No abstract

Obstacle avoidance represents a fundamental challenge for unmanned aerial vehicle navigation. This is particularly relevant for low altitude flight, which is highly subjected to collisions, causing property damage or even compromise human safety. Autonomous navigation algorithms address this problem and are applied in various tasks. However, this approach is usually overshadowed by unreliable results in uncertain environments. In contrast, human pilots are able to maneuver vehicles in complex situations, in which an algorithm would no offer a reliable performance. This article explores a novel configuration of assisted flying and implements an experimental setup in order to prove its efficacy. The user controls an unmanned aerial vehicle with a force feedback device, where simultaneously an assisted navigation algorithm can manipulate this apparatus to divert the unmanned aerial vehicle from its path. Experiments confirm the authors’ hypothesis that the unmanned aerial vehicle is deviated or maintains the same course at the operator’s will. Unlike conventional controllers that dictate roll, pitch, and yaw, this implementation uses direct mapping between the position represented by the haptic device and the unmanned aerial vehicle. This configuration applies feedback before the unmanned aerial vehicle has reached the position referenced by the haptic device, providing valuable time for the user to make the necessary path correction.

No abstract

A method for tracking the 3D pose and controlling an unmanned aerial vehicle (UAV) is presented. Planar faces of target vehicle are tracked using the Efficient Second Order Minimization algorithm, one at a time. Homography decomposition is used to recover the 3D pose of the textured planar face that is being tracked. Then, a cuboid model is used to estimate the homographies of the remaining faces. This allows switching faces as the object moves and rotates. Cascade and single PID controllers are used to control the vehicle pose. Results confirm that this approach is effective for real-time aerial vehicle control using only one camera. This is a step towards an automatic 3D pose tracking system.