Multi-obstacle avoidance for UAVs in indoor applications

Salau, Babajide A.; Challoo, Rajab

doi:10.1109/iccicct.2015.7475386

Cited by 2 publications

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [33], the authors propose a navigation potential field approach to bypass multiple obstacles simultaneously. In the paper, the authors first assume all the obstacles can be represented as cylinders, and the UAV's altitude is fixed when doing obstacle avoidance maneuvers.…”

Section: Related Workmentioning

confidence: 99%

A 3D Vision Cone Based Method for Collision Free Navigation of a Quadcopter UAV among Moving Obstacles

Ming

Huang

2021

Drones

View full text Add to dashboard Cite

In the near future, it’s expected that unmanned aerial vehicles (UAVs) will become ubiquitous surrogates for human-crewed vehicles in the field of border patrol, package delivery, etc. Therefore, many three-dimensional (3D) navigation algorithms based on different techniques, e.g., model predictive control (MPC)-based, navigation potential field-based, sliding mode control-based, and reinforcement learning-based, have been extensively studied in recent years to help achieve collision-free navigation. The vast majority of the 3D navigation algorithms perform well when obstacles are sparsely spaced, but fail when facing crowd-spaced obstacles, which causes a potential threat to UAV operations. In this paper, a 3D vision cone-based reactive navigation algorithm is proposed to enable small quadcopter UAVs to seek a path through crowd-spaced 3D obstacles to the destination without collisions. The proposed algorithm is simulated in MATLAB with different 3D obstacles settings to demonstrate its feasibility and compared with the other two existing 3D navigation algorithms to exhibit its superiority. Furthermore, a modified version of the proposed algorithm is also introduced and compared with the initially proposed algorithm to lay the foundation for future work.

show abstract

Section: Related Workmentioning

confidence: 99%