A contribution to vision-based autonomous helicopter flight in urban environments

Abstract

A navigation strategy that exploits the optic flow and inertial information to continuously avoid collisions with both lateral and frontal obstacles has been used to control a simulated helicopter flying autonomously in a textured urban environment. Experimental results demonstrate that the corresponding controller generates cautious behavior, whereby the helicopter tends to stay in the middle of narrow corridors, while its forward velocity is automatically reduced when the obstacle density increases. When confronted with a frontal obstacle, the controller is also able to generate a tight U-turn that ensures the UAV's survival. The paper provides comparisons with related work, and discusses the applicability of the approach to real platforms.

Visual simulator for MavLink-protocol-based UAV, applied for search and analyze task

“…They are exceptionally critical when operating in urban environment and during inspection tasks. In such applications robot is required not only to avoid obstacles (even when flying on high altitude) but also to position itself precisely to execute given tasks [10], [19], [20] The scenario presented in section V require utilization of precise navigation as well. To support UAV with necessary capabilities the following methods were provided: 1) Calculating position of Points Of Interest (later referred to as POI) in the area photographed from high altitude.…”

“…1177-1185 DOI: 10.15439/2017F184 ISSN 2300-5963 ACSIS, Vol. 11 IEEE Catalog Number: CFP1785N-ART c 2017, PTIrobotics and can contribute to the solution of more complex tasks, such as scene understanding and robot localization [9], [10].…”

Optic Flow‐Based Vision System for Autonomous 3D Localization and Control of Small Aerial Vehicles

“…These behaviors originated in research on insect flight are appropriate for implementation in a biomimetic autopilot for small UAVs and robotics in general [17,18,19]. Potential applications of optical flow for small aerial vehicles include altitude control and terrain following [20,21], autonomous landing [20,22,23] and obstacles avoidance [24,25,26].…”

Evolution of visual controllers for obstacle avoidance in mobile robotics

“…This technique is inspired by the strategy of flying insects, which use it to center themselves when flying in a narrow environment. The insects naturally take advantage of their pair of lateral sensors, however, the technique has been used with success on an autonomous helicopter using a wide field single camera [23].…”