[en] Autonomous navigation in unknown environments
populated by humans and other robots is one of the main
challenges when working with mobile robots. In this paper,
we present a new approach to dynamic collision avoidance for
multi-rotor unmanned aerial vehicles (UAVs). A new nonlinear
model predictive control (NMPC) approach is proposed to
safely navigate in a workspace populated by static and/or
moving obstacles. The uniqueness of our approach lies in
its ability to anticipate the dynamics of multiple obstacles,
avoiding them in real-time. Exploiting active set algorithms,
only the obstacles that affect to the UAV during the prediction
horizon are considered at each sample time. We also improve
the fluency of avoidance maneuvers by reformulating the
obstacles as orientable ellipsoids, being less prone to local
minima and allowing the definition of a preferred avoidance
direction. Finally, we present two real-time implementations
based on simulation. The former demonstrates that our
approach outperforms its analog static formulation in terms of
safety and efficiency. The latter shows its capability to avoid
multiple dynamic obstacles.
Research center :
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Automation & Robotics Research Group
Disciplines :
Computer science
Author, co-author :
Castillo Lopez, Manuel ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Sajadi Alamdari, Seyed Amin ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Sanchez Lopez, Jose Luis ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Olivares Mendez, Miguel Angel ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Voos, Holger ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
External co-authors :
no
Language :
English
Title :
Model Predictive Control for Aerial Collision Avoidance in Dynamic Environments
Publication date :
June 2018
Event name :
26th Mediterranean Conference on Control and Automation (MED)
Event date :
19-22 June 2018
Main work title :
26th Mediterranean Conference on Control and Automation (MED), Zadar, Croatia, 19-22 June 2018
Peer reviewed :
Peer reviewed
Focus Area :
Computational Sciences
FnR Project :
FNR10484117 - Robust Emergency Sense-and-avoid Capability For Small Remotely Piloted Aerial Systems, 2015 (01/02/2016-31/01/2019) - Holger Voos