Journal articles: 'Collision avoidance algorithm for fixed-wing UAVs'

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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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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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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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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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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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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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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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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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Yan, Peng, Zhuo Yan, Hongxing Zheng, and Jifeng Guo. "A Fixed Wing UAV Path Planning Algorithm Based On Genetic Algorithm and Dubins Curve Theory." MATEC Web of Conferences 179 (2018): 03003. http://dx.doi.org/10.1051/matecconf/201817903003.

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Path planning is an essential problem in the autonomy research of UAVs. This paper proposes a new path planning algorithm for fixed wing UAVs based on genetic algorithm (GA) and Dubins curve theory. Path planning is treated as a global optimization problem under certain constraints, including the velocity vectors of initial and goal points and the minimum turning radius of UAVs. Dubins curve theory is utilized to satisfy the velocity vector constraints. GA is utilized to generate the shortest threats avoidance path in a 2D environment. A new encoding scheme is proposed, taking into account initial circles, goal circles, threats circles and the positional relationship between the path and these circles. The 2D Dubins path is converted to Dubins airplane path by adding a flight-path angle to it. The algorithm was tested in a complex flight environment and the planned path was tracked by a 6DOF Simulink model of a fixed wing UAV. Results show that the algorithm can generate shortest threats avoidance path in a complicated 3D environment and meanwhile satisfy constraints mentioned above.

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Lai, Ying-Chih, and Zong-Ying Huang. "Detection of a Moving UAV Based on Deep Learning-Based Distance Estimation." Remote Sensing 12, no. 18 (September 17, 2020): 3035. http://dx.doi.org/10.3390/rs12183035.

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Distance information of an obstacle is important for obstacle avoidance in many applications, and could be used to determine the potential risk of object collision. In this study, the detection of a moving fixed-wing unmanned aerial vehicle (UAV) with deep learning-based distance estimation to conduct a feasibility study of sense and avoid (SAA) and mid-air collision avoidance of UAVs is proposed by using a monocular camera to detect and track an incoming UAV. A quadrotor is regarded as an owned UAV, and it is able to estimate the distance of an incoming fixed-wing intruder. The adopted object detection method is based on the you only look once (YOLO) object detector. Deep neural network (DNN) and convolutional neural network (CNN) methods are applied to exam their performance in the distance estimation of moving objects. The feature extraction of fixed-wing UAVs is based on the VGG-16 model, and then its result is applied to the distance network to estimate the object distance. The proposed model is trained by using synthetic images from animation software and validated by using both synthetic and real flight videos. The results show that the proposed active vision-based scheme is able to detect and track a moving UAV with high detection accuracy and low distance errors.

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Mirzaee Kahagh, A., F. Pazooki, and S. Etemadi Haghighi. "Obstacle avoidance in V-shape formation flight of multiple fixed-wing UAVs using variable repulsive circles." Aeronautical Journal 124, no. 1282 (October 23, 2020): 1979–2000. http://dx.doi.org/10.1017/aer.2020.81.

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ABSTRACTA formation control and obstacle avoidance algorithm has been introduced in this paper for the V-shape formation flight of fixed-wing UAVs (Unmanned Aerial Vehicles) using the potential functions method. An innovative vector approach has been suggested to fix the conventional challenge in employing the artificial potential field (APF) approach (the creation of local minimums). A method called variable repulsive circles (VRC) has been then presented aimed at designing proper flight paths tailored with functional limitations of fixed-wing UAVs in facing obstacles. Finally, the efficiency of the designed algorithm has been examined and evaluated for different flight scenarios.

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Qu, Yue, and Wenjun Yi. "Three-Dimensional Obstacle Avoidance Strategy for Fixed-Wing UAVs Based on Quaternion Method." Applied Sciences 12, no. 3 (January 18, 2022): 955. http://dx.doi.org/10.3390/app12030955.

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This work provides a generalization of the three-dimensional velocity obstacle (VO) collision avoidance strategy for nonlinear second-order underactuated systems in three-dimensional dynamic uncertain environments. A hierarchical architecture is exploited to deal with conflicting multiple subtasks, which are defined as several rotations and are parameterized by quaternions. An improved VO method considering the kinodynamic constraints of a class of fixed-wing unmanned aerial vehicles (UAV) is proposed to implement the motion planning. The position error and velocity error can be mapped onto one desired axis so that, only relying on an engine, UAVs can achieve the goal of point tracking without collision. Additionally, the performance of the closed-loop system is demonstrated through a series of simulations performed in a three-dimensional manner.

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Bulka, Eitan, and Meyer Nahon. "Reactive Obstacle-Avoidance for Agile, Fixed-Wing, Unmanned Aerial Vehicles." Field Robotics 2, no. 1 (March 10, 2022): 1507–66. http://dx.doi.org/10.55417/fr.2022048.

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Agile, fixed-wing, aircraft have been proposed for diverse applications, due to their enhanced flight efficiency, compared to rotorcraft, and their superior maneuverability, relative to conventional, fixed-wing, aircraft. We present a novel, reactive, obstacle-avoidance algorithm that enables autonomous flight through unknown, cluttered environments using only on-board sensing and computation. The method selects a reference trajectory in real-time from a pre-computed library, based on goal location, instantaneous point cloud data, and the aircraft states. At each time-step, a cost is assigned to candidate trajectories that are collision-free and lead to the edge of the obstacle sensor’s field-of-view, with cost based on both distance to obstacles, and the goal. The lowest cost reference trajectory is then tracked. If all potential trajectories result in a collision, the aircraft has enough space to come to a stop, which theoretically guarantees collision-free flight. Our work demonstrates autonomous flight in unknown and unstructured environments using only on-board sensing (stereo camera, IMU, and GPS) and computation with an agile, fixed-wing, aircraft in both simulation and outdoor flight tests. During flight testing, the aircraft cumulatively flew 4.4km autonomously in outdoor environments with trees as obstacles with an average speed of 8.1ms−1 and a top speed of 14.4ms−1. To the best of our knowledge, ours is the first obstacle-avoidance algorithm suitable for agile, fixed-wing, aircraft that can theoretically guarantee collision-free flight and has been validated experimentally using only on-board sensing and computation in an unknown environment.

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Wang, Yajing, Xiangke Wang, Shulong Zhao, and Lincheng Shen. "A Hierarchical Collision Avoidance Architecture for Multiple Fixed-Wing UAVs in an Integrated Airspace." IFAC-PapersOnLine 53, no. 2 (2020): 2477–82. http://dx.doi.org/10.1016/j.ifacol.2020.12.199.

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de Ruiter, A. H. J., and S. Owlia. "Autonomous obstacle avoidance for fixed-wing unmanned aerial vehicles." Aeronautical Journal 119, no. 1221 (November 2015): 1415–36. http://dx.doi.org/10.1017/s0001924000011325.

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AbstractThis paper investigates a method for autonomous obstacle avoidance for fixed-wing unmanned aerial vehicles (UAVs), utilising potential fluid flow theory. The obstacle avoidance algorithm needs only compute the instantaneous local potential velocity vector, which is passed to the flight control laws as a direction command. The approach is reactive, and can readily accommodate real-time changes in obstacle information. UAV manoeuvring constraints on turning or pull-up radii, are accounted for by approximating obstacles by bounding rectangles, with wedges added to their front and back to shape the resulting fluid pathlines. It is shown that the resulting potential flow velocity field is completely determined by the obstacle field geometry, allowing one to determine a non-dimensional relationship between obstacle added wedge-length and the corresponding minimum pathline radius of curvature, which can then be readily scaled in on-board implementation. The efficacy of the proposed approach has been demonstrated numerically with an Aerosonde UAV model.

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Suo, Wenbo, Mengyang Wang, Dong Zhang, Zhongjun Qu, and Lei Yu. "Formation Control Technology of Fixed-Wing UAV Swarm Based on Distributed Ad Hoc Network." Applied Sciences 12, no. 2 (January 6, 2022): 535. http://dx.doi.org/10.3390/app12020535.

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The formation control technology of the unmanned aerial vehicle (UAV) swarm is a current research hotspot, and formation switching and formation obstacle avoidance are vital technologies. Aiming at the problem of formation control of fixed-wing UAVs in distributed ad hoc networks, this paper proposed a route-based formation switching and obstacle avoidance method. First, the consistency theory was used to design the UAV swarm formation control protocol. According to the agreement, the self-organized UAV swarm could obtain the formation waypoint according to the current position information, and then follow the corresponding rules to design the waypoint to fly around and arrive at the formation waypoint at the same time to achieve formation switching. Secondly, the formation of the obstacle avoidance channel was obtained by combining the geometric method and an intelligent path search algorithm. Then, the UAV swarm was divided into multiple smaller formations to achieve the formation obstacle avoidance. Finally, the abnormal conditions during the flight were handled. The simulation results showed that the formation control technology based on distributed ad hoc network was reliable and straightforward, easy to implement, robust in versatility, and helpful to deal with the communication anomalies and flight anomalies with variable topology.

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Wu, Weihuan, Xiangyin Zhang, and Yang Miao. "Starling-Behavior-Inspired Flocking Control of Fixed-Wing Unmanned Aerial Vehicle Swarm in Complex Environments with Dynamic Obstacles." Biomimetics 7, no. 4 (November 26, 2022): 214. http://dx.doi.org/10.3390/biomimetics7040214.

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For the sake of accomplishing the rapidity, safety and consistency of obstacle avoidance for a large-scale unmanned aerial vehicle (UAV) swarm in a dynamic and unknown 3D environment, this paper proposes a flocking control algorithm that mimics the behavior of starlings. By analyzing the orderly and rapid obstacle avoidance behavior of a starling flock, a motion model inspired by a flock of starlings is built, which contains three kinds of motion patterns, including the collective pattern, evasion pattern and local-following pattern. Then, the behavior patterns of the flock of starlings are mapped on a fixed-wing UAV swarm to improve the ability of obstacle avoidance. The key contribution of this paper is collective and collision-free motion planning for UAV swarms in unknown 3D environments with dynamic obstacles. Numerous simulations are conducted in different scenarios and the results demonstrate that the proposed algorithm improves the speed, order and safety of the UAV swarm when avoiding obstacles.

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Xu, Dan, Yunxiao Guo, Zhongyi Yu, Zhenfeng Wang, Rongze Lan, Runhao Zhao, Xinjia Xie, and Han Long. "PPO-Exp: Keeping Fixed-Wing UAV Formation with Deep Reinforcement Learning." Drones 7, no. 1 (December 31, 2022): 28. http://dx.doi.org/10.3390/drones7010028.

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Flocking for fixed-Wing Unmanned Aerial Vehicles (UAVs) is an extremely complex challenge due to fixed-wing UAV’s control problem and the system’s coordinate difficulty. Recently, flocking approaches based on reinforcement learning have attracted attention. However, current methods also require that each UAV makes the decision decentralized, which increases the cost and computation of the whole UAV system. This paper researches a low-cost UAV formation system consisting of one leader (equipped with the intelligence chip) with five followers (without the intelligence chip), and proposes a centralized collision-free formation-keeping method. The communication in the whole process is considered and the protocol is designed by minimizing the communication cost. In addition, an analysis of the Proximal Policy Optimization (PPO) algorithm is provided; the paper derives the estimation error bound, and reveals the relationship between the bound and exploration. To encourage the agent to balance their exploration and estimation error bound, a version of PPO named PPO-Exploration (PPO-Exp) is proposed. It can adjust the clip constraint parameter and make the exploration mechanism more flexible. The results of the experiments show that PPO-Exp performs better than the current algorithms in these tasks.

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Cai, Fake, Danyang Liu, Weihan Yuan, Shuo Ding, Yongxu Ning, and Chenyang Yue. "Motion Planning of Unmanned Aerial Vehicle Based on Rapid-exploration Random Tree Algorithm." Journal of Physics: Conference Series 2283, no. 1 (June 1, 2022): 012017. http://dx.doi.org/10.1088/1742-6596/2283/1/012017.

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Abstract In the study of route planning problems in complex environments, in order to reduce the flight cost of unmanned aerial vehicles (UAVs), it is necessary to achieve a better balance between planning time and path quality. This paper utilizes the Rapid-exploration Random Tree (RRT) algorithm for motion planning of a fixed-wing UAV and a multi-rotor UAV (i.e., a quad-rotor UAV), and gives the origin and destination locations on a 3-D map. By following aerodynamic constraints such as maximum roll angle, flight path angle, and airspeed, a collision-free and flight-friendly path is found through simulation. In addition, this paper employs a path smoothing algorithm to simplify the 3-D Dubins path and generates the shortest trajectory. The simulation results show that the RRT and the optimization method have faster convergence speed and shorter search time, reduce redundant planning points, shorten the planning track, and improve the track planning efficiency.

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Kim, A. Ram, Shawn Keshmiri, Weizhang Huang, and Gonzalo Garcia. "Guidance of Multi-Agent Fixed-Wing Aircraft Using a Moving Mesh Method." Unmanned Systems 04, no. 03 (July 2016): 227–44. http://dx.doi.org/10.1142/s2301385016500084.

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This paper presents a novel guidance logic for multi-agent fixed-wing unmanned aerial systems using a moving mesh method. The moving mesh method is originally designed for use in the adaptive numerical solution of partial differential equations, where a high proportion of mesh points are placed in the regions of large solution variations and few points in the rest of the domain. In this work, the positions of the aircraft are considered as mesh nodes connected to form a triangular mesh in two spatial dimensions. The outer aircraft positions are planned with the reference point algorithm. This logic provides the outer agents moving point positions that are relative to a virtual point position with the desired heading angle and velocity. The inner agents, or interior mesh nodes, are moved with a moving mesh technique to keep the whole mesh as uniform as possible. The moving mesh technique has built-in mechanisms to keep the mesh as uniform as possible and prevent nodes from crossing over or tangling. This property can be seen as an automatic internal collision avoidance mechanism. It also has explicit formulas for nodal velocities, making the technique easy to implement on computer. The mesh nodes are replaced by unmanned aerial systems with nonlinear six degrees of freedom dynamics. The centralized moving mesh guidance is complimented by a decentralized nonlinear predictive controller to control each aircraft. To validate flexibility and coherency of agents and formation, the moving point concept is used in the simulation to follow an arbitrary, linear, sinewave-like, or curvature shaped flight segments. Robustness of the algorithm is also verified where agents were affected by external wind.

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Stastny, Thomas J., Gonzalo A. Garcia, and Shawn S. Keshmiri. "Collision and Obstacle Avoidance in Unmanned Aerial Systems Using Morphing Potential Field Navigation and Nonlinear Model Predictive Control." Journal of Dynamic Systems, Measurement, and Control 137, no. 1 (August 28, 2014). http://dx.doi.org/10.1115/1.4028034.

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This paper presents a novel approach to collision and obstacle avoidance in fixed-wing unmanned aerial systems (UASs), vehicles with high speed and high inertia, operating in proximal or congested settings. A unique reformulation of classical artificial potential field (APF) navigational approaches, adaptively morphing the functions' shape considering six-degrees-of-freedom (6DOF) dynamic characteristics and constraints of fixed-wing aircraft, is fitted to an online predictive and prioritized waypoint planning algorithm for generation of evasive paths during abrupt encounters. The time-varying waypoint horizons output from the navigation unit are integrated into a combined guidance and nonlinear model predictive control scheme. Real-time avoidance capabilities are demonstrated in full nonlinear 6DOF simulation of a large unmanned aircraft showcasing evasion efficiency with respect to classical methods and collision free operation in a congested urban scenario.

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Liu, Xuzan, Yu Han, and Jian Chen. "Discrete pigeon-inspired optimization-simulated annealing algorithm and optimal reciprocal collision avoidance scheme for fixed-wing UAV formation assembly." Unmanned Systems, December 31, 2020. http://dx.doi.org/10.1142/s230138502141003x.

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Mirzaee Kahagh, A., F. Pazooki, S. Etemadi Haghighi, and D. Asadi. "Real-time formation control and obstacle avoidance algorithm for fixed-wing UAVs." Aeronautical Journal, February 23, 2022, 1–23. http://dx.doi.org/10.1017/aer.2022.9.

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Abstract This paper proposes a novel real-time formation control and obstacle avoidance algorithm for multiple fixed-wing UAVs. A formation control algorithm is designed by a combination of the virtual structure, leader-follower, and artificial potential fields methods and harnessing the advantages of those approaches. The kinematic and dynamic constraints of fixed-wing UAVs are considered in the path planning. The performance of the proposed algorithm is examined through simulation in Matlab software by applying the translational dynamics of fixed-wing UAVs. Simulations of different complex scenarios demonstrate the effectiveness of the presented formation flight algorithm through generating multiple efficient paths, which are fully consistent with the functional constraints of the UAVs in the presence of obstacles.

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Aiello, Giuseppe, Kimon P. Valavanis, and Alessandro Rizzo. "Fixed-Wing UAV Energy Efficient 3D Path Planning in Cluttered Environments." Journal of Intelligent & Robotic Systems 105, no. 3 (July 2022). http://dx.doi.org/10.1007/s10846-022-01608-1.

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AbstractUAV path planning in 3D cluttered and uncertain environments centers on finding an optimal / sub-optimal collision-free path, considering in parallel geometric, physical and temporal constraints, fox example, obstacles, infrastructure, physical or artificial landmarks, etc. This paper introduces a novel node-based algorithm, called Energy Efficient A* (EEA*), which is based on the A* search algorithm, but overcomes some of its key limitations. The EEA* deals with 3D environments, it is robust converging fast to the solution, it is energy efficient and it is real-time implementable and executable. In addition to the EEA*, a local path planner is also derived to cope with unknown dynamic threats within the working environment. The EEA* and the local path planner are first implemented and evaluated via simulated experiments using a fixed-wing UAV operating in mountain-like 3D environments, and in the presence of unknown dynamic obstacles. This is followed by evaluating a set up where three UAVs are commanded to follow their respective paths in a safe way. The energy efficiency of EEA* is also tested and compared with the conventional A* algorithm.

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Babel, Luitpold. "Online flight path planning with flight time constraints for fixed-wing UAVs in dynamic environments." International Journal of Intelligent Unmanned Systems ahead-of-print, ahead-of-print (May 4, 2021). http://dx.doi.org/10.1108/ijius-11-2020-0063.

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PurposeA major challenge for mission planning of aircraft is to generate flight paths in highly dynamic environments. This paper presents a new approach for online flight path planning with flight time constraints for fixed-wing UAVs. The flight paths must take into account the kinematic restrictions of the vehicle and be collision-free with terrain, obstacles and no-fly areas. Moreover, the flight paths are subject to time constraints such as predetermined time of arrival at the target or arrival within a specified time interval.Design/methodology/approachThe proposed flight path planning algorithm is an evolution of the well-known RRT* algorithm. It uses three-dimensional Dubins paths to reflect the flight capabilities of the air vehicle. Requirements for the flight time are realized by skillfully concatenating two rapidly exploring random trees rooted in the start and target point, respectively.FindingsThe approach allows to consider static obstacles, obstacles which might pop up unexpectedly, as well as moving obstacles. Targets might be static or moving with constantly changing course. Even a change of the target during flight, a change of the target approach direction or a change of the requested time of arrival is included.Originality/valueThe capability of the flight path algorithm is demonstrated by simulation results. Response times of fractions of a second qualify the algorithm for real-time applications in highly dynamic scenarios.

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Journal articles on the topic 'Collision avoidance algorithm for fixed-wing UAVs'

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