The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Model Predictive Control for Aerial Collision Avoidance in Dynamic Environments
Open accessAbstract
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.
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“A single integrator model was used to characterize the motion of the target. This model is a canonical example of a first order control system. As described in [24], demonstrating numerical convergence with this model guarantees convergence even with more complex models, as long as the control law is suitably adapted to the various motion constraints.”
From A Perception-Aware NMPC for Vision-Based Target Tracking and Collision Avoidance with a Multi-Rotor UAV (2022) · cites this paper as [24]
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Cites
Learning deep control policies for autonomous aerial vehicles with MPC-guided policy search
Zhang, T., Kahn, G., Levine, S., & Abbeel, P. (2016) · 2016 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2016.7487175 · Cited by 452 · cited 1×
“Recent work using NMPC often simplifies this problem by considering only the closest obstacle [11] or reducing the obstacles as bi-dimensional static [12], [13] or dynamic [14] constraints.”
Method
Research works printed but not linked: 18
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Cited by
Sorted by citations
Dmytruk, A., Silano, G., Bicego, D., Licea, D. B., & Saska, M. (2022) · 2022 International Conference on Unmanned Aircraft Systems (ICUAS) · doi:10.1109/icuas54217.2022.9836071 · Cited by 8
“Similarly to [22], [23], this approach allows relaxing the constraint on the obstacle avoidance to guarantee feasible solutions in tight situations by introducing a penalty term in”
Background
Metadata
- Authors
- Castillo-Lopez, M., Sajadi-Alamdari, S. A., Sanchez-Lopez, J. L., Olivares-Mendez, M. A., & Voos, H.
- Affiliations
- University of Luxembourg (LU)
- Venue
- 2018 26th Mediterranean Conference on Control and Automation (MED)
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 1-6
- ISSN
- 2325-369X (ISSN-L)
- OpenAlex
- W2888152797
- Cited by
- 44
- References
- 1
- Citations by year
- 2026: 3 · 2025: 7 · 2024: 9 · 2023: 5 · 2022: 5 · 2021: 9 · 2020: 3 · 2019: 3
- FWCI
- 1.13
- Type
- conference paper
- Language
- en
- Open access
- green
- orbilu.uni.lu
- Licence
- cc-by-nc-sa
- Locations
- Mediterranean Conference on Control and Automation (MED) (publishedVersion)
- Open Repository and Bibliography (University of Luxembourg) (submittedVersion, cc-by-nc-sa)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Robotic Path Planning Algorithms
- Physical Sciences › Engineering › Control and Systems Engineering › Advanced Control Systems Optimization
- Physical Sciences › Engineering › Control and Systems Engineering › Control and Dynamics of Mobile Robots
- Keywords
- nonlinear model predictive control, multirotor drones, dynamic obstacles, active set algorithm, real-time navigation, autonomous navigation, human-populated environments
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- The paper
- Castillo-Lopez, M., Sajadi-Alamdari, S. A., Sanchez-Lopez, J. L., Olivares-Mendez, M. A., & Voos, H. (2018). Model Predictive Control for Aerial Collision Avoidance in Dynamic Environments. 2018 26th Mediterranean Conference on Control and Automation (MED). https://doi.org/10.1109/med.2018.8442967
