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Contribution Details
| Type | Journal Article |
| Scope | Discipline-based scholarship |
| Title | Perception-Aware Perching on Powerlines With Multirotors |
| Organization Unit | |
| Authors |
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| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Journal Title | IEEE Robotics and Automation Letters |
| Publisher | Institute of Electrical and Electronics Engineers |
| Geographical Reach | international |
| ISSN | 2377-3766 |
| Volume | 7 |
| Number | 2 |
| Page Range | 3077 - 3084 |
| Date | 2022 |
| Abstract Text | 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 |
| Digital Object Identifier | 10.1109/LRA.2022.3145514 |
| Other Identification Number | merlin-id:22183 |
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