Статті в журналах: "UAV collision avoidance"

1

Shan, Lin, Huan-Bang Li, Ryu Miura, Takashi Matsuda, and Takeshi Matsumura. "A Novel Collision Avoidance Strategy with D2D Communications for UAV Systems." Drones 7, no. 5 (April 22, 2023): 283. http://dx.doi.org/10.3390/drones7050283.

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In recent years, safety operation issues related to the autonomous flight of unmanned aerial vehicles (UAVs) have become popular research and development topics worldwide. Among all UAV applications, multiple UAV-related applications are emerging due to the integration of UAVs into 6G networks, which is an important topic for next-generation wireless communication systems. For multiple UAV applications, flight safety among UAVs is the most significant issue. Therefore, collision avoidance for UAVs has become an emerging topic in UAV-related research. In the past, although many UAV collision avoidance methods have been proposed, there is still a probability of other problems, such as no possible avoidance route and unmanaged UAVs that are without centralized control, which both result in an unpredictable risk of collisions. In this study, we investigate the current existing methods and propose novel collision avoidance methods based on the elastic collision principle. To verify the performance of the proposed methods, we also conduct simulations in this paper to demonstrate their effectiveness. From the simulation results, it can be seen that the proposed methods can effectively perform collision avoidance for multiple UAVs. Specifically, using the proposed methods, all UAVs can reach their destination points within reasonable time resources without any collision, validating the effectiveness of the proposed methods.

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2

Chen, C. W., P. H. Hsieh, and W. H. Lai. "APPLICATION OF DECISION TREE ON COLLISION AVOIDANCE SYSTEM DESIGN AND VERIFICATION FOR QUADCOPTER." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W6 (August 23, 2017): 71–75. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w6-71-2017.

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The purpose of the research is to build a collision avoidance system with decision tree algorithm used for quadcopters. While the ultrasonic range finder judges the distance is in collision avoidance interval, the access will be replaced from operator to the system to control the altitude of the UAV. According to the former experiences on operating quadcopters, we can obtain the appropriate pitch angle. The UAS implement the following three motions to avoid collisions. Case1: initial slow avoidance stage, Case2: slow avoidance stage and Case3: Rapid avoidance stage. Then the training data of collision avoidance test will be transmitted to the ground station via wireless transmission module to further analysis. The entire decision tree algorithm of collision avoidance system, transmission data, and ground station have been verified in some flight tests. In the flight test, the quadcopter can implement avoidance motion in real-time and move away from obstacles steadily. In the avoidance area, the authority of the collision avoidance system is higher than the operator and implements the avoidance process. The quadcopter can successfully fly away from the obstacles in 1.92 meter per second and the minimum distance between the quadcopter and the obstacle is 1.05 meters.

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3

Tan, Chee Yong, Sunan Huang, Kok Kiong Tan, Rodney Swee Huat Teo, Wen Qi Liu, and Feng Lin. "Collision Avoidance Design on Unmanned Aerial Vehicle in 3D Space." Unmanned Systems 06, no. 04 (October 2018): 277–95. http://dx.doi.org/10.1142/s2301385018500115.

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Collision avoidance is one of the core problems in designing a multi-unmanned aerial vehicle (UAV) system. It is to ensure each UAV can reach its target without colliding with any moving or static obstacles. With increasing usage of UAVs in many application areas, research on collision avoidance algorithm has become a hot topic. Several approaches on collision avoidance have been reported. In this paper, we design a three-dimensional collision avoidance on multi-UAV systems based on the original three-dimensional velocity obstacle method.

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4

He, Renke, Ruixuan Wei, and Qirui Zhang. "UAV autonomous collision avoidance approach." Automatika 58, no. 2 (April 3, 2017): 195–204. http://dx.doi.org/10.1080/00051144.2017.1388646.

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5

Et.al, Jung Kyu Park. "UAV Collision Detection Algorithm based on Formulafor Fast Calculation." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 6 (April 10, 2021): 452–56. http://dx.doi.org/10.17762/turcomat.v12i6.1869.

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Collision Avoidance System (CAS) is known as a pre-collision system or a forward collision warning system, and research has first begun as a vehicle safety system. In this paper, we propose an algorithm for collision detection of UAV. The proposed algorithm uses a mathematical method and detects the collision of the UAV by modeling it in a two-dimensional plane. Using the mathematical modeling method, it is possible to determine the collision location of the UAV in advance. Experiments were conducted to measure the performance and accuracy of the proposed algorithm. In the experiment, we proceeded assuming three environments and were able to detect an accurate collision when the UAV moved. By applying the algorithm proposed in the paper to CAS, many collision accidents can be prevented. The proposed algorithm detects collisions through mathematical calculations. In addition, the movement time of the UAV was modeled in a 2D environment to shorten the calculation time.

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6

H. Sawalmeh, Ahmad, and Noor Shamsiah Othman. "An Overview of Collision Avoidance Approaches and Network Architecture of Unmanned Aerial Vehicles (UAVs)." International Journal of Engineering & Technology 7, no. 4.35 (November 30, 2018): 924. http://dx.doi.org/10.14419/ijet.v7i4.35.27395.

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As an autonomous vehicle, Unmanned Aerial Vehicles (UAVs) are subjected to several challenges. One of the challenges is for UAV to be able to avoid collision. Many collision avoidance methods have been proposed to address this issue. Furthermore, in a multi-UAV system, it is also important to address communication issue among UAVs for cooperation and collaboration. This issue can be addressed by setting up an ad-hoc network among UAVs. There is also a need to consider the challenges in the deployment of UAVs, as well as, in the development of collision avoidance methods and the establishment of communication for cooperation and collaboration in a multi-UAV system. In this paper, we present general challenges in the deployment of UAV and comparison of UAV communication services based on its operating frequency. We also present major collision avoidance approaches, and specifically discuss collision avoidance approaches that are suitable for indoor applications. We also present the Flying Ad-hoc Networks (FANET) network architecture, communication and routing protocols for each Open System Interconnection (OSI) communication layers.

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7

Lam, T. Mung, Max Mulder, and M. M. (René) van Paassen. "Haptic Interface For UAV Collision Avoidance." International Journal of Aviation Psychology 17, no. 2 (April 17, 2007): 167–95. http://dx.doi.org/10.1080/10508410701328649.

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8

Berdonosov, V. D., A. A. Zivotova, Zaw Htet Naing, and D. O. Zhuravlev. "Speed Approach for UAV Collision Avoidance." Journal of Physics: Conference Series 1015 (May 2018): 052002. http://dx.doi.org/10.1088/1742-6596/1015/5/052002.

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9

Peng, Zhihong, and Zhimin Chen. "Ground Target Tracking and Collision Avoidance for UAV Based Guidance Vector Field." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 2 (March 20, 2015): 277–83. http://dx.doi.org/10.20965/jaciii.2015.p0277.

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This paper focuses on ground-moving target tracking of an unmanned aerial vehicle (UAV) in the presence of static obstacles and moving threat sources. Due to a UAV is restricted by airspace restrictions and measurement limitations during flight, we derive a dynamic path planning strategy by generating guidance vector filed combined Lyapunov vector field with collision avoidance potential function to track target in standoff distance loitering pattern, and resolved collision avoidance, simultaneously. This method relies only on the current information of the UAV and target, and generates a single-step route plan in realtime. Its performance is simple, efficient, and fast and have low computational complexity. The results of numerical simulation verify the effectiveness of the tracking and collision avoidance process of the UAV.

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10

HARUN, MOHAMAD HANIFF, SHAHRUM SHAH ABDULLAH, MOHD SHAHRIEEL MOHD ARAS, MOHD BAZLI BAHAR, and FARIZ ALI @IBRAHIM. "3D COLLISION AVOIDANCE SYSTEM FOR UNMANNED AERIAL VEHICLE (UAV) WITH DECENTRALIZED APPROACH." IIUM Engineering Journal 24, no. 2 (July 4, 2023): 373–90. http://dx.doi.org/10.31436/iiumej.v24i2.2803.

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Unmanned aerial vehicles UAVs have been developed and refined for decades. Using an integrated software system, autonomous unmanned aerial vehicles (UAVs) perform missions automatically and return to a pre-programmed point. Malaysia has a lot of unoccupied airspace, yet autonomous UAV applications and research are still rare. In critical conditions, autonomous UAVs must deal with a variety of environmental and flight issues. This project involves a decentralized 3D collision avoidance system for an autonomous UAV. Ultrasonic, infrared, and laser rangefinders were chosen for the 3D collision avoidance system. The UAV's obstacle recognition and collision avoidance performance are also tested in four experiments. In various flight conditions, the 3D collision avoidance system can identify several material types and opacities by integrating selected rangefinders. Finally, the 3D collision avoidance system quickly reacts to obstacles in the X, Y, and Z axes. ABSTRAK: Kenderaan udara tanpa pemandu (UAV) telah dibangunkan dan diperhalusi selama beberapa dekad. Menggunakan sistem perisian bersepadu, kenderaan udara tanpa pemandu (UAV) autonomi melaksanakan misi secara automatik dan kembali ke titik pra-diprogramkan. Malaysia mempunyai banyak ruang udara yang tidak berpenghuni, namun aplikasi dan penyelidikan UAV autonomi masih jarang berlaku. Dalam keadaan kritikal, UAV autonomi mesti menangani pelbagai isu alam sekitar dan penerbangan. Projek ini melibatkan sistem pengelakan perlanggaran 3D terpencar untuk UAV autonomi. Pencari jarak ultrasonik, inframerah dan laser telah dipilih untuk sistem pengelakan perlanggaran 3D. Prestasi pengecaman halangan dan pengelakan perlanggaran UAV juga diuji dalam empat eksperimen. Dalam pelbagai keadaan penerbangan, sistem pengelakan perlanggaran 3D boleh mengenal pasti beberapa jenis bahan dan kelegapan dengan menyepadukan pencari jarak terpilih. Akhir sekali, sistem pengelakan perlanggaran 3D bertindak balas dengan cepat terhadap halangan dalam paksi X, Y dan Z.

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11

Huang, Yang, Jun Tang, and Songyang Lao. "UAV Group Formation Collision Avoidance Method Based on Second-Order Consensus Algorithm and Improved Artificial Potential Field." Symmetry 11, no. 9 (September 13, 2019): 1162. http://dx.doi.org/10.3390/sym11091162.

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The problem of collision avoidance of an unmanned aerial vehicle (UAV) group is studied in this paper. A collision avoidance method of UAV group formation based on second-order consensus algorithm and improved artificial potential field is proposed. Based on the method, the UAV group can form a predetermined formation from any initial state and fly to the target position in normal flight, and can avoid collision according to the improved smooth artificial potential field method when encountering an obstacle. The UAV group adopts the “leader–follower” strategy, that is, the leader UAV is the controller and flies independently according to the mission requirements, while the follower UAV follows the leader UAV based on the second-order consensus algorithm and formations gradually form during the flight. Based on the second-order consensus algorithm, the UAV group can achieve formation maintenance easily and the Laplacian matrix used in the algorithm is symmetric for an undirected graph. In the process of obstacle avoidance, the improved artificial potential field method can solve the jitter problem that the traditional artificial potential field method causes for the UAV and avoids violent jitter. Finally, simulation experiments of two scenarios were designed to verify the collision avoidance effect and formation retention effect of static obstacles and dynamic obstacles while the two UAV groups fly in opposite symmetry in the dynamic obstacle scenario. The experimental results demonstrate the effectiveness of the proposed method.

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12

Hashim, Fakroul Ridzuan. "Decentralized 3D Collision Avoidance System for Unmanned Aerial Vehicle (UAV)." Journal of Advanced Research in Dynamical and Control Systems 12, no. 7 (July 20, 2020): 446–60. http://dx.doi.org/10.5373/jardcs/v12i7/20202025.

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13

Lee, Hae-In, Hyo-Sang Shin, and Antonios Tsourdos. "UAV Collision Avoidance Considering No-Fly-Zones." IFAC-PapersOnLine 53, no. 2 (2020): 14748–53. http://dx.doi.org/10.1016/j.ifacol.2020.12.1893.

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14

Singh, Simran, Hee Won Lee, Tuyen X. Tran, Yu Zhou, Mihail L. Sichitiu, Ismail Guvenc, and Arupjyoti Bhuyan. "FPV Video Adaptation for UAV Collision Avoidance." IEEE Open Journal of the Communications Society 2 (2021): 2095–110. http://dx.doi.org/10.1109/ojcoms.2021.3106274.

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15

Han, Su-Cheol, and Hyo-choong Bang. "Near-OptimaI Collision Avoidance Maneuvers for UAV." International Journal of Aeronautical and Space Sciences 5, no. 2 (December 31, 2004): 43–53. http://dx.doi.org/10.5139/ijass.2004.5.2.043.

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16

Lee, Hae-In, Hyo-Sang Shin, and Antonios Tsourdos. "A Probabilistic–Geometric Approach for UAV Detection and Avoidance Systems." Sensors 22, no. 23 (November 27, 2022): 9230. http://dx.doi.org/10.3390/s22239230.

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This paper proposes a collision avoidance algorithm for the detection and avoidance capabilities of Unmanned Aerial Vehicles (UAVs). The proposed algorithm aims to ensure minimum separation between UAVs and geofencing with multiple no-fly zones, considering the sensor uncertainties. The main idea is to compute the collision probability and to initiate collision avoidance manoeuvres determined by the differential geometry concept. The proposed algorithm is validated by both theoretical and numerical analysis. The results indicate that the proposed algorithm ensures minimum separation, efficiency, and scalability compared with other benchmark algorithms.

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17

Moses, Allistair, Matthew J. Rutherford, Michail Kontitsis, and Kimon P. Valavanis. "UAV-borne X-band radar for collision avoidance." Robotica 32, no. 1 (July 19, 2013): 97–114. http://dx.doi.org/10.1017/s0263574713000659.

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SUMMARYThe increased use of unmanned aerial vehicles (UAVs) is coincidentally accompanied by a notable lack of sensors suitable for enabling further improvement in levels of autonomy and, consequently, integration into the National Airspace System (NAS). The majority of available sensors suitable for UAV integration into the NAS are based on infrared detectors, focal plane arrays, optical and ultrasonic rangefinders, etc. These sensors are generally not able to detect or identify other UAV-sized targets and, when detection is possible, considerable computational power is typically required for successful identification. Furthermore, the performance of visual-range optical sensor systems may suffer when operating under conditions that are typically encountered during search and rescue, surveillance, combat, and most other common UAV applications. However, the addition of a miniature RADAR sensor can, in consort with other sensors, provide comprehensive target detection and identification capabilities for UAVs. This trend is observed in manned aviation where RADAR sensors are the primary on-board detection and identification sensors. In this paper, a miniature, lightweight X-band RADAR sensor for use on a miniature (710-mm rotor diameter) rotorcraft is described. We present an analysis of the performance of the RADAR sensor in a realistic scenario with two UAVs. Additionally, an analysis of UAV navigation and collision avoidance behaviors is performed to determine the effect of integrating RADAR sensors into UAVs. Further study is also performed to demonstrate the scalability of the RADAR for use with larger UAV classes.

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18

Fabra, Francisco, Willian Zamora, Julio Sangüesa, Carlos T. Calafate, Juan-Carlos Cano, and Pietro Manzoni. "A Distributed Approach for Collision Avoidance between Multirotor UAVs Following Planned Missions." Sensors 19, no. 10 (May 26, 2019): 2404. http://dx.doi.org/10.3390/s19102404.

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As the number of potential applications for Unmanned Aerial Vehicles (UAVs) keeps rising steadily, the chances that these devices get close to each other during their flights also increases, causing concerns regarding potential collisions. This paper proposed the Mission Based Collision Avoidance Protocol (MBCAP), a novel UAV collision avoidance protocol applicable to all types of multicopters flying autonomously. It relies on wireless communications in order to detect nearby UAVs, and to negotiate the procedure to avoid any potential collision. Experimental and simulation results demonstrated the validity and effectiveness of the proposed solution, which typically introduces a small overhead in the range of 15 to 42 s for each risky situation successfully handled.

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19

Chao, Zhou, Shao-Lei Zhou, Lei Ming, and Wen-Guang Zhang. "UAV Formation Flight Based on Nonlinear Model Predictive Control." Mathematical Problems in Engineering 2012 (2012): 1–15. http://dx.doi.org/10.1155/2012/261367.

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We designed a distributed collision-free formation flight control law in the framework of nonlinear model predictive control. Formation configuration is determined in the virtual reference point coordinate system. Obstacle avoidance is guaranteed by cost penalty, and intervehicle collision avoidance is guaranteed by cost penalty combined with a new priority strategy.

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20

Cheng, Qiao, Xiangke Wang, Jian Yang, and Lincheng Shen. "Automated Enemy Avoidance of Unmanned Aerial Vehicles Based on Reinforcement Learning." Applied Sciences 9, no. 4 (February 15, 2019): 669. http://dx.doi.org/10.3390/app9040669.

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This paper focuses on one of the collision avoidance scenarios for unmanned aerial vehicles (UAVs), where the UAV needs to avoid collision with the enemy UAV during its flying path to the goal point. Such a type of problem is defined as the enemy avoidance problem in this paper. To deal with this problem, a learning based framework is proposed. Under this framework, the enemy avoidance problem is formulated as a Markov Decision Process (MDP), and the maneuver policies for the UAV are learned based on a temporal-difference reinforcement learning method called Sarsa. To handle the enemy avoidance problem in continuous state space, the Cerebellar Model Arithmetic Computer (CMAC) function approximation technique is embodied in the proposed framework. Furthermore, a hardware-in-the-loop (HITL) simulation environment is established. Simulation results show that the UAV agent can learn a satisfying policy under the proposed framework. Comparing with the random policy and the fixed-rule policy, the learned policy can achieve a far higher possibility in reaching the goal point without colliding with the enemy UAV.

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21

Eze, C. N., and N. Nnaji. "Modified Tentacle Algorithm for Collision Avoidance for UAV." International Journal of Research and Innovation in Applied Science 07, no. 02 (2022): 40–42. http://dx.doi.org/10.51584/ijrias.2022.7201.

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A collision avoidance methodology in UAV Operation using the modified tentacles algorithm is proposed in this work. Tremendous and remarkable achievements have been recorded in the area of collision avoidance in UAV operations. Applying the modified tentacles algorithm, this research used two point research objectives the principal aim was to maintain accurate collision avoidance. With a reliable simulation result, an algorithm based on the modified tentacles technique was proposed. This was also modeled. In executing the proposed algorithm, a 3D environmental data and information were converted to 2D information for ease of mathematical computation. Appropriate occupancy grid maps were generated to show a practical result of the 3D conversion to 2D. Thus, instead of dealing with an x, y, z or three dimensional environments, an x, y or two dimensional environment results and mathematical differentiation was applied. A development of an integrated model comprising of the conventional, Modified Tentacles Algorithm. This all inclusive integrated model was simulated in order to validate and justify the work. It is a justification and validation of this research on the use of Modified Tentacles Algorithm in achieving energy efficiency in UAV operations. It has been established that energy consumption in UAV operations is influenced by its mode of operation at a given time.

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22

Penney, R. W. "Collision avoidance within flight dynamics constraints for UAV applications." Aeronautical Journal 109, no. 1094 (April 2005): 193–99. http://dx.doi.org/10.1017/s0001924000000695.

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Abstract Avoiding collisions with other aircraft is an absolutely fundamental capability for semi-autonomous UAVs. However, an aircraft avoiding moving obstacles requires an evasive tactic that is simultaneously very quick to compute, compatible with the platform’s flight dynamics, and deals with the subtle spatio-temporal features of the threat. We will give an overview of a novel prototype method of rapidly generating smooth flight-paths constrained to avoid moving obstacles, using an efficient trajectory-optimisation technique. Obstacles are described in terms of simple geometrical shapes, such as ellipsoids, whose centres and shapes can vary with time. The technique generates a spatio-temporal trajectory which offers a high likelihood of avoiding the volume in space-time excluded by the predicted motion of each of the known obstacles. Such a flight-path could then be passed to the aircraft’s flight-control systems to negotiate the threat posed by the obstacles. Results from a demonstration implementation of the collision-avoidance technique will be discussed, including non-trivial scenarios handled well within 100ms on a 300MHz processor.

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23

Huang, Sunan, Rodney Swee Huat Teo, and Wenqi Liu. "Distributed Cooperative Avoidance Control for Multi-Unmanned Aerial Vehicles." Actuators 8, no. 1 (December 21, 2018): 1. http://dx.doi.org/10.3390/act8010001.

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It is well-known that collision-free control is a crucial issue in the path planning of unmanned aerial vehicles (UAVs). In this paper, we explore the collision avoidance scheme in a multi-UAV system. The research is based on the concept of multi-UAV cooperation combined with information fusion. Utilizing the fused information, the velocity obstacle method is adopted to design a decentralized collision avoidance algorithm. Four case studies are presented for the demonstration of the effectiveness of the proposed method. The first two case studies are to verify if UAVs can avoid a static circular or polygonal shape obstacle. The third case is to verify if a UAV can handle a temporary communication failure. The fourth case is to verify if UAVs can avoid other moving UAVs and static obstacles. Finally, hardware-in-the-loop test is given to further illustrate the effectiveness of the proposed method.

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24

Xu, Zhao, Jinwen Hu, Yunhong Ma, Man Wang, and Chunhui Zhao. "A Study on Path Planning Algorithms of UAV Collision Avoidance." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 37, no. 1 (February 2019): 100–106. http://dx.doi.org/10.1051/jnwpu/20193710100.

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The unmanned aerial vehicle (UAV) has been a research hotspot worldwide. The UAV system is developing to be more and more intelligent and autonomous. UAV path planning is an important part of UAV autonomous control and the important guarantee of UAV's safety. For the purpose of improving the collision avoidance and path planning algorithms, the artificial potential field, fuzzy logic algorithm and ant colony algorithm are simulated respectively in the static obstacle and dynamic obstacle environments, and compared based on the minimum avoidance distance and range ratio. Meanwhile, an improved algorithm of artificial potential field is proposed, and the improvement helps the UAV escape the local minimum by introducing the vertical guidance repulsion. The simulation results are rigorous and reliable, which lay a foundation for the further fusion of multiple algorithms and improving the path planning algorithms.

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25

Sánchez, Paloma, Rafael Casado, and Aurelio Bermúdez. "Real-Time Collision-Free Navigation of Multiple UAVs Based on Bounding Boxes." Electronics 9, no. 10 (October 3, 2020): 1632. http://dx.doi.org/10.3390/electronics9101632.

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Predictably, future urban airspaces will be crowded with autonomous unmanned aerial vehicles (UAVs) offering different services to the population. One of the main challenges in this new scenario is the design of collision-free navigation algorithms to avoid conflicts between flying UAVs. The most appropriate collision avoidance strategies for this scenario are non-centralized ones that are dynamically executed (in real time). Existing collision avoidance methods usually entail a high computational cost. In this work, we present Bounding Box Collision Avoidance (BBCA) algorithm, a simplified velocity obstacle-based technique that achieves a balance between efficiency and cost. The performance of the proposal is analyzed in detail in different airspace configurations. Simulation results show that the method is able to avoid all the conflicts in two UAV scenarios and most of them in multi-UAV ones. At the same time, we have found that the penalty of using the BBCA collision avoidance technique on the flying time and the distance covered by the UAVs involved in the conflict is reasonably acceptable. Therefore, we consider that BBCA may be an excellent candidate for the design of collision-free navigation algorithms for UAVs.

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26

Raimundo, A., D. Peres, N. Santos, P. Sebastião, and N. Souto. "USING DISTANCE SENSORS TO PERFORM COLLISION AVOIDANCE MANEUVRES ON UAV APPLICATIONS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W6 (August 24, 2017): 303–9. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w6-303-2017.

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The Unmanned Aerial Vehicles (UAV) and its applications are growing for both civilian and military purposes. The operability of an UAV proved that some tasks and operations can be done easily and at a good cost-efficiency ratio. Nowadays, an UAV can perform autonomous missions. It is very useful to certain UAV applications, such as meteorology, vigilance systems, agriculture, environment mapping and search and rescue operations. One of the biggest problems that an UAV faces is the possibility of collision with other objects in the flight area. To avoid this, an algorithm was developed and implemented in order to prevent UAV collision with other objects. “Sense and Avoid” algorithm was developed as a system for UAVs to avoid objects in collision course. This algorithm uses a Light Detection and Ranging (LiDAR), to detect objects facing the UAV in mid-flights. This light sensor is connected to an on-board hardware, Pixhawk’s flight controller, which interfaces its communications with another hardware: Raspberry Pi. Communications between Ground Control Station and UAV are made via Wi-Fi or cellular third or fourth generation (3G/4G). Some tests were made in order to evaluate the “Sense and Avoid” algorithm’s overall performance. These tests were done in two different environments: A 3D simulated environment and a real outdoor environment. Both modes worked successfully on a simulated 3D environment, and “Brake” mode on a real outdoor, proving its concepts.

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27

Zhou, Xiaobin, Xiang Yu, and Xiaoyan Peng. "UAV Collision Avoidance Based on Varying Cells Strategy." IEEE Transactions on Aerospace and Electronic Systems 55, no. 4 (August 2019): 1743–55. http://dx.doi.org/10.1109/taes.2018.2875556.

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28

Lin, Yucong, and Srikanth Saripalli. "Sampling-Based Path Planning for UAV Collision Avoidance." IEEE Transactions on Intelligent Transportation Systems 18, no. 11 (November 2017): 3179–92. http://dx.doi.org/10.1109/tits.2017.2673778.

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29

Wang, Xiaohua, Vivek Yadav, and S. N. Balakrishnan. "Cooperative UAV Formation Flying With Obstacle/Collision Avoidance." IEEE Transactions on Control Systems Technology 15, no. 4 (July 2007): 672–79. http://dx.doi.org/10.1109/tcst.2007.899191.

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30

Pierpaoli, Pietro, and Amir Rahmani. "UAV collision avoidance exploitation for noncooperative trajectory modification." Aerospace Science and Technology 73 (February 2018): 173–83. http://dx.doi.org/10.1016/j.ast.2017.12.008.

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31

Radzi, N. F. M., M. Mubin, N. A. Rahim, and N. Mokhtar. "UAV Collision Detection Algorithm Design Based on Circle Overlapping Test." Applied Mechanics and Materials 229-231 (November 2012): 1487–91. http://dx.doi.org/10.4028/www.scientific.net/amm.229-231.1487.

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Each UAV require a robust collision detection in its operating system to ensure it can aware the potential of collision in near future. Collision detection algorithm is one of the important parts that need to be concerned in the CAS. This paper proposes a design of collision avoidance algorithm for unmanned aerial vehicle based on circle overlapping test. Assuming two moving UAV are flying in the same altitude and in straight path within a two dimensional plane as conflict problem. Both UAV are cooperative aircrafts which communication of position, heading angle, waypoint, and velocity are allowed. The proposed algorithm will determine the collision potential between the surrounding intruders according to the conflict information.

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Alturbeh, Hamid, and James F. Whidborne. "Visual Flight Rules-Based Collision Avoidance Systems for UAV Flying in Civil Aerospace." Robotics 9, no. 1 (February 25, 2020): 9. http://dx.doi.org/10.3390/robotics9010009.

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Анотація:

The operation of Unmanned Aerial Vehicles (UAVs) in civil airspace is restricted by the aviation authorities, which require full compliance with regulations that apply for manned aircraft. This paper proposes control algorithms for a collision avoidance system that can be used as an advisory system or a guidance system for UAVs that are flying in civil airspace under visual flight rules. A decision-making system for collision avoidance is developed based on the rules of the air. The proposed architecture of the decision-making system is engineered to be implementable in both manned aircraft and UAVs to perform different tasks ranging from collision detection to a safe avoidance manoeuvre initiation. Avoidance manoeuvres that are compliant with the rules of the air are proposed based on pilot suggestions for a subset of possible collision scenarios. The proposed avoidance manoeuvres are parameterized using a geometric approach. An optimal collision avoidance algorithm is developed for real-time local trajectory planning. Essentially, a finite-horizon optimal control problem is periodically solved in real-time hence updating the aircraft trajectory to avoid obstacles and track a predefined trajectory. The optimal control problem is formulated in output space, and parameterized by using B-splines. Then the optimal designed outputs are mapped into control inputs of the system by using the inverse dynamics of a fixed wing aircraft.

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Kelechi Kingsley Igbokwe, James Eke, and Patrick Uche Okafor. "Simulation of motions of a 6DoF unmanned aerial vehicle from the mathematical model in free space." Global Journal of Engineering and Technology Advances 15, no. 2 (May 30, 2023): 011–18. http://dx.doi.org/10.30574/gjeta.2023.15.2.0084.

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This work prepares a smooth playing ground to modeling and simulation of a Collision Avoidance for Cooperative UAVs with Rolling Optimization Algorithm (ROA) Based on Predictive State Space (PSS) technique using Matlab, Simulink and Aerospace toolboxes of MathsWork. The methodological approach adopted is Computer Aided Software Engineering and to have handle collisions and maneuvers in a mathematical way, normal simulation of the trajectory of both the UAV and obstacles is defined by geometric approach. The goal of the work is to develop a mathematical model of a six degree of freedom (6DoF) of unmanned aerial vehicle (UAV) in free space for further modeling and simulation of a collision avoidance for cooperative UAVs with the required specifications. The motion (modeling of mathematical parameters like velocity, position, body rotational rate and Euler angles) of the 6DoF aircraft was determined by coordinate systems which allow the aircraft’s position and orientation in spaceto be kept tracked. The discrete form of the developed model for simulation was achieved using appropriate transfer functions. The development of the mathematical model will enable the development of an artificial NN model predictive controller (MPC) that can handle the nonlinearities associated with the UAV based on PSS technique.

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Pedro, Dário, João P. Matos-Carvalho, Fábio Azevedo, Ricardo Sacoto-Martins, Luís Bernardo, Luís Campos, José M. Fonseca, and André Mora. "FFAU—Framework for Fully Autonomous UAVs." Remote Sensing 12, no. 21 (October 28, 2020): 3533. http://dx.doi.org/10.3390/rs12213533.

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Анотація:

Unmanned Aerial Vehicles (UAVs), although hardly a new technology, have recently gained a prominent role in many industries being widely used not only among enthusiastic consumers, but also in high demanding professional situations, and will have a massive societal impact over the coming years. However, the operation of UAVs is fraught with serious safety risks, such as collisions with dynamic obstacles (birds, other UAVs, or randomly thrown objects). These collision scenarios are complex to analyze in real-time, sometimes being computationally impossible to solve with existing State of the Art (SoA) algorithms, making the use of UAVs an operational hazard and therefore significantly reducing their commercial applicability in urban environments. In this work, a conceptual framework for both stand-alone and swarm (networked) UAVs is introduced, with a focus on the architectural requirements of the collision avoidance subsystem to achieve acceptable levels of safety and reliability. The SoA principles for collision avoidance against stationary objects are reviewed and a novel approach is described, using deep learning techniques to solve the computational intensive problem of real-time collision avoidance with dynamic objects. The proposed framework includes a web-interface allowing the full control of UAVs as remote clients with a supervisor cloud-based platform. The feasibility of the proposed approach was demonstrated through experimental tests using a UAV, developed from scratch using the proposed framework. Test flight results are presented for an autonomous UAV monitored from multiple countries across the world.

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35

Huang, Tang, and Lao. "Cooperative Multi-UAV Collision Avoidance Based on a Complex Network." Applied Sciences 9, no. 19 (September 20, 2019): 3943. http://dx.doi.org/10.3390/app9193943.

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The conflict resolution problem in cooperative unmanned aerial vehicle (UAV) clusters sharing a three-dimensional airspace with increasing air traffic density is very important. This paper innovatively solves this problem by employing the complex network (CN) algorithm. The proposed approach allows a UAV to perform only one maneuver—that of the flight level change. The novel UAV conflict resolution is divided into two steps, corresponding to the key node selection (KS) algorithm based on the node contraction method and the sense selection (SS) algorithm based on an objective function. The efficiency of the cooperative multi-UAV collision avoidance (CA) system improved a lot due to the simple two-step collision avoidance logic. The paper compares the difference between random selection and the use of the node contraction method to select key nodes. Experiments showed that using the node contraction method to select key nodes can make the collision avoidance effect of UAVs better. The CA maneuver was validated with quantitative simulation experiments, demonstrating advantages such as minimal cost when considering the robustness of the global traffic situation, as well as significant real-time and high efficiency. The CN algorithm requires a relatively small computing time that renders the approach highly suitable for solving real-life operational situations.

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36

Maw, Aye Aye, Maxim Tyan, Tuan Anh Nguyen, and Jae-Woo Lee. "iADA*-RL: Anytime Graph-Based Path Planning with Deep Reinforcement Learning for an Autonomous UAV." Applied Sciences 11, no. 9 (April 27, 2021): 3948. http://dx.doi.org/10.3390/app11093948.

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Path planning algorithms are of paramount importance in guidance and collision systems to provide trustworthiness and safety for operations of autonomous unmanned aerial vehicles (UAV). Previous works showed different approaches mostly focusing on shortest path discovery without a sufficient consideration on local planning and collision avoidance. In this paper, we propose a hybrid path planning algorithm that uses an anytime graph-based path planning algorithm for global planning and deep reinforcement learning for local planning which applied for a real-time mission planning system of an autonomous UAV. In particular, we aim to achieve a highly autonomous UAV mission planning system that is adaptive to real-world environments consisting of both static and moving obstacles for collision avoidance capabilities. To achieve adaptive behavior for real-world problems, a simulator is required that can imitate real environments for learning. For this reason, the simulator must be sufficiently flexible to allow the UAV to learn about the environment and to adapt to real-world conditions. In our scheme, the UAV first learns about the environment via a simulator, and only then is it applied to the real-world. The proposed system is divided into two main parts: optimal flight path generation and collision avoidance. A hybrid path planning approach is developed by combining a graph-based path planning algorithm with a learning-based algorithm for local planning to allow the UAV to avoid a collision in real time. The global path planning problem is solved in the first stage using a novel anytime incremental search algorithm called improved Anytime Dynamic A* (iADA*). A reinforcement learning method is used to carry out local planning between waypoints, to avoid any obstacles within the environment. The developed hybrid path planning system was investigated and validated in an AirSim environment. A number of different simulations and experiments were performed using AirSim platform in order to demonstrate the effectiveness of the proposed system for an autonomous UAV. This study helps expand the existing research area in designing efficient and safe path planning algorithms for UAVs.

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37

Tong, Liang, Xusheng Gan, Yarong Wu, Nan Yang, and Maolong Lv. "An ADS-B Information-Based Collision Avoidance Methodology to UAV." Actuators 12, no. 4 (April 6, 2023): 165. http://dx.doi.org/10.3390/act12040165.

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A collision avoidance method that is specifically tailored for UAVs (unmanned aerial vehicles) operating in converging airspace is proposed. The method is based on ADS-B messages and it aims to detect and resolve conflicts between UAVs. The proposed method involves two main steps. First, a UAV conflict-sensing scheme is developed, which utilizes ADS-B information flow path and analyzes the message format information. Second, an unscented Kalman filter is used to predict UAV trajectories based on the acquired ADS-B information. The predicted information is then used to determine potential conflict scenarios, and different deconfliction strategies are selected accordingly. These strategies include speed regulation, direction regulation, and compound deconfliction, and are mathematically validated using the velocity obstacle method. The feasibility and effectiveness of the proposed method are evaluated through simulation, and it is concluded that the method can significantly improve the conflict resolution capability of UAV flights. This research provides a valuable contribution to the field of UAV collision avoidance, and can serve as a theoretical foundation for further advancements in this area.

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38

Ahmed, Shibbir, Baijing Qiu, Fiaz Ahmad, Chun-Wei Kong, and Huang Xin. "A State-of-the-Art Analysis of Obstacle Avoidance Methods from the Perspective of an Agricultural Sprayer UAV’s Operation Scenario." Agronomy 11, no. 6 (May 26, 2021): 1069. http://dx.doi.org/10.3390/agronomy11061069.

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Анотація:

Over the last decade, Unmanned Aerial Vehicles (UAVs), also known as drones, have been broadly utilized in various agricultural fields, such as crop management, crop monitoring, seed sowing, and pesticide spraying. Nonetheless, autonomy is still a crucial limitation faced by the Internet of Things (IoT) UAV systems, especially when used as sprayer UAVs, where data needs to be captured and preprocessed for robust real-time obstacle detection and collision avoidance. Moreover, because of the objective and operational difference between general UAVs and sprayer UAVs, not every obstacle detection and collision avoidance method will be sufficient for sprayer UAVs. In this regard, this article seeks to review the most relevant developments on all correlated branches of the obstacle avoidance scenarios for agricultural sprayer UAVs, including a UAV sprayer’s structural details. Furthermore, the most relevant open challenges for current UAV sprayer solutions are enumerated, thus paving the way for future researchers to define a roadmap for devising new-generation, affordable autonomous sprayer UAV solutions. Agricultural UAV sprayers require data-intensive algorithms for the processing of the images acquired, and expertise in the field of autonomous flight is usually needed. The present study concludes that UAV sprayers are still facing obstacle detection challenges due to their dynamic operating and loading conditions.

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Migliaccio, G., G. Mengali, and R. Galatolo. "Conflict detection and resolution algorithms for UAVs collision avoidance." Aeronautical Journal 118, no. 1205 (July 2014): 828–42. http://dx.doi.org/10.1017/s0001924000009568.

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Abstract Collision-avoidance is a safety-critical requirement to operate UAVs in non-segregated airspaces. In case of communication problems between a UAV and the corresponding pilot-in-command, a technology is required onboard the UAV to implement a capability to detect and avoid collision-hazards even autonomously. After an introduction to the problem of developing a so-called sense-and-avoid system and its avoid-function, this work presents a solution in terms of algorithms to implement the above capability. To detect and resolve potential mid-air conflicts, a geometric deterministic approach has been utilised: an intruder is modeled trough a moving-ellipsoid and a four-dimensional approach in the time-space domain provides the solution. The approach makes use of kinematics information to detect potential conflicts and to provide actions for conflict resolution, such as speed-changes in intensity and/or direction. The proposed solution also enables the UAV to meet the applicable vertical and horizontal minima of separation and to comply with real-time constraints.

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Chandran, Navaneetha Krishna, Mohammed Thariq Hameed Sultan, Andrzej Łukaszewicz, Farah Syazwani Shahar, Andriy Holovatyy, and Wojciech Giernacki. "Review on Type of Sensors and Detection Method of Anti-Collision System of Unmanned Aerial Vehicle." Sensors 23, no. 15 (July 30, 2023): 6810. http://dx.doi.org/10.3390/s23156810.

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Анотація:

Unmanned aerial vehicle (UAV) usage is increasing drastically worldwide as UAVs are used in various industries for many applications, such as inspection, logistics, agriculture, and many more. This is because performing a task using UAV makes the job more efficient and reduces the workload needed. However, for a UAV to be operated manually or autonomously, the UAV must be equipped with proper safety features. An anti-collision system is one of the most crucial and fundamental safety features that UAVs must be equipped with. The anti-collision system allows the UAV to maintain a safe distance from any obstacles. The anti-collision technologies are of crucial relevance to assure the survival and safety of UAVs. Anti-collision of UAVs can be varied in the aspect of sensor usage and the system’s working principle. This article provides a comprehensive overview of anti-collision technologies for UAVs. It also presents drone safety laws and regulations that prevent a collision at the policy level. The process of anti-collision technologies is studied from three aspects: Obstacle detection, collision prediction, and collision avoidance. A detailed overview and comparison of the methods of each element and an analysis of their advantages and disadvantages have been provided. In addition, the future trends of UAV anti-collision technologies from the viewpoint of fast obstacle detection and wireless networking are presented.

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41

Park, Jongho, and Namhoon Cho. "Collision Avoidance of Hexacopter UAV Based on LiDAR Data in Dynamic Environment." Remote Sensing 12, no. 6 (March 18, 2020): 975. http://dx.doi.org/10.3390/rs12060975.

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A reactive three-dimensional maneuver strategy for a multirotor Unmanned Aerial Vehicle (UAV) is proposed based on the collision cone approach to avoid potential collision with a single moving obstacle detected by an onboard sensor. A Light Detection And Ranging (LiDAR) system is assumed to be mounted on a hexacopter to obtain the obstacle information from the collected point clouds. The collision cone approach is enhanced to appropriately deal with the moving obstacle with the help of a Kalman filter. The filter estimates the position, velocity, and acceleration of the obstacle by using the LiDAR data as the associated measurement. The obstacle state estimate is utilized to predict the future trajectories of the moving obstacle. The collision detection and obstacle avoidance maneuver decisions are made considering the predicted trajectory of the obstacle. Numerical simulations, including a Monte Carlo campaign, are conducted to verify the performance of the proposed collision avoidance algorithm.

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42

Masiero, A., F. Fissore, A. Guarnieri, F. Pirotti, and A. Vettore. "UAV POSITIONING AND COLLISION AVOIDANCE BASED ON RSS MEASUREMENTS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1/W4 (August 26, 2015): 219–25. http://dx.doi.org/10.5194/isprsarchives-xl-1-w4-219-2015.

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Анотація:

In recent years, Unmanned Aerial Vehicles (UAVs) are attracting more and more attention in both the research and industrial communities: indeed, the possibility to use them in a wide range of remote sensing applications makes them a very flexible and attractive solution in both civil and commercial cases (e.g. precision agriculture, security and control, monitoring of sites, exploration of areas difficult to reach). Most of the existing UAV positioning systems rely on the use of the GPS signal. Despite this can be a satisfactory solution in open environments where the GPS signal is available, there are several operating conditions of interest where it is unavailable or unreliable (e.g. close to high buildings, or mountains, in indoor environments). Consequently, a different approach has to be adopted in these cases. This paper considers the use ofWiFi measurements in order to obtain position estimations of the device of interest. More specifically, to limit the costs for the devices involved in the positioning operations, an approach based on radio signal strengths (RSS) measurements is considered. Thanks to the use of a Kalman filter, the proposed approach takes advantage of the temporal dynamic of the device of interest in order to improve the positioning results initially provided by means of maximum likelihood estimations. The considered UAVs are assumed to be provided with communication devices, which can allow them to communicate with each other in order to improve their cooperation abilities. In particular, the collision avoidance problem is examined in this work.

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43

Ouahouah, Sihem, Miloud Bagaa, Jonathan Prados-Garzon, and Tarik Taleb. "Deep-Reinforcement-Learning-Based Collision Avoidance in UAV Environment." IEEE Internet of Things Journal 9, no. 6 (March 15, 2022): 4015–30. http://dx.doi.org/10.1109/jiot.2021.3118949.

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44

Sharma, R. K., and D. Ghose. "Collision avoidance between UAV clusters using swarm intelligence techniques." International Journal of Systems Science 40, no. 5 (May 2009): 521–38. http://dx.doi.org/10.1080/00207720902750003.

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45

Lee, Hyeon-Cheol, In-Kyu Kim, and Tae-Yeoul Yun. "Collision-avoidance radar-system antenna for a smart UAV." Microwave and Optical Technology Letters 44, no. 6 (2005): 498–501. http://dx.doi.org/10.1002/mop.20678.

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Koukiou, Georgia, and Vassilis Anastassopoulos. "UAV Sensors Autonomous Integrity Monitoring—SAIM." Electronics 12, no. 3 (February 2, 2023): 746. http://dx.doi.org/10.3390/electronics12030746.

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Анотація:

For Unmanned Aerial Vehicles (UAVs), it is of crucial importance to develop a technically advanced Collision Avoidance System (CAS). Such a system must necessarily consist of many sensors of various types, each one having special characteristics and performance. The poor performance of one of the sensors can lead to a total failure in collision avoidance if there is no provision for the performance of each separate sensor to be continuously monitored. In this work, a Sensor Autonomous Integrity Monitoring (SAIM) methodology is proposed. The configuration of the sensors and their interaction is based on a fusion procedure that involves a total of five sensors. Accordingly, the performance of each one of the sensors is continuously checked against the combined (fused) operation of the other four. A complementary experiment with a total of four sensors, one of which had low performance, was also conducted. Experimental results reveal a reliable approach for Sensor Autonomous Integrity Monitoring (SAIM). The method can be easily extended to a larger number of sensors.

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47

Sun, Huiyu, Guangming Song, Zhong Wei, and Ying Zhang. "P-like controllers with collision avoidance for passive bilateral teleoperation of a UAV." Industrial Robot: An International Journal 45, no. 1 (January 15, 2018): 152–66. http://dx.doi.org/10.1108/ir-04-2017-0072.

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Purpose This paper aims to tele-operate the movement of an unmanned aerial vehicle (UAV) in the obstructed environment with asymmetric time-varying delays. A simple passive proportional velocity errors plus damping injection (P-like) controller is proposed to deal with the asymmetric time-varying delays in the aerial teleoperation system. Design/methodology/approach This paper presents both theoretical and real-time experimental results of the bilateral teleoperation system of a UAV for collision avoidance over the wireless network. First, a position-velocity workspace mapping is used to solve the master-slave kinematic/dynamic dissimilarity. Second, a P-like controller is proposed to ensure the stability of the time-delayed bilateral teleoperation system with asymmetric time-varying delays. The stability is analyzed by the Lyapunov–Krasovskii function and the delay-dependent stability criteria are obtained under linear-matrix-inequalities conditions. Third, a vision-based localization is presented to calibrate the UAV’s pose and provide the relative distance for obstacle avoidance with a high accuracy. Finally, the performance of the teleoperation scheme is evaluated by both human-in-the-loop simulations and real-time experiments where a single UAV flies through the obstructed environment. Findings Experimental results demonstrate that the teleoperation system can maintain passivity and collision avoidance can be achieved with a high accuracy for asymmetric time-varying delays. Moreover, the operator could tele-sense the force reflection to improve the maneuverability in the aerial teleoperation. Originality/value A real-time bilateral teleoperation system of a UAV for collision avoidance is performed in the laboratory. A force and visual interface is designed to provide force and visual feedback of the slave environment to the operator.

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48

Zhang, Qiqian, Weiwei Xu, Honghai Zhang, and Han Li. "The Obstacle-Avoidance Path Planning for UAV Based on IOCAD." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 38, no. 2 (April 2020): 238–45. http://dx.doi.org/10.1051/jnwpu/20203820238.

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To plan the path for UAV flying in the complex, dense and irregular obstacles environment, this paper proposed an obstacle collision-avoidance detection model and designed an UAV path planning algorithm based on irregular obstacles collision-avoidance detection (IOCAD), which includes irregular obstacles pretreatment method. The proposed method uses the grid method to model the environment. Rough set theory and convexity filling are used to pretreat the obstacles, and the ray method is used to select the available points. The intersection detection and the distance detection are held for the obstacle to the flight path. The objective function minimizes the distance from the obstacle to the flight path to get planned paths. The simulation results show that the proposed method can effectively plan the paths with the constraints of the assumed environment and UAV performances. It is shown that the performance of the proposed method is sensitive to the grid length and safety distance. The optimized values for the grid length and safety distance are 0.5 km and 0.4 km respectively.

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Kelechi Kingsley Igbokwe, James Eke, and Patrick Uche Okafor. "Development of model predictive controller in avoidance design of Unmanned Aerial Vehicle (UAV)." World Journal of Advanced Engineering Technology and Sciences 9, no. 1 (May 30, 2023): 076–83. http://dx.doi.org/10.30574/wjaets.2023.9.1.0137.

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This research was accomplished by employing already developed mathematical model of a 6DoF UAV in free space through the motion of the 6DoF aircraft (unmanned aerial vehicle) determined by coordinate systems which allow an aircraft’s position and orientation in space to be kept tracked. The discrete method of the model developed for simulation in Simulink was realized with a suitable transfer function. Then, an artificial neural network model predictive adaptive controller that can handle the nonlinearities associated with the UAV was developed based on state space technique and the model predictive controller (MPC) utilizes a neural network model to envisage future plant (aircraft) responses to potential control signals. The developed model predictive controller network was successfully trained offline using Feed-forward Back-propagation algorithm with speed and position as inputs since the underlying objective of this work is to improve the speed and position in order to advance the safety collision distance of the UAV because these parameters are mostly considered in the avoidance maneuver performance. Also, the results reveal that the speed of the generated UAV is approximately the input speed of 75MS–1 during the time the UAV is affecting maneuvering. Significance of the result is that the proposed algorithm is capable of generating collision-free trajectory for different waypoint cases.

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50

Wan, Yu, Jun Tang, and Songyang Lao. "Research on the Collision Avoidance Algorithm for Fixed-Wing UAVs Based on Maneuver Coordination and Planned Trajectories Prediction." Applied Sciences 9, no. 4 (February 25, 2019): 798. http://dx.doi.org/10.3390/app9040798.

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This paper presents a novel collision avoidance (CA) algorithm for a cooperative fixed-wing unmanned aerial vehicle (UAV). The method is based on maneuver coordination and planned trajectory prediction. Each aircraft in a conflict generates three available maneuvers and predicts the corresponding planned trajectories. The algorithm coordinates planned trajectories between participants in a conflict, determines which combination of planned trajectories provides the best separation, eventually makes an agreement on the maneuver for collision avoidance and activates the preferred maneuvers when a collision is imminent. The emphasis is placed on providing protection for UAVs, while activating maneuvers late enough to reduce interference, which is necessary for collision avoidance in the formation and clustering of UAVs. The CA has been validated with various simulations to show the advantage of collision avoidance for continuous conflicts in multiple, high-dynamic, high-density and three-dimensional (3D) environments. It eliminates the disadvantage of traditional CA, which has high uncertainty, and takes the performance parameters of different aircraft into consideration and makes full use of the maneuverability of fixed-wing aircraft.

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