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

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.

Comprehensive Review of Drones Collision Avoidance Schemes: Challenges and Open Issues

“…role. Since UAVs operate in unfamiliar dynamic environments with several fixed or moving impediments, recognizing, and avoiding obstacles is critical [47]. There may be three distinct parts to the 3D navigation problem: recognizing obstacles, avoiding them, and finally arriving at the desired destination.…”

“…Based on the combined colliding cone and alert criteria, a collision detection and alerting theory is presented out in [53]. A 3D vision cone model in [47] proposes for the obstacle detection issue. A Sliding-Mode Controller (SMC) is used to avoid obstacles and reach the objective.…”

“…Other than that, 2D algorithms significantly decrease the UAV's trip efficiency. It will not look for a route around the closely spaced obstructions, leading to UAV crashes or tracking in a particular 3D environment [47].…”

Multiple-UAV Reinforcement Learning Algorithm Based on Improved PPO in Ray Framework

“…With the improvement of hardware computing power and the proposal of many efficient distributed training frameworks, deep reinforcement learning (DRL), which combines the excellent perception ability of deep learning and the decision-making ability of reinforcement learning, has walked into people's vision. DRL realizes end-to-end learning from the original input of data to decision making and has been widely used in a series of problems such as unmanned driving [5], resource scheduling [6,7], and navigation [8,9]. In the field of UAV decision-making, Syed et al [10] designed a novel control and testing platform based on Q-learning for a smart morphing wing system that was introduced to obtain optimal aerodynamic properties.…”

The Use of Terrestrial and Maritime Autonomous Vehicles in Nonintrusive Object Inspection

“…In the case of traditional nonintrusive object inspection, in some instances, it is impossible to use fixed control points. Thus, mobile and self-moving vehicles might help [ 24 , 25 , 26 , 27 , 28 ]. As an example, such inspection could be provided for underwater object surveillance during border control, when it is not easy to give a traditional type of nonintrusive inspection with the fixed control point over large vehicles, such as cruise liners, or at a vast area, such as port or a gulf.…”