Post-Stall Navigation with Fixed-Wing UAVs using Onboard Vision

Polevoy, Adam; Basescu, Max; Scheuer, Luca; Moore, J. Russell

doi:10.1109/icra46639.2022.9812099

Cited by 3 publications

(3 citation statements)

References 30 publications

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“…For sensing and obstacle detection technologies, classical vision sensors (monocular camera, stereo camera, and RGB‐D camera) are the popular sensing choice to perceive obstacles as their light weight and relatively high perception accuracy (Zaffar et al, 2018). When choosing the classical vision sensors as the front end, the DF and occupancy map are commonly chosen as the back end because they reasonably estimate the perception uncertainty (Lu et al, 2022; Polevoy et al, 2022; Tordesillas et al, 2021; B. Zhou et al, 2020). Considering the perception efficiency, the depth features and image features are also widely used in high speed flight (Carrio et al, 2020; Hatch et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

“…The depth camera can also be used for generating point clouds. Polevoy et al (2022) used a depth camera for Fixed‐wings MAV autonomous flight. Their method used the depth camera to develop a lightweight local map through a point cloud mapping framework, Nanomap (Florence et al, 2018).…”

Section: Mavs Onboard Sensingmentioning

confidence: 99%

“…In the non‐cooperative environment, the MAV need to keep updating its sensing information to generate collision avoidance maneuver. The pictures of the fixed‐wing MAV, and the flapping‐wing MAV are ATS Ai450 v2.5 ER from Edge 540 (mini) (Polevoy et al, 2022), and Flapper Drones from Crazyflie, respectively. MAV, micro aerial vehicle.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A comprehensive survey on non‐cooperative collision avoidance for micro aerial vehicles: Sensing and obstacle detection

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Fasano

Carrio³

et al. 2023

Journal of Field Robotics

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In recent years, unmanned aerial vehicles (UAVs) have been confirmed as a powerful tool for countless applications in nearly every industry, in which collision avoidance plays a vital role for micro aerial vehicles (MAVs) performing autonomous missions.The complexity of collision avoidance systems is directly related to the complexity of the navigation environment, the avoidance of highly uncertain obstacles being a more thoroughly researched critical aspect. The non-cooperative collision avoidance strategy is necessary when communication between the agents is unavailable, under the case with many existing passive obstacles and non-cooperative flying objects. In this article, we review the topic of non-cooperative collision avoidance for MAVs in dynamic environments by covering two main issues: onboard sensing and obstacle description algorithms. A detailed discussion of these two topics is provided to face non-cooperative collision avoidance technology for MAVs. Finally, the paper summarizes the open challenges and foreseen future directions in this field.

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