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Добірка наукової літератури з теми "Collision avoidance algorithm for fixed-wing UAVs"

Автор: Grafiati

Опубліковано: 6 вересня 2023

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Статті в журналах з теми "Collision avoidance algorithm for fixed-wing UAVs"

Zhao, Yu, Jifeng Guo, Chengchao Bai, and Hongxing Zheng. "Reinforcement Learning-Based Collision Avoidance Guidance Algorithm for Fixed-Wing UAVs." Complexity 2021 (January 16, 2021): 1–12. http://dx.doi.org/10.1155/2021/8818013.

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Анотація:

A deep reinforcement learning-based computational guidance method is presented, which is used to identify and resolve the problem of collision avoidance for a variable number of fixed-wing UAVs in limited airspace. The cooperative guidance process is first analyzed for multiple aircraft by formulating flight scenarios using multiagent Markov game theory and solving it by machine learning algorithm. Furthermore, a self-learning framework is established by using the actor-critic model, which is proposed to train collision avoidance decision-making neural networks. To achieve higher scalability, the neural network is customized to incorporate long short-term memory networks, and a coordination strategy is given. Additionally, a simulator suitable for multiagent high-density route scene is designed for validation, in which all UAVs run the proposed algorithm onboard. Simulated experiment results from several case studies show that the real-time guidance algorithm can reduce the collision probability of multiple UAVs in flight effectively even with a large number of aircraft.

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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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Анотація:

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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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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Basescu, Max, Adam Polevoy, Bryanna Yeh, Luca Scheuer, Erin Sutton, and Joseph Moore. "Agile Fixed-Wing UAVs for Urban Swarm Operations." Field Robotics 3, no. 1 (January 10, 2023): 725–65. http://dx.doi.org/10.55417/fr.2023023.

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Fixed-wing unmanned aerial vehicles (UAVs) offer significant performance advantages over rotary-wing UAVs in terms of speed, endurance, and efficiency. Such attributes make these vehicles ideally suited for long-range or high-speed reconnaissance operations and position them as valuable complementary members of a heterogeneous multi-robot team. However, these vehicles have traditionally been severely limited with regards to both vertical take-off and landing (VTOL) as well as maneuverability, which greatly restricts their utility in environments characterized by complex obstacle fields (e.g., forests or urban centers). This paper describes a set of algorithms and hardware advancements that enable agile fixed-wing UAVs to operate as members of a swarm in complex urban environments. At the core of our approach is a direct nonlinear model predictive control (NMPC) algorithm that is capable of controlling fixed-wing UAVs through aggressive post-stall maneuvers. We demonstrate in hardware how our online planning and control technique can enable navigation through tight corridors and in close proximity to obstacles.We also demonstrate how our approach can be combined with onboard stereo vision to enable high-speed flight in unknown environments. Finally, we describe our method for achieving swarm system integration; this includes a gimballed propeller design to facilitate automatic take-off, a precision deep-stall landing capability, multi-vehicle collision avoidance, and software integration with an existing swarm architecture.

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Lin, Zijie, Lina Castano, Edward Mortimer, and Huan Xu. "Fast 3D Collision Avoidance Algorithm for Fixed Wing UAS." Journal of Intelligent & Robotic Systems 97, no. 3-4 (June 29, 2019): 577–604. http://dx.doi.org/10.1007/s10846-019-01037-7.

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Blasi, Luciano, Egidio D’Amato, Immacolata Notaro, and Gennaro Raspaolo. "Clothoid-Based Path Planning for a Formation of Fixed-Wing UAVs." Electronics 12, no. 10 (May 12, 2023): 2204. http://dx.doi.org/10.3390/electronics12102204.

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Анотація:

Unmanned aerial vehicles (UAVs) are playing an increasingly crucial role in many applications such as search and rescue, delivery services, and military operations. However, one of the significant challenges in this area is to plan efficient and safe trajectories for UAV formations. This paper presents an optimization procedure for trajectory planning for fixed-wing UAV formations using graph theory and clothoid curves. The proposed planning strategy consists of two main steps. Firstly, the geometric optimization of paths is carried out using graphs for each UAV, providing piece-wise linear paths whose smooth connections are made with clothoids. Secondly, the geometric paths are transformed into time-dependent trajectories, optimizing the assigned aircraft speeds to avoid collisions by solving a mixed-integer optimal control problem for each UAV of the flight formation. The proposed method is effective in achieving suboptimal paths while ensuring collision avoidance between aircraft. A sensitivity analysis of the main parameters of the algorithm was conducted in ideal conditions, highlighting the possibility of decreasing the length of the optimal path by about 4.19%, increasing the number of points used in the discretization and showing a maximum path length reduction of about 10% compared with the average solution obtained with a similar algorithm using a graph based on random directions. Furthermore, the use of clothoids, whose parameters depend on the UAV performance constraints, provides smoother connections, giving a significant improvement over traditional straight-line or circular trajectories in terms of flight dynamics compliance and trajectory tracking capabilities. The method can be applied to various UAV formation scenarios, making it a versatile and practical tool for mission planning.

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Zhang, Jialong, Jianguo Yan, Pu Zhang, and Xiangjie Kong. "Collision Avoidance in Fixed-Wing UAV Formation Flight Based on a Consensus Control Algorithm." IEEE Access 6 (2018): 43672–82. http://dx.doi.org/10.1109/access.2018.2864169.

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Mu, Jun, and Zhaojie Sun. "Trajectory Design for Multi-UAV-Aided Wireless Power Transfer toward Future Wireless Systems." Sensors 22, no. 18 (September 10, 2022): 6859. http://dx.doi.org/10.3390/s22186859.

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Анотація:

In this paper, we investigate an unmanned aerial vehicle (UAV)-assisted wireless power transfer (WPT) system, in which a set of UAV-mounted mobile energy transmitters (ETs) are dispatched to broadcast wireless energy to an energy receiver (ER) on the ground. In particular, we aim to maximize the amount of energy transferred to the ER during a finite UAV’s flight period, subject to the UAV’s maximum speed and collision avoidance constraints. First, the basic one/two-UAV scenarios are researched in detail, which show that UAVs should hover at fixed locations during the whole charging period. Specifically, the Lagrange multiplier method is employed to solve the proposed optimization problem for the case of two UAV situation. Specifically, the general conclusions based on the theoretical analysis of one/two-UAV scenarios are drawn contribute to deducing the trajectory design of UAVs when the number of UAVs increases from three to seven. The obtained trajectory solution implies that UAVs should be evenly distributed on the circumference with point (0,0,H) as the center and UAVs’ safe distance as the radius. Finally, numerical results are provided to validate the trajectory design algorithm for the multiple UAVs-enabled single-user WPT system.

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Aldao, Enrique, Luis M. González-deSantos, Humberto Michinel, and Higinio González-Jorge. "UAV Obstacle Avoidance Algorithm to Navigate in Dynamic Building Environments." Drones 6, no. 1 (January 10, 2022): 16. http://dx.doi.org/10.3390/drones6010016.

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Анотація:

In this work, a real-time collision avoidance algorithm was presented for autonomous navigation in the presence of fixed and moving obstacles in building environments. The current implementation is designed for autonomous navigation between waypoints of a predefined flight trajectory that would be performed by an UAV during tasks such as inspections or construction progress monitoring. It uses a simplified geometry generated from a point cloud of the scenario. In addition, it also employs information from 3D sensors to detect and position obstacles such as people or other UAVs, which are not registered in the original cloud. If an obstacle is detected, the algorithm estimates its motion and computes an evasion path considering the geometry of the environment. The method has been successfully tested in different scenarios, offering robust results in all avoidance maneuvers. Execution times were measured, demonstrating that the algorithm is computationally feasible to be implemented onboard an UAV.

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FAN, Liyuan, Haozhe ZHANG, Zhao XU, Mingwei LYU, Jinwen HU, Chunhui ZHAO, and Xiaobin LIU. "A dense obstacle avoidance algorithm for UAVs based on safe flight corridor." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 40, no. 6 (December 2022): 1288–96. http://dx.doi.org/10.1051/jnwpu/20224061288.

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Анотація:

Aiming at the problem of autonomous obstacle avoidance of fixed-wing UAVs in a complex, dense and multi-obstacle environment, a path planning algorithm for fixed-wing UAVs based on a safe flight corridor is proposed. The difficulty of avoiding dense obstacles lies in the choice of obstacle circumvention and traversal: although circumvention is safer, the flight cost is greater; although the traversal cost is lower, the safety threat is higher. How to quickly solve the optimal path is the core issue. This paper firstly defines a safe flight corridor innovatively based on the maneuvering characteristics of fixed-wing UAVs and the Dubins curves. By comprehensively considering UAV flight safety and flight costs, an obstacle threat evaluation function is constructed. Secondly, in view of the computational complexity caused by the dense obstacles, an obstacle clustering algorithm based on obstacle density is proposed, and the nonlinear evaluation function in a high dynamic environment is quickly approximated by Monte Carlo sampling method. Finally, simulations verify the effectiveness of the proposed algorithm in solving dense obstacle avoidance for fixed-wing UAVs.

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Більше джерел

Дисертації з теми "Collision avoidance algorithm for fixed-wing UAVs"

Tony, Lima Agnel. "Mid-Air Collision Avoidance of Unmanned Aerial Vehicles." Thesis, 2021. https://etd.iisc.ac.in/handle/2005/5349.

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Анотація:

Autonomy is an essential feature of any robotic system. Aerial robots, commonly known as Unmanned Aerial Vehicles (UAVs), are being integrated into airspace and various trials towards achieving higher levels of autonomy are in progress. When multiple UAVs share the same airspace, safety from inter-UAV conflict is of utmost importance. Collision avoidance is an unavoidable feature of any UAV, and diverse methods addressing this problem are available in the literature. This thesis presents avoidance maps, a collision avoidance algorithm for fixed-wing UAVs. Avoidance of fixed-wing UAVs is challenging because of their inability to hover in contrast to their rotary-wing counterpart. Further, physical constraints like minimum turn radius make the process less flexible. The proposed avoidance map partitions the control input space of the UAVs into those leading to collision (red region) and avoidance (green region). Here, the control input used is constant lateral acceleration. Various versions of this are developed, which improves its computational cost. The algorithm could be implemented for cooperative, non-cooperative, and multiple UAVs and is demonstrated by suitable examples. In the next part of this thesis, precision UAV collision avoidance is discussed. This method is characterized by a gradual reduction of applied lateral acceleration during the avoidance process. Precision-control based avoidance optimizes the energy expenditure of the UAVs. The UAVs get away from their initial course while maneuvering. They are brought back to the initial direction of motion using Dubins curves, which joins two points via the shortest distance. The return to the course is achieved by Dubins path, where the necessary maneuvers are chosen from the avoidance map. An avoidance map can be used for realistic systems also. This utility is demonstrated by simulations using guidance models and six-degree-of-freedom UAV models. The avoidance map is further extended to few versions in the subsequent chapter. A time-graded version is introduced first, which classifies the collision region based on time to collision. This enables the use of several maneuvers from the collision region of the map as well. Next, asynchronous avoidance is introduced, which makes the avoidance process flexible for UAVs. The asynchronous avoidance maps compute avoidance maneuvers with a predetermined time delay for either of the UAVs. This results in one of the UAVs remaining on course for the desired time delay before maneuvering to avoid. Avoidance map is extended for constrained environments like corridors or geo-fences where the control input is the UAV heading angle. The application of avoidance maps for virtual intersections and lane changing for UAV virtual skyways are also discussed in this work. The last part of the thesis formulates collision avoidance of UAVs using game theory. This applies to both fixed-wing and rotor-craft categories and is based on the solution concept of correlated equilibrium. UAVs are considered to be intelligent players and the conflict resolution process is formulated as a game. The decision-making framework, which is termed CONCORD, works independently of the kind of avoidance algorithm used. The framework is found suitable for cooperative, non-cooperative, and multiple UAVs. It is shown that the proposed framework fairly resolves conflicts among UAVs and guarantees safety. A brief discussion on UAV integration to airspace and concord integration to such UAV traffic management system concludes this work.

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Частини книг з теми "Collision avoidance algorithm for fixed-wing UAVs"

Fan, Liyuan, Yifei Lei, Lei Lu, Jinwen Hu, Chunhui Zhao, and Zhao Xu. "Collision Avoidance Formation Control of Fixed-Wing UAVs with Nonholonomic Kinematic Constraints." In Proceedings of 2022 International Conference on Autonomous Unmanned Systems (ICAUS 2022), 233–42. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0479-2_22.

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Su, Meimei, Huixia Liu, Jinwen Hu, Chunhui Zhao, Xiaolei Hou, Quan Pan, and Caijuan Jia. "Path Planning Based on Improved MPC for Fixed Wing UAVs with Collision Avoidance." In Lecture Notes in Electrical Engineering, 2287–96. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8155-7_192.

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Тези доповідей конференцій з теми "Collision avoidance algorithm for fixed-wing UAVs"

Lin, Zijie, Lina Castano, and Huan Xu. "A Fast Obstacle Collision Avoidance Algorithm for Fixed Wing UAS." In 2018 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, 2018. http://dx.doi.org/10.1109/icuas.2018.8453307.

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Ryan, Allison D., David L. Nguyen, and J. Karl Hedrick. "Hybrid Control for UAV-Assisted Search and Rescue." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-80648.

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Анотація:

We develop a decentralized hybrid controller for fixed-wing UAVs assisting a manned helicopter in a United States Coast Guard search and rescue mission. The UAVs assist the manned helicopter by providing an expanded sensor footprint using onboard cameras. We consider two UAVs, one flying on either side of the helicopter, with constant velocity and maximum turn rate constraints. Tracking the helicopter around sharp corners will be difficult due to these constraints and the difference in path lengths for the two UAVs. To solve this problem, we propose a hybrid controller that allows the UAVs to swap positions in an attempt to improve the tracking and ground coverage performance of the formation. We discuss tracking control, the position swapping algorithm and collision avoidance. Simulation results demonstrate improved search efficiency and aircraft safety.

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Sherman, Tristan, Thomas Elemy, Margaux Retherford, Tristan Cady, and Subodh Bhandari. "Collision Avoidance System for Fixed-Wing UAVs using Ping-2020 ADS-B Transreceivers." In AIAA Scitech 2019 Forum. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2019. http://dx.doi.org/10.2514/6.2019-2075.

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Browne, Jeremy P., Cole Neuhart, Travis W. Moleski, and Jay Wilhelm. "Minimal Deviation from Mission Path After Automated Collision Avoidance for Small Fixed Wing UAVs." In AIAA SCITECH 2022 Forum. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2022. http://dx.doi.org/10.2514/6.2022-0275.

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Zhang, Shuiqing, Tianye Xu, Hui Cheng, and Fan Liang. "Collision Avoidance of Fixed-Wing UAVs in Dynamic Environments Based on Spline-RRT and Velocity Obstacle." In 2020 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, 2020. http://dx.doi.org/10.1109/icuas48674.2020.9213934.

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Yan, Chao, Xiaojia Xiang, Chang Wang, and Zhen Lan. "Flocking and Collision Avoidance for a Dynamic Squad of Fixed-Wing UAVs Using Deep Reinforcement Learning." In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2021. http://dx.doi.org/10.1109/iros51168.2021.9636183.

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Lee, Seongheon, Hyochoong Bang, and Dongjin Lee. "Predictive ground collision avoidance system for UAV applications: PGCAS design for fixed-wing UAVs and processor in the loop simulation." In 2016 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, 2016. http://dx.doi.org/10.1109/icuas.2016.7502561.

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