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Custom Non-Linear Model Predictive Control for Obstacle Avoidance in Indoor and Outdoor Environments

Laban, Lara LU orcid ; Wzorek, Mariusz ; Rudol, Piotr and Persson, Tommy (2024)
Abstract
Navigating complex environments requires Unmanned Aerial Vehicles (UAVs) and autonomous systems to perform trajectory tracking and obstacle avoidance in realtime. While many control strategies have effectively utilized linear approximations, addressing the non-linear dynamics of UAV, especially in obstacle-dense environments, remains a key challenge that requires further research. This paper introduces a Non-linear Model Predictive Control (NMPC) framework for the DJI Matrice 100, addressing these challenges by using a dynamic model and B-spline interpolation for smooth reference trajectories, ensuring minimal deviation while respecting safety constraints. The framework supports various trajectory types and employs a penalty-based cost... (More)
Navigating complex environments requires Unmanned Aerial Vehicles (UAVs) and autonomous systems to perform trajectory tracking and obstacle avoidance in realtime. While many control strategies have effectively utilized linear approximations, addressing the non-linear dynamics of UAV, especially in obstacle-dense environments, remains a key challenge that requires further research. This paper introduces a Non-linear Model Predictive Control (NMPC) framework for the DJI Matrice 100, addressing these challenges by using a dynamic model and B-spline interpolation for smooth reference trajectories, ensuring minimal deviation while respecting safety constraints. The framework supports various trajectory types and employs a penalty-based cost function for control accuracy in tight maneuvers. The framework utilizes CasADi for efficient real-time optimization, enabling the UAV to maintain robust operation even under tight computational constraints. Simulation and real-world indoor and outdoor experiments demonstrated the NMPC ability to adapt to disturbances, resulting in smooth, collision-free navigation. (Less)
Please use this url to cite or link to this publication:
author
; ; and
organization
publishing date
type
Working paper/Preprint
publication status
published
subject
publisher
arXiv.org
DOI
10.48550/arXiv.2410.02732
project
UAS@LU: Autonomous Flight
RobotLab LTH
language
English
LU publication?
yes
id
2960349a-9c32-4f96-b5e6-e1b5dd209ba7
date added to LUP
2024-11-26 09:36:03
date last changed
2025-04-04 14:54:41
@misc{2960349a-9c32-4f96-b5e6-e1b5dd209ba7,
  abstract     = {{Navigating complex environments requires Unmanned Aerial Vehicles (UAVs) and autonomous systems to perform trajectory tracking and obstacle avoidance in realtime. While many control strategies have effectively utilized linear approximations, addressing the non-linear dynamics of UAV, especially in obstacle-dense environments, remains a key challenge that requires further research. This paper introduces a Non-linear Model Predictive Control (NMPC) framework for the DJI Matrice 100, addressing these challenges by using a dynamic model and B-spline interpolation for smooth reference trajectories, ensuring minimal deviation while respecting safety constraints. The framework supports various trajectory types and employs a penalty-based cost function for control accuracy in tight maneuvers. The framework utilizes CasADi for efficient real-time optimization, enabling the UAV to maintain robust operation even under tight computational constraints. Simulation and real-world indoor and outdoor experiments demonstrated the NMPC ability to adapt to disturbances, resulting in smooth, collision-free navigation.}},
  author       = {{Laban, Lara and Wzorek, Mariusz and Rudol, Piotr and Persson, Tommy}},
  language     = {{eng}},
  month        = {{10}},
  note         = {{Working Paper}},
  publisher    = {{arXiv.org}},
  title        = {{Custom Non-Linear Model Predictive Control for Obstacle Avoidance in Indoor and Outdoor Environments}},
  url          = {{http://dx.doi.org/10.48550/arXiv.2410.02732}},
  doi          = {{10.48550/arXiv.2410.02732}},
  year         = {{2024}},
}