J.L. Paneque

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2papers
211citations

2 Papers

5.5ROFeb 13, 2022Code
Perception-Aware Perching on Powerlines with Multirotors

Julio L. Paneque, Jose Ramiro Martínez de Dios, Aníbal Ollero. Drew Hanover et al.

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 paper 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 degrees. The developed code is available online at: https://github.com/grvcPerception/pa_powerline_perching

11.6ROJan 25, 2021
The GRIFFIN Perception Dataset: Bridging the Gap Between Flapping-Wing Flight and Robotic Perception

J. P. Rodríguez-Gómez, R. Tapia, J. L. Paneque et al.

The development of automatic perception systems and techniques for bio-inspired flapping-wing robots is severely hampered by the high technical complexity of these platforms and the installation of onboard sensors and electronics. Besides, flapping-wing robot perception suffers from high vibration levels and abrupt movements during flight, which cause motion blur and strong changes in lighting conditions. This paper presents a perception dataset for bird-scale flapping-wing robots as a tool to help alleviate the aforementioned problems. The presented data include measurements from onboard sensors widely used in aerial robotics and suitable to deal with the perception challenges of flapping-wing robots, such as an event camera, a conventional camera, and two Inertial Measurement Units (IMUs), as well as ground truth measurements from a laser tracker or a motion capture system. A total of 21 datasets of different types of flights were collected in three different scenarios (one indoor and two outdoor). To the best of the authors' knowledge this is the first dataset for flapping-wing robot perception.