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Inner structure of 3D vision cone. θ: the apex angle of the 3D vision cone. l: the length of the boundary of this 3D vision cone. h: the height of this 3D vision cone. r: the radius of the bottom circle of this 3D vision cone.
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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-ba...
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... 3D vision cone shares the same height, and each boundary of the 3D vision cone will check the intersect with the obstacles to find the vacant space, which will lead the UAV to conduct obstacle avoidance. Furthermore, the inner structure of each 3D vision cone can be described by using Figure 2. We denote θ as the apex angle, l refers to the boundary length, h is the height of the 3D vision cone and is determined by the capability of the depth camera, and r is the radius of the bottom circle. ...
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Citations
... The following are the critical parts that have dealt with avoiding collisions in drone navigation and guidance (Fig. 2. 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]. ...
In the contemporary landscape, the escalating deployment of drones across diverse industries has ushered in a consequential concern, including ensuring the security of drone operations. This concern extends to a spectrum of challenges, encompassing collisions with stationary and mobile obstacles and encounters with other drones. Moreover, the inherent limitations of drones, namely constraints on energy consumption, data storage capacity, and processing power, present formidable obstacles in developing collision avoidance algorithms. This review paper explores the challenges of ensuring safe drone operations, focusing on collision avoidance. We explore collision avoidance methods for UAVs from various perspectives, categorizing them into four main groups: obstacle detection and avoidance, collision avoidance algorithms, drone swarm, and path optimization. Additionally, our analysis delves into machine learning techniques, discusses metrics and simulation tools to validate collision avoidance systems, and delineates local and global algorithmic perspectives. Our evaluation reveals significant challenges in current drone collision prevention algorithms. Despite advancements, critical UAV network and communication challenges are often overlooked, prompting a reliance on simulation-based research due to cost and safety concerns. Challenges encompass precise detection of small and moving obstacles, minimizing path deviations at minimal cost, high machine learning and automation expenses, prohibitive costs of real testbeds, limited environmental comprehension, and security apprehensions. By addressing these key areas, future research can advance the field of drone collision avoidance and pave the way for safer and more efficient UAV operations.
... Chang, Tsai, Lu, and Lai (2020) proposed a new deep reinforcement learning method to analyse the performance and improve the control of drones flying during patrolling. Ming and Huang (2021) put forward a 3D corn-based navigational method to realise the anti-collision among the crowed-spaced 3D obstacles for future UAV operations. In summary, the literature addresses the diverse approaches and techniques employed in drone patrolling, with an emphasis on routing, navigation, and operations. ...
... Another approach investigated by Ming and Huang which reconstruct 3D vision cone utilizing the depth map information. But, this approach requires high computational power to achieve real-time operation [16]. ...
The need for autonomous Unmanned Aerial Vehicle (UAV) is rapidly increasing for various industrial applications nowadays. However, the realization of UAV autonomy requires a careful mix of AI and Computer Vision (CV) techniques to be implemented to address end-user challenges and satisfy field requirements. This study aims to develop an intelligent system for UAV to achieve autonomy with standard low cost hardware components. We have infused state of the art AI and CV technologies in our developed system architecture to improve UAV autonomous navigational capabilities. The proposed architecture has been implemented and tested on Telo UAV equipped only with an on-board monocular camera and Inertial Measurement Unit (IMU). We focused on resolving multiple practical technical problems that have aroused during this study due to the UAV hardware limitations. The experiments were carried out in an environment that included dynamic obstacles which were not considered in the map. The experimental results showed outstanding performance in terms of successful autonomous navigation for a wide range of light intensity. These results show that the proposed approach has high potential of to achieve autonomous UAV operation in field with low hardware and energy requirements.
... Besides avoiding static obstacles, each vehicle was considered as a dynamic obstacle, so the offline approach was not suitable in this scenario. In [28], moving obstacles were considered and a collision-free navigation was based on the 3D vision method. In [29], an optimal path was defined for each mobile robot, which takes into account the sum of all the other paths. ...
... x m (0|k) = x m (k) (28) c(x(j|k), ∆p re f (j|k)) ≤ 0, j = 1, . . . , N − 1 (29) where functions F and G are the terminal and stage cost, respectively, and are defined as follows: ...
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... In the past decade, the problem of UAV control has attracted more and more attention [1][2][3]. Since the multi-agent control problem has received continuous attention [4,5], multi-UAV control is also a hot area of research [6][7][8]. The multi-UAV circumnavigation control problem is a field of application for multi-UAV control, which refers to a group of UAVs surrounding the target in a prescribed formation to perform tasks such as monitoring [9], tracking and rounding up [10]. ...
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