Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Motion planning and control of underactuated systems over optimized trajectories
Download
Thesis_Eminalp.pdf
Date
2024-9-5
Author
Koyuncu, Eminalp
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
28
views
22
downloads
Cite This
In this work, we propose an optimal control strategy that is robust and capable of running real-time for nonlinear underactuated systems. Our method combines an optimization-based trajectory planner with the feedback motion planning methods. We combine the local controllers created by the feedback motion planning algorithms to generate a global trajectory with trajectory optimization, taking the underactuation into consideration. We follow the generated trajectory using a global controller. We first generate a Sparse Neighborhood Graph (SNG) in the obstacle-free region of the configuration space. We generate waypoints at each node intersection on the graph, and hierarchical waypoints are identified along the shortest path from start to goal. We then run an optimization algorithm, taking the system dynamics and constraints into account to minimize input effort and generate trajectories between waypoints using a receding horizon optimization strategy. Finally, we use a linear time-varying (LTV) model predictive control (MPC) policy to track the generated trajectory, ensuring constraints are satisfied during system operation. We tested our algorithm on underactuated unmanned surface vehicles (USVs) to drive them in the presence of workspace obstacles, and input and speed constraints. We used 2 different USV models, one implemented in MATLAB and the other is Clearpath Robotics Heron USV on a ROS-Gazebo simulation. We compared our results with previous works, considering real-time performance and robustness. Our work showed superior results regarding all the criterion. However, one drawback of our method is the computational time and power required for the offline planning action.
Subject Keywords
Trajectory optimization
,
Underactuated systems
,
Model predictive control
URI
https://hdl.handle.net/11511/111305
Collections
Graduate School of Natural and Applied Sciences, Thesis
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
E. Koyuncu, “Motion planning and control of underactuated systems over optimized trajectories,” M.S. - Master of Science, Middle East Technical University, 2024.