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Perception-Aware Perching on Powerlines With Multirotors


Paneque, Julio L; Dios, Jose Ramiro Martinez-de; Ollero, Anibal; Hanover, Drew; Sun, Sihao; Romero, Angel; Scaramuzza, Davide (2022). Perception-Aware Perching on Powerlines With Multirotors. IEEE Robotics and Automation Letters, 7(2):3077-3084.

Abstract

Multirotor aerial robots are becoming widely used for the inspection of powerlines. To enable continuous, robust inspection without human intervention, the robots must be able to perch on the powerlines to recharge their batteries. Highly versatile perching capabilities are necessary to adapt to the variety of configurations and constraints that are present in real powerline systems. This letter presents a novel perching trajectory generation framework that computes perception-aware, collision-free, and dynamically-feasible maneuvers to guide the robot to the desired final state. Trajectory generation is achieved via solving a Nonlinear Programming problem using the Primal-Dual Interior Point method. The problem considers the full dynamic model of the robot down to its single rotor thrusts and minimizes the final pose and velocity errors while avoiding collisions and maximizing the visibility of the powerline during the maneuver. The generated maneuvers consider both the perching and the posterior recovery trajectories. The framework adopts costs and constraints defined by efficient mathematical representations of powerlines, enabling online onboard execution in resource-constrained hardware. The method is validated on-board an agile quadrotor conducting powerline inspection and various perching maneuvers with final pitch values of up to 180 ∘ . The developed code is available online at: https://github.com/grvcPerception/pa_powerline_perching

Abstract

Multirotor aerial robots are becoming widely used for the inspection of powerlines. To enable continuous, robust inspection without human intervention, the robots must be able to perch on the powerlines to recharge their batteries. Highly versatile perching capabilities are necessary to adapt to the variety of configurations and constraints that are present in real powerline systems. This letter presents a novel perching trajectory generation framework that computes perception-aware, collision-free, and dynamically-feasible maneuvers to guide the robot to the desired final state. Trajectory generation is achieved via solving a Nonlinear Programming problem using the Primal-Dual Interior Point method. The problem considers the full dynamic model of the robot down to its single rotor thrusts and minimizes the final pose and velocity errors while avoiding collisions and maximizing the visibility of the powerline during the maneuver. The generated maneuvers consider both the perching and the posterior recovery trajectories. The framework adopts costs and constraints defined by efficient mathematical representations of powerlines, enabling online onboard execution in resource-constrained hardware. The method is validated on-board an agile quadrotor conducting powerline inspection and various perching maneuvers with final pitch values of up to 180 ∘ . The developed code is available online at: https://github.com/grvcPerception/pa_powerline_perching

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Scopus Subject Areas:Physical Sciences > Control and Systems Engineering
Physical Sciences > Biomedical Engineering
Physical Sciences > Human-Computer Interaction
Physical Sciences > Mechanical Engineering
Physical Sciences > Computer Vision and Pattern Recognition
Physical Sciences > Computer Science Applications
Physical Sciences > Control and Optimization
Physical Sciences > Artificial Intelligence
Scope:Discipline-based scholarship (basic research)
Language:English
Date:2022
Deposited On:17 Feb 2022 09:58
Last Modified:27 Apr 2024 01:35
Publisher:Institute of Electrical and Electronics Engineers
ISSN:2377-3766
OA Status:Green
Publisher DOI:https://doi.org/10.1109/LRA.2022.3145514
Other Identification Number:merlin-id:22183
  • Content: Accepted Version