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Power Line Inspection Tasks With Multi-Aerial Robot Systems Via Signal Temporal Logic Specifications
Open accessAbstract
A framework for computing feasible and constrained trajectories for a fleet of quad-rotors leveraging on Signal Temporal Logic (STL) specifications for power line inspection tasks is proposed in this letter. The planner allows the formulation of complex missions that avoid obstacles and maintain a safe distance between drones while performing the planned mission. An optimization problem is set to generate optimal strategies that satisfy these specifications and also take vehicle constraints into account. Further, an event-triggered replanner is proposed to reply to unforeseen events and external disturbances. An energy minimization term is also considered to implicitly save quad-rotors battery life while carrying out the mission. Numerical simulations in MATLAB and experimental results show the validity and the effectiveness of the proposed approach, and demonstrate its applicability in real-world scenarios.
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“In these works, the authors make use of generic dynamic models which can represent a large class of multirotor UAVs [18]. This allows to formulate the problem for both standard coplanar under-actuated vehicles as well as for fully-actuated platforms with tilted propellers characterized by a fixed geometry (i.e., propellers' orientation is fixed).”
From A Perception-Aware NMPC for Vision-Based Target Tracking and Collision Avoidance with a Multi-Rotor UAV (2022) · cites this paper as [18]
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“This approach allows to attain the perception-objectives, but it can cause problems as the NMPC generator does not consider the limitations posed by the low-level controller [8], [18]. As a consequence, the generated trajectory could violate the actuation limitations of the UAV resulting in an unfeasible solution.”
From A Perception-Aware NMPC for Vision-Based Target Tracking and Collision Avoidance with a Multi-Rotor UAV (2022) · cites this paper as [18]
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Research works printed but not linked: 20
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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
“In the last decade, the research and commercial interest on Unmanned Aerial Vehicles (UAVs) has exploded as demonstrated by the growing number of applications, such as infrastructure monitoring [1], aerial filming [2], surveillance and search and rescue missions [3] and wireless communications [4].”
BackgroundSelf-citation
Metadata
- Authors
- Silano, G., Baca, T., Penicka, R., Liuzza, D., & Saska, M.
- Affiliations
- Czech Technical University in Prague (CZ)
- National Agency for New Technologies, Energy and Sustainable Economic Development (IT)
- Venue
- IEEE Robotics and Automation Letters
- Published
- 2021-04
- Volume
- 6
- Issue
- 2
- Pages
- 4169-4176
- ISSN
- 2377-3766 (ISSN-L)
- 2377-3774
- OpenAlex
- W3137252442
- Cited by
- 58
- References
- 0
- Citations by year
- 2026: 6 · 2025: 9 · 2024: 12 · 2023: 13 · 2022: 10 · 2021: 8
- FWCI
- 3.87
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Robotics and Automation Letters (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- European Commission (871479)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Robotic Path Planning Algorithms
- Physical Sciences › Engineering › Mechanical Engineering › Power Line Inspection Robots
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Formal Methods in Verification
- Keywords
- power line inspection, multi-robot trajectory planning, signal temporal logic, obstacle avoidance, energy minimization, battery life
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- Silano, G., Baca, T., Penicka, R., Liuzza, D., & Saska, M. (2021). Power Line Inspection Tasks With Multi-Aerial Robot Systems Via Signal Temporal Logic Specifications. IEEE Robotics and Automation Letters. https://doi.org/10.1109/lra.2021.3068114
