y a
su resiliencia frente a efectos externos. Para lo primero, se han analizado diferentes estrategias
de control descentralizado โes decir, que usan tipologรญas de control diferentes para cada uno de
los subsistemasโ, pero que tienen indirectamente en consideraciรณn la interacciรณn entre manipulaciรณn
y vuelo. Como resultado de esta lรญnea, se propone una estretegia de control conformada
por dos controladores. Estos se coordinan de tal forma que se le da prioridad a la manipulaciรณn
sobre el seguimiento de posiciones del vehรญculo, produciรฉndose un sistema de control que mejora
la precisiรณn de las interacciones entre el sistema manipulador y el entorno. Para validar estos resultados,
se ha analizado su estabilidad y robustez tanto teรณricamente como mediante simulaciones
numรฉricas.
Por otro lado, se ha buscado mejorar la respuesta y aplicabilidad de los manipuladores que se
usan en vuelo mediante su control. Dentro de esta tendencia, la tesis se ha centrado en el diseรฑo
de controladores para manipuladores ligeros flexibles, ya que estos permiten reducir el peso del
sistema completo y reducen el riesgo de incidentes debidos a contactos inesperados. Sin embargo,
la flexibilidad de estos produce comportamientos indeseados durante la operaciรณn, como la apariciรณn
de modos de segundo orden y cierta incentidumbre en su comportamiento. Para reducir su
impacto en la precisiรณn de las tareas de manipulaciรณn, se ha desarrollado un controlador no lineal
adaptable. Para validar estos resultados, se ha analizado la estabilidad del sistema teรณricamente y se
han desarrollado una serie de experimentos. En ellos, se ha comprobado su robustez ante impactos
inesperados y contactos con elementos no caracterizados.
Posteriormente, esta estrategia para manipuladores flexibles ha sido ampliada al aรฑadir un bucle
externo que posibilita el control en fuerzas en varias direcciones. Esto permite, mediante un รบnico
controlador, mantener la suave respuesta de la estrategia. Ademรกs cabe destacar que, al contar esta
estrategia con un diseรฑo en cascade, la transiciรณn entre los segmentos de desplazamiento del brazo
y de aplicaciรณn de fuerzas es fluida y automรกtica. La estabilidad de esta estrategia ampliada ha sido
analizada teรณricamente y los resultados han sido validados experimentalmente.
Para validar la primera estrategia mediante simulaciones que representen fielmente las condiciones
en exteriores antes de su implementaciรณn, ha sido necesario realizar un estudio mediante
mecรกnica de fluidos computacional para obtener un modelo explรญcito de las fuerzas y momentos
aerodinรกmicos a los que se efrenta la plataforma en vuelo. Los resultados de este estudio han
sido comparados con la alternativa mรกs empleada actualmente, mostrรกndose que los avances del
mรฉtodo propuesto son sustanciales. Asimismo, es importante destacar que esta caracterizaciรณn podrรญa
tambiรฉn usarse en el futuro para desarrollar controladores con una respuesta mejorada ante
perturbaciones aerodinรกmicas, como en el caso de volar con viento. Finalmente, al ser esta una tesis centrada en las estrategias de control novedosas en sistemas
reales para la mejora de su rendimiento en misiones complejas, se ha desarrollado un autopiloto
modular fรกcilmente modificable para implementarlas. Este permite validar experimentalmente
nuevos algoritmos y facilita la transiciรณn entre teorรญa y prรกctica. Para ello, esta herramienta se
basa en una interfaz sencilla ampliamente conocida por los investigadores de robรณtica, Simulinkยฎ,
y cuenta con un autopiloto de respaldo, PX4, para enfrentarse a los incidentes inesperados que
pudieran surgir en vuelo
MRS Drone: A Modular Platform for Real-World Deployment of Aerial Multi-Robot Systems
This paper presents a modular autonomous Unmanned Aerial Vehicle (UAV)
platform called the Multi-robot Systems (MRS) Drone that can be used in a large
range of indoor and outdoor applications. The MRS Drone features unique
modularity with respect to changes in actuators, frames, and sensory
configuration. As the name suggests, the platform is specially tailored for
deployment within a MRS group. The MRS Drone contributes to the
state-of-the-art of UAV platforms by allowing smooth real-world deployment of
multiple aerial robots, as well as by outperforming other platforms with its
modularity. For real-world multi-robot deployment in various applications, the
platform is easy to both assemble and modify. Moreover, it is accompanied by a
realistic simulator to enable safe pre-flight testing and a smooth transition
to complex real-world experiments. In this manuscript, we present mechanical
and electrical designs, software architecture, and technical specifications to
build a fully autonomous multi UAV system. Finally, we demonstrate the full
capabilities and the unique modularity of the MRS Drone in various real-world
applications that required a diverse range of platform configurations.Comment: 49 pages, 39 figures, accepted for publication to the Journal of
Intelligent & Robotic System
Wireless model-based predictive networked control system over cooperative wireless network
Owing to their distributed architecture, networked control systems (NCSs) are proven to be feasible in scenarios where a spatially distributed feedback control system is required. Traditionally, such NCSs operate over real-time wired networks. Recently, in order to achieve the utmost flexibility, scalability, ease of deployment, and maintainability, wireless networks such as IEEE 802.11 wireless local area networks (LANs) are being preferred over dedicated wired networks. However, conventional NCSs with event-triggered controllers and actuators cannot operate over such general purpose wireless networks since the stability of the system is compromised due to unbounded delays and unpredictable packet losses that are typical in the wireless medium. Approaching the wireless networked control problem from two perspectives, this work introduces a practical wireless NCS and an implementation of a cooperative medium access control protocol that work jointly to achieve decent control under severe impairments, such as unbounded delay, bursts of packet loss and ambient wireless traffic. The proposed system is evaluated on a dedicated test platform under numerous scenarios and significant performance gains are observed, making cooperative communications a strong candidate for improving the reliability of industrial wireless networks
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