Robust Active Visual Perching With Quadrotors on Inclined Surfaces

Autonomous micro aerial vehicles are deployed for a variety of tasks including surveillance and monitoring. Perching and staring allow the vehicle to monitor targets without flying, saving battery power and increasing the overall mission time without the need to frequently replace batteries. This article addresses the active visual perching (AVP) control problem to autonomously perch on inclined surfaces up to 90 degrees C. Our approach generates dynamically feasible trajectories to navigate and perch on a desired target location while taking into account actuator and field-of-view constraints. By replanning in midflight, we take advantage of more accurate target localization increasing the perching maneuver's robustness to target localization or control errors. We leverage the Karush-Kuhn-Tucker (KKT) conditions to identify the compatibility between planning objectives and the visual sensing constraint during the planned maneuver. Furthermore, we experimentally identify the corresponding boundary conditions that maximize the spatio-temporal target visibility during the perching maneuver. The proposed approach works on-board in real time with significant computational constraints relying exclusively on cameras and an inertial measurement unit. Experimental results validate the proposed approach and show a higher success rate as well as increased target interception precision and accuracy compared to a one-shot planning approach, while still retaining aggressive capabilities with flight envelopes that include large displacements from the hover position on inclined surfaces up to 90(degrees)C, angular speeds up to 750(degrees)/s, and accelerations up to 10 m/s(2).

Automated summary

This article focuses on the problem of active visual perching (AVP) control to autonomously perch on inclined surfaces. The proposed approach generates feasible trajectories and takes into account actuator and field-of-view constraints. Experimental results show that the approach improves target interception precision and accuracy compared to one-shot planning.