A path planning algorithm for three-dimensional collision avoidance based on potential field and B-spline boundary curve

Liu, Mingjie; Zhang, Hongxin; Yang, Jian; Zhang, Tiezhu; Zhang, Caihong; Bo, Lan

doi:10.1016/j.ast.2023.108763

Cited by 7 publications

(1 citation statement)

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“…Reference [132] employed the rotating component of the repulsive force to resolve local minima. The study [134] introduces an enhanced APF algorithm that incorporates the Fibonacci Sphere technique for route planning of UAVs. This integration allows for accurate obstacle avoidance and effective evasion of local minima in three-dimensional settings.…”

Section: B Uavs Collision Avoidance Algorithmsmentioning

confidence: 99%

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

Rezaee,

Hamid,

Hussin

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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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.

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Section: B Uavs Collision Avoidance Algorithmsmentioning

confidence: 99%