ICUAS 2020 Paper Abstract


Paper ThA1.5

Zhang, Zhouyu (Nanjing University of Aeronautics and Astronautics), Zhang, Youmin (Concordia University), Cao, Yunfeng (Nanjing University of Aeronautics and Astronautics)

Monocular Vision-based Obstacle Avoidance Trajectory Planning for Unmanned Aerial Vehicles

Scheduled for presentation during the Regular Session "See and Avoid Systems I" (ThA1), Thursday, September 3, 2020, 11:20−11:40, Macedonia Hall

2020 International Conference on Unmanned Aircraft Systems (ICUAS), September 1-4, 2020 (Postponed from June 9-12, 2020), Athens, Greece

This information is tentative and subject to change. Compiled on September 25, 2020

Keywords See-and-avoid Systems, Path Planning


Monocular vision has become a promising sensing method for Unmanned Aerial Vehicle (UAV) Sense and Avoid (SAA), with the advantages of low cost and small size. However, obstacle perception capability of monocular vision is limited due to the restriction of optical imaging properties, which have significant influence on avoidance trajectory planning. By considering the characteristics of monocular vision-based optical measurement, a receding horizon-based collision avoidance trajectory planning algorithm is proposed for eliminating the hazard of both static and dynamic obstacles in this paper. This paper aims at dealing with two core problems: 1) how to localize obstacle via monocular vision; 2) how to generate a collision free trajectory with partially observed obstacle information obtained by monocular vision. To solve the first problem, monocular vision-based optical measurement for obstacle estimation is firstly analyzed and orthogonal iteration-based localization is further adopted. To solve the second problem, with the constraints containing obstacle avoidance and UAV aerodynamics, a receding horizon-based collision-free trajectory planning method is proposed. Simulation results demonstrate the algorithm proposed in this paper increases the safety level of UAV and is capable of avoiding both static and dynamic obstacles.



All Content © PaperCept, Inc.

This site is protected by copyright and trademark laws under US and International law.
All rights reserved. © 2002-2020 PaperCept, Inc.
Page generated 2020-09-25  16:45:29 PST  Terms of use