Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2024 European Control Conference, ECC 2024 |
| Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
| Seiten | 1208-1213 |
| Seitenumfang | 6 |
| ISBN (elektronisch) | 9783907144107 |
| ISBN (Print) | 979-8-3315-4092-0 |
| Publikationsstatus | Veröffentlicht - 25 Juni 2024 |
| Veranstaltung | 2024 European Control Conference, ECC 2024 - Stockholm, Schweden Dauer: 25 Juni 2024 → 28 Juni 2024 |
Abstract
This work considers the problem of robots with challenging dynamics having to solve motion tasks that consist in transitioning from an initial state to a goal state in an environment that is obstructed by obstacles. We propose a novel combination of methods from motion planning and iterative learning control to solve these motion tasks. The proposed method only requires an approximate, linear model of the nonlinear, possibly underactuated robot dynamics. The proposed method employs the approximate, linear model in a kinodynamic rapidly exploring random tree to plan a state trajectory that solves the motion task. Based on the distance to the obstacles, the most relevant samples of the planned trajectory are selected as reference points. Lastly, point-to-point iterative learning control is employed to learn a feedforward input trajectory that leads to the state trajectory precisely tracking the reference points despite the robot's nonlinear real-world dynamics. The proposed method is validated in real-world experiments on a two-wheeled inverted pendulum robot that has to solve a motion task that requires the robot to perform an agile motion to dive beneath an obstacle.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Steuerung und Optimierung
- Mathematik (insg.)
- Modellierung und Simulation
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2024 European Control Conference, ECC 2024. Institute of Electrical and Electronics Engineers Inc., 2024. S. 1208-1213.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Solving Motion Tasks with Challenging Dynamics by Combining Kinodynamic Motion Planning and Iterative Learning Control
AU - Meindl, Michael
AU - Campe, Ferdinand
AU - Lehmann, Dustin
AU - Seel, Thomas
N1 - Publisher Copyright: © 2024 EUCA.
PY - 2024/6/25
Y1 - 2024/6/25
N2 - This work considers the problem of robots with challenging dynamics having to solve motion tasks that consist in transitioning from an initial state to a goal state in an environment that is obstructed by obstacles. We propose a novel combination of methods from motion planning and iterative learning control to solve these motion tasks. The proposed method only requires an approximate, linear model of the nonlinear, possibly underactuated robot dynamics. The proposed method employs the approximate, linear model in a kinodynamic rapidly exploring random tree to plan a state trajectory that solves the motion task. Based on the distance to the obstacles, the most relevant samples of the planned trajectory are selected as reference points. Lastly, point-to-point iterative learning control is employed to learn a feedforward input trajectory that leads to the state trajectory precisely tracking the reference points despite the robot's nonlinear real-world dynamics. The proposed method is validated in real-world experiments on a two-wheeled inverted pendulum robot that has to solve a motion task that requires the robot to perform an agile motion to dive beneath an obstacle.
AB - This work considers the problem of robots with challenging dynamics having to solve motion tasks that consist in transitioning from an initial state to a goal state in an environment that is obstructed by obstacles. We propose a novel combination of methods from motion planning and iterative learning control to solve these motion tasks. The proposed method only requires an approximate, linear model of the nonlinear, possibly underactuated robot dynamics. The proposed method employs the approximate, linear model in a kinodynamic rapidly exploring random tree to plan a state trajectory that solves the motion task. Based on the distance to the obstacles, the most relevant samples of the planned trajectory are selected as reference points. Lastly, point-to-point iterative learning control is employed to learn a feedforward input trajectory that leads to the state trajectory precisely tracking the reference points despite the robot's nonlinear real-world dynamics. The proposed method is validated in real-world experiments on a two-wheeled inverted pendulum robot that has to solve a motion task that requires the robot to perform an agile motion to dive beneath an obstacle.
UR - http://www.scopus.com/inward/record.url?scp=85200535640&partnerID=8YFLogxK
U2 - 10.23919/ECC64448.2024.10590944
DO - 10.23919/ECC64448.2024.10590944
M3 - Conference contribution
AN - SCOPUS:85200535640
SN - 979-8-3315-4092-0
SP - 1208
EP - 1213
BT - 2024 European Control Conference, ECC 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 European Control Conference, ECC 2024
Y2 - 25 June 2024 through 28 June 2024
ER -