Abstract
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.
Highlights
We demonstrate the performance of the proposed 3D vision cone-based navigation algorithm with MATLAB simulation
This simulation demonstrates that the proposed 3D vision cone-based navigation algorithm is capable of maintaining a safe distance between unmanned aerial vehicles (UAVs) and obstacle and drive the UAV to the final destination
A 3D vision cone-based reactive navigation algorithm and its modified version are proposed to enable a UAV to seek a path through the crowd-spaced 3D obstacles to the destination without collisions
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. With the development of unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs), unmanned vehicles progressively take the place of human-operated vehicles to conduct complex or dangerous missions such as specific area searching and surveillance [1,2,3,4,5,6], farming [7,8,9,10], package delivery [11,12,13,14], and disaster relief [15,16,17,18]. It could be more difficult in non-cooperative scenarios where
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