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1
Wei, Zhiqiang, Yu Hu, Zhiyan Dong, Wenbin Bai, Haiyue Yang, Yaen Xie, Feng Shu, and Lihua Zhang. "UAVs Path Planning based on Combination of Rapidly Exploring Random Tree and Rauch-Tung-Striebel Filter." Journal of Physics: Conference Series 2755, no. 1 (May 1, 2024): 012031. http://dx.doi.org/10.1088/1742-6596/2755/1/012031.
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Abstract Aiming at the problem of Unmanned Aerial Vehicle(UAV) formation path planning under complex constraints, a UAV formation path planning method based on the combination of Rapidly exploring Random Tree (RRT) and Rauch-Tung-Striebel (RTS) filter is proposed. Firstly, a path planning algorithm based on the improved RRT algorithm with adaptive step size is de-signed to solve the problem that the RRT algorithm is easy to fall into local optimum. Then, an RTS filter is introduced to smooth the trajectory planned by the improved RRT algorithm to achieve curvature continuity. Finally, taking the smooth trajectory as the reference, a UAV formation path planning algorithm over the Artificial Potential Field (APF) method is designed. The simulation results show that the designed UAV formation path planning algorithm can solve the planning problems of single trajectory and formation trajectories in complex constrained space, and can plan the formation trajectory with continuous curvature, to facilitate the UAV trajectory tracking control.
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Wang, Xing, Jeng-Shyang Pan, Qingyong Yang, Lingping Kong, Václav Snášel, and Shu-Chuan Chu. "Modified Mayfly Algorithm for UAV Path Planning." Drones 6, no. 5 (May 23, 2022): 134. http://dx.doi.org/10.3390/drones6050134.
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The unmanned aerial vehicle (UAV) path planning problem is primarily concerned with avoiding collision with obstacles while determining the best flight path to the target position. This paper first establishes a cost function to transform the UAV route planning issue into an optimization issue that meets the UAV’s feasible path requirements and path safety constraints. Then, this paper introduces a modified Mayfly Algorithm (modMA), which employs an exponent decreasing inertia weight (EDIW) strategy, adaptive Cauchy mutation, and an enhanced crossover operator to effectively search the UAV configuration space and discover the path with the lowest overall cost. Finally, the proposed modMA is evaluated on 26 benchmark functions as well as the UAV route planning problem, and the results demonstrate that it outperforms the other compared algorithms.
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Chen, Xiaotong, Qin Li, Ronghao Li, Xiangyuan Cai, Jiangnan Wei, and Hongying Zhao. "UAV Network Path Planning and Optimization Using a Vehicle Routing Model." Remote Sensing 15, no. 9 (April 22, 2023): 2227. http://dx.doi.org/10.3390/rs15092227.
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Unmanned aerial vehicle (UAV) remote sensing has been applied in various fields due to its rapid implementation ability and high-resolution imagery. Single-UAV remote sensing has low efficiency and struggles to meet the growing demands of complex aerial remote sensing tasks, posing challenges for practical applications. Using multiple UAVs or a UAV network for remote sensing applications can overcome the difficulties and provide large-scale ultra-high-resolution data rapidly. UAV network path planning is required for these important applications. However, few studies have investigated UAV network path planning for remote sensing observations, and existing methods have various problems in practical applications. This paper proposes an optimization algorithm for UAV network path planning based on the vehicle routing problem (VRP). The algorithm transforms the task assignment problem of the UAV network into a VRP and optimizes the task assignment result by minimizing the observation time of the UAV network. The optimized path plan prevents route crossings effectively. The accuracy and validity of the proposed algorithms were verified by simulations. Moreover, comparative experiments with different task allocation objectives further validated the applicability of the proposed algorithm for various remote sensing applications
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Wu, Yan, Mingtao Nie, Xiaolei Ma, Yicong Guo, and Xiaoxiong Liu. "Co-Evolutionary Algorithm-Based Multi-Unmanned Aerial Vehicle Cooperative Path Planning." Drones 7, no. 10 (September 26, 2023): 606. http://dx.doi.org/10.3390/drones7100606.
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Multi-UAV cooperative path planning is a key technology to carry out multi-UAV tasks, and its research has important practical significance. A multi-UAV cooperative path is a combination of single-UAV paths, so the idea of problem decomposition is effective to deal with multi-UAV cooperative path planning. With this analysis, a multi-UAV cooperative path planning algorithm based on co-evolution optimization was proposed in this paper. Firstly, by analyzing the meaning of multi-UAV cooperative flight, the optimization model of multi-UAV cooperative path planning was given. Secondly, we designed the cost function of multiple UAVs with the penalty function method to deal with multiple constraints and designed two information-sharing strategies to deal with the combination path search between multiple UAVs. The two information-sharing strategies were called the optimal individual selection strategy and the mixed selection strategy. The new cooperative path planning algorithm was presented by combining the above designation and co-evolution algorithm. Finally, the proposed algorithm is applied to a rendezvous task in complex environments and compared with two evolutionary algorithms. The experimental results show that the proposed algorithm can effectively cope with the multi-UAV cooperative path planning problem in complex environments.
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Liu, Yongbei, Naiming Qi, Weiran Yao, Jun Zhao, and Song Xu. "Cooperative Path Planning for Aerial Recovery of a UAV Swarm Using Genetic Algorithm and Homotopic Approach." Applied Sciences 10, no. 12 (June 17, 2020): 4154. http://dx.doi.org/10.3390/app10124154.
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To maximize the advantages of being low-cost, highly mobile, and having a high flexibility, aerial recovery technology is important for unmanned aerial vehicle (UAV) swarms. In particular, the operation mode of “launch-recovery-relaunch” will greatly improve the efficiency of a UAV swarm. However, it is difficult to realize large-scale aerial recovery of UAV swarms because this process involves complex multi-UAV recovery scheduling, path planning, rendezvous, and acquisition problems. In this study, the recovery problem of a UAV swarm by a mother aircraft has been investigated. To solve the problem, a recovery planning framework is proposed to establish the coupling mechanism between the scheduling and path planning of a multi-UAV aerial recovery. A genetic algorithm is employed to realize efficient and precise scheduling. A homotopic path planning approach is proposed to cover the paths with an expected length for long-range aerial recovery missions. Simulations in representative scenarios validate the effectiveness of the recovery planning framework and the proposed methods. It can be concluded that the recovery planning framework can achieve a high performance in dealing with the aerial recovery problem.
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Fu, Zhangjie, Jingnan Yu, Guowu Xie, Yiming Chen, and Yuanhang Mao. "A Heuristic Evolutionary Algorithm of UAV Path Planning." Wireless Communications and Mobile Computing 2018 (September 9, 2018): 1–11. http://dx.doi.org/10.1155/2018/2851964.
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With the rapid development of the network and the informatization of society, how to improve the accuracy of information is an urgent problem to be solved. The existing method is to use an intelligent robot to carry sensors to collect data and transmit the data to the server in real time. Many intelligent robots have emerged in life; the UAV (unmanned aerial vehicle) is one of them. With the popularization of UAV applications, the security of UAV has also been exposed. In addition to some human factors, there is a major factor in the UAV’s endurance. UAVs will face a problem of short battery life when performing flying missions. In order to solve this problem, the existing method is to plan the path of UAV flight. In order to find the optimal path for a UAV flight, we propose three cost functions: path security cost, length cost, and smoothness cost. The path security cost is used to determine whether the path is feasible; the length cost and smoothness cost of the path directly affect the cost of the energy consumption of the UAV flight. We proposed a heuristic evolutionary algorithm that designed several evolutionary operations: substitution operations, crossover operations, mutation operations, length operations, and smoothness operations. Through these operations to enhance our build path effect. Under the analysis of experimental results, we proved that our solution is feasible.
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Guo, Yifan, and Zhiping Liu. "UAV Path Planning Based on Deep Reinforcement Learning." International Journal of Advanced Network, Monitoring and Controls 8, no. 3 (September 1, 2023): 81–88. http://dx.doi.org/10.2478/ijanmc-2023-0068.
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Abstract Path planning is one of the very important aspects of UAV navigation control, which refers to the UAV searching for an optimal or near-optimal route from the starting point to the end point according to the performance indexes such as time, distance, et al. The path planning problem has a long history and has more abundant algorithms. The path planning problem has a long history and a rich set of algorithms, but most of the current algorithms require a known environment, however, in most cases, the environment model is difficult to describe and obtain, and the algorithms perform less satisfactorily. To address the above problems, this paper proposes a UAV path planning method based on deep reinforcement learning algorithm. Based on the OpenAI-GYM architecture, a 3D map environment model is constructed, with the map grid as the state set and 26 actions as the action set, which does not need an environment model and relies on its own interaction with the environment to complete the path planning task. The algorithm is based on stochastic process theory, modeling the path planning problem as a Markov Decision Process (MDP), fitting the UAV path planning decision function and state-action function, and designing the DQN algorithm model according to the state space, action space and network structure. The algorithm enables the intelligences to carry out strategy iteration efficiently. Through simulation, the DQN algorithm is verified to avoid obstacles and complete the path planning task in only about 160 rounds, which validates the effectiveness of the proposed path planning algorithm.
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Xu, Yiqing, Jiaming Li, and Fuquan Zhang. "A UAV-Based Forest Fire Patrol Path Planning Strategy." Forests 13, no. 11 (November 18, 2022): 1952. http://dx.doi.org/10.3390/f13111952.
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The application of UAVs in forest fire monitoring has attracted increasing attention. When a UAV carries out forest fire monitoring cruises in a large area of the forest, one of the main problems is planning an appropriate cruise path so that the UAV can start from the starting point, cruise the entire area with little detour, and return to the initial position within its maximum cruise distance. In this paper, we propose a flight path planning method for UAV forest fire monitoring based on a forest fire risk map. According to the forest fire risk level, the method uses the ring self-organizing mapping (RSOM) algorithm to plan a corresponding flight path. In addition, since it is difficult for a single UAV to complete a single full-path cruise task in a large area within its maximum cruise time, a multi-UAV cruise scheme is proposed. First, the Gaussian mixture clustering algorithm is used to cluster the study area and divide it into several subareas. In combination with the RSOM algorithm, the corresponding path is planned for each UAV. A simulation with an actual dataset showed that the proposed method solves the problem of UAV patrol path planning for forest fire monitoring and can complete the task within a reasonable time.
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Liu, Zhengqing, Xinhua Wang, and Kangyi Li. "Research on path planning of multi-rotor UAV based on improved artificial potential field method." MATEC Web of Conferences 336 (2021): 07006. http://dx.doi.org/10.1051/matecconf/202133607006.
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UAV needs sensor to fly in an environment with obstacles. However, UAV may not be able to move forward when it encounters a large obstacle, or UAV will be in a dangerous state when the sensor fails briefly which disturbed by the environment factors. In order to solve these problems, the following methods are proposed in this paper. Aiming at the first problem, this paper proposes an improved APF method for path planning, and verified by simulation experiments that this method can find the optimal path. Aiming at the second problem, this paper proposes a solution to expand the range of obstacles and dynamically change the distance in the APF repulsion function. It is verified that the UAV can fly safely within the short time of the sensor problem by simulation experiments. In conclusion, this paper has an important reference value for the application of UAV online dynamic path planning in engineering.
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Wang, Wentao, Chen Ye, and Jun Tian. "SGGTSO: A Spherical Vector-Based Optimization Algorithm for 3D UAV Path Planning." Drones 7, no. 7 (July 7, 2023): 452. http://dx.doi.org/10.3390/drones7070452.
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The application of 3D UAV path planning algorithms in smart cities and smart buildings can improve logistics efficiency, enhance emergency response capabilities as well as provide services such as indoor navigation, thus bringing more convenience and safety to people’s lives and work. The main idea of the 3D UAV path planning problem is how to plan to get an optimal flight path while ensuring that the UAV does not collide with obstacles during flight. This paper transforms the 3D UAV path planning problem into a multi-constrained optimization problem by formulating the path length cost function, the safety cost function, the flight altitude cost function and the smoothness cost function. This paper encodes each feasible flight path as a set of vectors consisting of magnitude, elevation and azimuth angles and searches for the optimal flight path in the configuration space by means of a metaheuristic algorithm. Subsequently, this paper proposes an improved tuna swarm optimization algorithm based on a sigmoid nonlinear weighting strategy, multi-subgroup Gaussian mutation operator and elite individual genetic strategy, called SGGTSO. Finally, the SGGTSO algorithm is compared with some other classical and novel metaheuristics in a 3D UAV path planning problem with nine different terrain scenarios and in the CEC2017 test function set. The comparison results show that the flight path planned by the SGGTSO algorithm significantly outperforms other comparison algorithms in nine different terrain scenarios, and the optimization performance of SGGTSO outperforms other comparison algorithms in 24 CEC2017 test functions.
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Zhang, Danyang, Xiongwei Li, Guoquan Ren, Jiangyi Yao, Kaiyan Chen, and Xi Li. "Three-Dimensional Path Planning of UAVs in a Complex Dynamic Environment Based on Environment Exploration Twin Delayed Deep Deterministic Policy Gradient." Symmetry 15, no. 7 (July 5, 2023): 1371. http://dx.doi.org/10.3390/sym15071371.
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Unmanned Aerial Vehicle (UAV) path planning research refers to the UAV automatically planning an optimal path to the destination under the corresponding environment, while avoiding collision with obstacles in this process. In order to solve the problem of 3D path planning of UAV in a dynamic environment, a heuristic dynamic reward function is designed to guide the UAV. We propose the Environment Exploration Twin Delayed Deep Deterministic Policy Gradient (EE-TD3) algorithm, which combines the symmetrical 3D environment exploration coding mechanism on the basis of TD3 algorithm. The EE-TD3 algorithm model can effectively avoid collisions, improve the training efficiency, and achieve faster convergence speed. Finally, the performance of the EE-TD3 algorithm and other deep reinforcement learning algorithms was tested in the simulation environment. The results show that the EE-TD3 algorithm is better than other algorithms in solving the 3D path planning problem of UAV.
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Zeng, Sha, and Kang Liu. "Research Status and Development Trend of UAV Path Planning Algorithms." Journal of Physics: Conference Series 2283, no. 1 (June 1, 2022): 012004. http://dx.doi.org/10.1088/1742-6596/2283/1/012004.
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Abstract This paper introduces the basic concepts of UAV track planning, the relationship between track planning and algorithms, and focuses on the analysis and induction of several commonly used algorithms at home and abroad in recent years. The advantages and disadvantages of current path planning algorithms in different application scenarios are summarized. Finally, the future development trend of UAV path planning algorithm is prospected, and it is pointed out that real-time online path planning algorithm research, multi-UAV swarm planning algorithm, perfect path planning problem model and algorithm hybrid optimization are the next research trends.
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Cao, Han. "Path Planning Approaches for Unmanned Aerial Vehicle." Highlights in Science, Engineering and Technology 76 (December 31, 2023): 146–52. http://dx.doi.org/10.54097/dc9y0s70.
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The difficulty of finding the ideal path from the starting point to the destination site for a UAV is one of the most essential challenges related with the deployment of unmanned aerial vehicle (UAV). Path planning algorithms are classified into traditional and intelligent algorithms in this article based on the order of discovery of the path planning methods. Intelligent algorithms are algorithms that are inspired by nature and can efficiently tackle the complex path planning problem. In this article, by introducing the different advantages of traditional algorithms and intelligent algorithms, it proposes employing intelligent algorithms to address the inefficiencies of traditional algorithms in uncertain conditions. The essay also outlines three classical intelligent algorithms and proposes optimization algorithms for their respective deficiencies. The article also discusses the objectives and constraints of UAV path planning. This analysis will help define the outcomes of UAV path planning and suggest the future research directions.
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Huang, Yanxi, Jiankang Xu, Mengting Shi, and Liang Liu. "Time-Efficient Coverage Path Planning for Energy-Constrained UAV." Wireless Communications and Mobile Computing 2022 (May 19, 2022): 1–15. http://dx.doi.org/10.1155/2022/5905809.
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Unmanned aerial vehicles (UAVs) have the characteristics of high mobility and wide coverage, making them widely used in coverage, search, and other fields. In these applications, UAV often has limited energy. Therefore, planning a time-efficient coverage path for energy-constrained UAV to cover the area of interest is the core issue. The existing coverage path planning algorithms assume that the UAV moves at a constant speed, without taking into account the cost of turns (including deceleration, turning, and acceleration), which is unrealistic. To solve the above problem, we propose a time-efficient coverage path planning (TECPP) algorithm for the energy-constrained UAV. We build a novel gadget-based graph model, which formalizes the time and energy costs of the flight path including straight flights and making turns (deceleration, turning, and acceleration). Moreover, our graph model is suitable for irregular-shaped areas with multiple obstacles. Finally, we transform the above problem into a generalized traveling salesman problem (GTSP) and use the generalized large neighborhood search (GLNS) solver to solve it. The experimental results show that TECPP outperforms the existing coverage path planning algorithms, and TECPP saves at least 21.6% of time.
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Song, Hui, Minghan Jia, Yihang Lian, Yijing Fan, and Keshan Liang. "UAV Path Planning Based on an Improved Ant Colony Algorithm." Journal of Electronic Research and Application 6, no. 2 (April 13, 2022): 10–25. http://dx.doi.org/10.26689/jera.v6i2.3808.
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Reviews and experimental verification have found that existing solution methods can be used to solve UAV path planning problems, but each approximate solution has its own advantages and disadvantages. For example, ant colony algorithm easily falls into the local optimum in the process of realizing path planning. In order to prevent too low pheromones on the longer path and too high pheromones in the shorter path, the upper and lower limits of pheromones as well as their volatile factors are set to avoid falling into the local optimum. Secondly, multi-heuristic factors are introduced, and the overall length of the path serves as an adaptive heuristic function factor that determines the probability of state transition, which affects the probability of ants choosing the corresponding path. The experimental results show that the path length planned by the improved algorithm is 93.6% of the original algorithm, and the optimal path length variance is only 14.22% of the original algorithm. The improved ant colony algorithm shortens the optimal path length and solves the UAV path planning problem in terms of local optima. At the same time, multiple enlightening factors are introduced to increase the suitability of UAV for complex environments and improve the performance of UAV.
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Ahmadi, S. M., H. Kebriaei, and H. Moradi. "Constrained coverage path planning: evolutionary and classical approaches." Robotica 36, no. 6 (February 21, 2018): 904–24. http://dx.doi.org/10.1017/s0263574718000139.
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SUMMARYThe constrained coverage path planning addressed in this paper refers to finding an optimal path traversed by a unmanned aerial vehicle (UAV) to maximize its coverage on a designated area, considering the time limit and the feasibility of the path. The UAV starts from its current position to assess the condition of a new entry to the area. Nevertheless, the UAV needs to comply with the coverage task, simultaneously and therefore, it is likely that the optimal policy would not be the shortest path in such a condition, since a wider area can be covered through a longer path. From the other side, along with a longer path, the UAV may not reach to the target in due time. In addition, the speed of UAV is assumed to be constant and as a result, a feasible path needs to be smooth enough to support this assumption. The problem is modeled as an Epsilon-constraint optimization in which a coverage function has to be maximized, considering the constraints on the length and the smoothness of the path. For this purpose, a new genetic path planning algorithm with adaptive operator selection is proposed to solve such a complicated constrained optimization problem. The proposed approach has been compared to some classical approaches like, a modified version of the Artificial Potential Field and a modified version of Dijkstra's algorithm (a graph-based approach). All the methods are implemented and tested in different scenarios and their performances are evaluated via the simulation results.
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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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Bagherian, M., and A. Alos. "3D UAV trajectory planning using evolutionary algorithms: A comparison study." Aeronautical Journal 119, no. 1220 (October 2015): 1271–85. http://dx.doi.org/10.1017/s0001924000011246.
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Abstract This paper focuses on the three dimensional flight path planning for an unmanned aerial vehicle (UAV) on a low altitude terrain following\terrain avoidance mission. The UAV trajectory planning problem is to compute an optimal or near-optimal trajectory for a UAV to do its mission objectives in a surviving penetration through the hostile enemy environment, considering the shape of the earth and the kinematics constraints of the UAV. Using the three dimensional terrain information, three dimensional flight path from a starting point to an end point, minimising a cost function and regarding the kinematics constraints of the UAV is calculated. The geographic information of the earth shape and enemy locations is generated using digital terrain model (DTM) and geographic information system (GIS), and is displayed in a 3D environment. Using 3D-maps containing the geographic data accompanied by DTM, and GIS, the problem is modelled by deriving the motion equations of the UAV. Two heuristic algorithms are proposed for this problem: genetic and particle swarm algorithms. Genetic and particle swarm algorithms are general purposes algorithms, because they can solve a wide range of problems, so they have to be adjusted to solve the trajectory planning problem. To test and compare the paths obtained from these algorithms, a software program is built using GIS tools and the programming languages C# and MATLAB.
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Aljalaud, Faten, Heba Kurdi, and Kamal Youcef-Toumi. "Bio-Inspired Multi-UAV Path Planning Heuristics: A Review." Mathematics 11, no. 10 (May 18, 2023): 2356. http://dx.doi.org/10.3390/math11102356.
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Despite the rapid advances in autonomous guidance and navigation techniques for unmanned aerial vehicle (UAV) systems, there are still many challenges in finding an optimal path planning algorithm that allows outlining a collision-free navigation route from the vehicle’s current position to a goal point. The challenges grow as the number of UAVs involved in the mission increases. Therefore, this work provides a comprehensive systematic review of the literature on the path planning algorithms for multi-UAV systems. In particular, the review focuses on biologically inspired (bio-inspired) algorithms due to their potential in overcoming the challenges associated with multi-UAV path planning problems. It presents a taxonomy for classifying existing algorithms and describes their evolution in the literature. The work offers a structured and accessible presentation of bio-inspired path planning algorithms for researchers in this subject, especially as no previous review exists with a similar scope. This classification is significant as it facilitates studying bio-inspired multi-UAV path planning algorithms under one framework, shows the main design features of the algorithms clearly to assist in a detailed comparison between them, understanding current research trends, and anticipating future directions. Our review showed that bio-inspired algorithms have a high potential to approach the multi-UAV path planning problem and identified challenges and future research directions that could help improve this dynamic research area.
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Li, Li, Hong Zhan, and Yongjing Hao. "The Online Path Planning Method of UAV Autonomous Inspection in Distribution Network." E3S Web of Conferences 256 (2021): 01047. http://dx.doi.org/10.1051/e3sconf/202125601047.
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In this paper, the problem of online path planning for autonomous inspection of distribution network lines by UAV is studied. Because the distribution lines are mostly distributed around cities, counties and mountainous areas, the lines and their surrounding environment are uncertain and dynamic. These factors will affect the safety of UAV inspection, making the off-line pre-planned path for UAV unavailable. This paper designs an improved iteration random tree algorithm (IRRT) algorithm, which can quickly plan the path of UAV in dynamic environment.
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Yu, Yang, and Sanghwan Lee. "Efficient Multi-UAV Path Planning for Collaborative Area Search Operations." Applied Sciences 13, no. 15 (July 28, 2023): 8728. http://dx.doi.org/10.3390/app13158728.
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Efficient UAV path-planning algorithms significantly improve inspection efficiency and reduce costs. However, due to the limitation of battery capacity, the endurance of existing UAVs is limited, making it difficult for them to directly undertake information collection, cruising, and inspection tasks over large work areas. This paper considers the problem of path allocation for multiple UAVs to minimize work time and reports research on multi-UAV (unmanned aerial vehicle) multi-task long-duration operation path planning. We propose a multi-UAV collaborative search algorithm based on the greedy algorithm (MUCS-GD) and a multi-UAV collaborative search algorithm based on the binary search algorithm (MUCS-BSAE), and later apply two UAV collaborative search algorithms to five UAV flight paths: (1) a snake curve path, (2) a “square wave signal” curve path, (3) a Peano curve path, (4) a Hilbert curve path, and (5) a Moore curve path, and compare the simulation results. We found that the performance of MUCS-BSAE was better than that of MUCS-GD in all of the above flight paths. In addition, the path with the “square wave signal” curve was the near-optimal path among all the flight paths. Finally, we improved the MUCS-BSAE applied on the “square wave signal” curve path and obtained an improved collaborative search algorithm for multiple UAVs based on the binary search algorithm (IMUCS-BSAE), which further reduced the working time of the UAV.
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Wang, Jingjing, Y. F. Zhang, L. Geng, J. Y. H. Fuh, and S. H. Teo. "A Heuristic Mission Planning Algorithm for Heterogeneous Tasks with Heterogeneous UAVs." Unmanned Systems 03, no. 03 (July 2015): 205–19. http://dx.doi.org/10.1142/s2301385015500132.
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This paper investigates the unmanned aerial vehicle (UAV)-mission planning problem (MPP) in which one needs to quickly find a good plan/schedule to carry out various tasks of different time windows at various locations using a fleet of fixed-winged heterogeneous UAVs. Such a realistic and complex UAV-MPP is decomposed into two sub-problems: flight path planning and task scheduling. A graph construction and search algorithm is developed for the flight path generation. For the task scheduling problem, a new hybrid algorithm based on heuristic has been proposed: (1) small-to-medium sized problem — heuristics for task assignment and all permutations for sequencing, and (2) large sized problem — heuristics for both task assignment and sequencing. The proposed algorithms have been implemented and tested. Numerical experimental results show that the proposed algorithm is very efficient and can effectively solve relatively big problems.
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Xiang, Fengtao, Keqin Chen, Jiongming Su, Hongfu Liu, and Wanpeng Zhang. "Penetration Planning and Design Method of Unmanned Aerial Vehicle Inspired by Biological Swarm Intelligence Algorithm." Wireless Communications and Mobile Computing 2021 (December 31, 2021): 1–13. http://dx.doi.org/10.1155/2021/4312592.
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Unmanned aerial vehicles (UAVs) are gradually used in logistics transportation. They are forbidden to fly in some airspace. To ensure the safety of UAVs, reasonable path planning and design is one of the key factors. Aiming at the problem of how to improve the success rate of unmanned aerial vehicle (UAV) maneuver penetration, a method of UAV penetration path planning and design is proposed. Ant colony algorithm has strong path planning ability in biological swarm intelligence algorithm. Based on the modeling of UAV planning and threat factors, improved ant colony algorithm is used for UAV penetration path planning and design. It is proposed that the path with the best pheromone content is used as the planning path. Some principles are given for using ant colony algorithm in UAV penetration path planning. By introducing heuristic information into the improved ant colony algorithm, the convergence is completed faster under the same number of iteratives. Compared with classical methods, the total steps reduced by 56% with 50 ant numbers and 200 iterations. 62% fewer steps to complete the first iteration. It is found that the optimal trajectory planned by the improved ant colony algorithm is smoother and the shortest path satisfying the constraints.
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Shen, Yong, Yunlou Zhu, Hongwei Kang, Xingping Sun, Qingyi Chen, and Da Wang. "UAV Path Planning Based on Multi-Stage Constraint Optimization." Drones 5, no. 4 (December 6, 2021): 144. http://dx.doi.org/10.3390/drones5040144.
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Evolutionary Algorithms (EAs) based Unmanned Aerial Vehicle (UAV) path planners have been extensively studied for their effectiveness and high concurrency. However, when there are many obstacles, the path can easily violate constraints during the evolutionary process. Even if a single waypoint causes a few constraint violations, the algorithm will discard these solutions. In this paper, path planning is constructed as a multi-objective optimization problem with constraints in a three-dimensional terrain scenario. To solve this problem in an effective way, this paper proposes an evolutionary algorithm based on multi-level constraint processing (ANSGA-III-PPS) to plan the shortest collision-free flight path of a gliding UAV. The proposed algorithm uses an adaptive constraint processing mechanism to improve different path constraints in a three-dimensional environment and uses an improved adaptive non-dominated sorting genetic algorithm (third edition—ANSGA-III) to enhance the algorithm’s path planning ability in a complex environment. The experimental results show that compared with the other four algorithms, ANSGA-III-PPS achieves the best solution performance. This not only validates the effect of the proposed algorithm, but also enriches and improves the research results of UAV path planning.
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Yu, Jiayang, Jiansheng Guo, Xiaofeng Zhang, Chuhan Zhou, and Tao Xie. "UAV Path Planning in Dynamical Environment: A Novel ICACO-IDWA Algorithm." Mathematical Problems in Engineering 2022 (December 17, 2022): 1–16. http://dx.doi.org/10.1155/2022/6802360.
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In this paper, a novel UAV path planning algorithm based on improved cellular ant colony algorithm and dynamic window algorithm (ICACO-IDWA) is proposed to solve the problem of dynamically changing threat during actual flight. The main innovations of this paper are as follows. (a) The hexagon grid method is proposed to model the UAV flight space, which solves the problem of inconsistent simulation time step. (b) A novel ICACO-IDWA algorithm is proposed. In the first stage, the optimal path is obtained by the improved cellular ant colony algorithm (ICACO). In the second stage, the improved dynamic window algorithm (IDWA) is used to optimize the optimal path considering dynamic threat. Through the algorithm, the UAV path planning with dynamic threat change is realized. Finally, simulation results verify the effectiveness of the proposed model and algorithm.
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Yu, Wangwang, Jun Liu, and Jie Zhou. "A Novel Sparrow Particle Swarm Algorithm (SPSA) for Unmanned Aerial Vehicle Path Planning." Scientific Programming 2021 (December 9, 2021): 1–15. http://dx.doi.org/10.1155/2021/5158304.
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Unmanned aerial vehicle (UAV) has been widely used in various fields, and meeting practical high-quality flight paths is one of the crucial functions of UAV. Many algorithms have the problem of too fast convergence and premature in UAV path planning. This study proposed a sparrow particle swarm algorithm for UAV path planning, the SPSA. The algorithm selects a suitable model for path initialization, changes the discoverer position update, and reinforces the influence of start-end line on path search, which can significantly reduce blind search. The number of target points reached is increased by adaptive variable speed escapes in areas of deadlock. In this case, the planned trajectory will fluctuate, and adaptive oscillation optimization can effectively reduce the fluctuation of the path. Finally, the optimal path is simplified, and the path nodes are interpolated with cubic splines to improve the smoothness of the path, which improves the smoothness of the UAV flight trajectory and makes it more suitable for use as the UAV real flight trajectory. By comparison, it can be concluded that the improved SPSA has good convergence speed and better search results and can avoid local optimality.
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Yang, Renjie, Pan Huang, Hui Gao, Qingyang Qin, Tao Guo, Yongchao Wang, and Yaoming Zhou. "A Photosensitivity-Enhanced Plant Growth Algorithm for UAV Path Planning." Biomimetics 9, no. 4 (March 31, 2024): 212. http://dx.doi.org/10.3390/biomimetics9040212.
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With the rise and development of autonomy and intelligence technologies, UAVs will have increasingly significant applications in the future. It is very important to solve the problem of low-altitude penetration of UAVs to protect national territorial security. Based on an S-57 electronic chart file, the land, island, and threat information for an actual combat environment is parsed, extracted, and rasterized to construct a marine combat environment for UAV flight simulation. To address the problem of path planning for low-altitude penetration in complex environments, a photosensitivity-enhanced plant growth algorithm (PEPG) is proposed. Based on the plant growth path planning algorithm (PGPP), the proposed algorithm improves upon the light intensity preprocessing and light intensity calculation methods. Moreover, the kinematic constraints of the UAV, such as the turning angle, are also considered. The planned path that meets the safety flight requirements of the UAV is smoother than that of the original algorithm, and the length is reduced by at least 8.2%. Finally, simulation tests are carried out with three common path planning algorithms, namely, A*, RRT, and GA. The results show that the PEPG algorithm is superior to the other three algorithms in terms of the path length and path quality, and the feasibility and safety of the path are verified via the autonomous tracking flight of a UAV.
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Zhang, Zhe, Jian Wu, Jiyang Dai, and Cheng He. "Rapid Penetration Path Planning Method for Stealth UAV in Complex Environment with BB Threats." International Journal of Aerospace Engineering 2020 (August 1, 2020): 1–15. http://dx.doi.org/10.1155/2020/8896357.
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This paper presents the flight penetration path planning algorithm in a complex environment with Bogie or Bandit (BB) threats for stealth unmanned aerial vehicle (UAV). The emergence of rigorous air defense radar net necessitates efficient flight path planning and replanning for stealth UAV concerning survivability and penetration ability. We propose the improved A-Star algorithm based on the multiple step search approach to deal with this uprising problem. The objective is to achieve rapid penetration path planning for stealth UAV in a complex environment. Firstly, the combination of single-base radar, dual-base radar, and BB threats is adopted to different threat scenarios which are closer to the real combat environment. Besides, the multistep search strategy, the prediction technique, and path planning algorithm are developed for stealth UAV to deal with BB threats and achieve the penetration path replanning in complex scenarios. Moreover, the attitude angle information is integrated into the flight path which can meet real flight requirements for stealth UAV. The theoretical analysis and numerical results prove the validity of our method.
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Wu, Runjia, Fangqing Gu, Hai-lin Liu, and Hongjian Shi. "UAV Path Planning Based on Multicritic-Delayed Deep Deterministic Policy Gradient." Wireless Communications and Mobile Computing 2022 (March 14, 2022): 1–12. http://dx.doi.org/10.1155/2022/9017079.
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Deep deterministic policy gradient (DDPG) algorithm is a reinforcement learning method, which has been widely used in UAV path planning. However, the critic network of DDPG is frequently updated in the training process. It leads to an inevitable overestimation problem and increases the training computational complexity. Therefore, this paper presents a multicritic-delayed DDPG method for solving the UAV path planning. It uses multicritic networks and delayed learning methods to reduce the overestimation problem of DDPG and adds noise to improve the robustness in the real environment. Moreover, a UAV mission platform is built to train and evaluate the effectiveness and robustness of the proposed method. Simulation results show that the proposed algorithm has a higher convergence speed, a better convergence effect, and stability. It indicates that UAV can learn more knowledge from the complex environment.
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Zhang, Jian, and Hailong Huang. "Occlusion-Aware UAV Path Planning for Reconnaissance and Surveillance." Drones 5, no. 3 (September 15, 2021): 98. http://dx.doi.org/10.3390/drones5030098.
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Unmanned Aerial Vehicles (UAVs) have become necessary tools for a wide range of activities including but not limited to real-time monitoring, surveillance, reconnaissance, border patrol, search and rescue, civilian, scientific and military missions, etc. Their advantage is unprecedented and irreplaceable, especially in environments dangerous to humans, for example, in radiation or pollution-exposed areas. Two path-planning algorithms for reconnaissance and surveillance are proposed in this paper, which ensures every point on the target ground area can be seen at least once in a complete surveillance circle. Moreover, the geometrically complex environments with occlusions are considered in our research. Compared with many existing methods, we decompose this problem into a waypoint-determination problem and an instance of the traveling-salesman problem.
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Wang, Lisong, Xiaoliang Zhang, Pingyu Deng, Jiexiang Kang, Zhongjie Gao, and Liang Liu. "An Energy-Balanced Path Planning Algorithm for Multiple Ferrying UAVs Based on GA." International Journal of Aerospace Engineering 2020 (August 14, 2020): 1–15. http://dx.doi.org/10.1155/2020/3516149.
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When performing a search and rescue mission, unmanned aerial vehicles (UAVs) should continuously search targets above the mission area. In order to transfer the search and rescue information quickly and efficiently, two types of UAVs, the ferrying UAVs and the searching UAVs, are used to complete the mission. Obviously, this application scenario requires an efficient path planning method for ferrying UAVs. The existing path planning methods for ferrying UAVs usually focus on shortening the path length and ignore the different initial energy of ferrying UAVs. However, the following problem does exist: if the ferrying UAV with less initial energy is assigned a longer path, meaning that the ferrying UAV with less initial energy will ferry messages for more searching UAVs. When the lower-initial-energy ferrying UAV is running out of energy, more searching UAVs will no longer deliver messages successfully. Therefore, the mismatch between the planned path length and the initial energy will eventually result in a lower global message delivery ratio. To solve this problem, we propose a new concept energy-factor for a ferrying UAV and use the variance of all ferrying UAVs’ energy-factor to measure the balance between the planned path length and the initial energy. Further, we model the energy-balanced path-planning problem for multiple ferrying UAVs, which actually is a multiobject optimization problem of minimizing the planned path length and minimizing the variance of all ferrying UAVs’ energy-factor. Based on the genetic algorithm, we design and implement an energy-balanced path planning algorithm (EMTSPA) for multiple ferrying UAVs to solve this multiobject optimization problem. Experimental results show that EMTSPA effectively increases the global message delivery ratio and decreases the global message delay.
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Liao, Chuanqi, and Zuoshi Liu. "Cooperative Path Planning of Ground-air Robots for Distributed Photovoltaic Inspection." Journal of Physics: Conference Series 2658, no. 1 (December 1, 2023): 012015. http://dx.doi.org/10.1088/1742-6596/2658/1/012015.
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Abstract Facing the Distributed PV generation inspection scenario, to overcome the low efficiency of traditional manual inspection and the insufficient endurance of existing UAV inspection, UAV aerial inspection and UGVs to provide energy supply in the road network are adopted, and a path planning method that prioritizes the meeting points is proposed. Firstly, the improved genetic algorithm is used to plan the inspection path of each UAV, then the UAV path is clustered to determine the optimal number of meeting points, then the adaptive particle swarm algorithm is used to find the best location of the meeting points in the road network, and finally, the patrol path with the meeting points is optimized twice. Through the simulation test of the standard data set and real data, the effectiveness of the proposed method in solving the path planning problem of distributed photovoltaic coordinated inspection by ground-to-air robots is verified.
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Mei, Haoran, and Limei Peng. "Energy-Efficient UAV Trajectory Planning based on Flexible Segment Clustering Algorithm." Journal of Networking and Network Applications 3, no. 3 (2023): 109–18. http://dx.doi.org/10.33969/j-nana.2023.030302.
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This paper plans the energy-efficient UAV trajectory when a UAV gathers data from massive IoT devices in a given area. The UAV trajectory design is addressed by two steps, i.e., IoT node clustering and UAV flight path planning for scanning the clusters, which are formulated as Cluster Minimization (CM) problem and Traveling Salesman Problem (TSP) in this work, respectively. The CM aims to contribute fewest clusters with minimal overlap to cover all the IoT devices and the per cluster size approaching the UAV communication coverage. On the other hand, the TSP seeks to design the shortest flight path to cover all the grouped clusters while minimizing energy consumption. Specifically, this work mainly focuses on the CM problem since the TSP issues have been well addressed in the past. In particular, we design a two-stage ILP optimization model to formulate the CM problem and propose two flexible clustering algorithms with low complexity, i.e., segment clustering (SC) and its variant, saying shifted SC (SSC). For the proposed ILP model and algorithms, we conduct extensive simulations under five different topologies and compare the performance results with existing methods. The simulation results indicate that the performance achieved by the proposed SSC algorithm is closest to the optimal results obtained from the ILP model. Moreover, it outperforms the existing methods under most topologies regarding cluster numbers, trajectory path length, and power consumption.
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YK, Guruprasad, and NageswaraGuptha M N. "Autonomous UAV object Avoidance with Floyd-warshall differential evolution approach." Inteligencia Artificial 25, no. 70 (December 5, 2022): 77–94. http://dx.doi.org/10.4114/intartif.vol25iss70pp77-94.
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Unmanned Aerial Vehicles (UAVs) are recently focused with significant research attention from commercial to military industries. Due to its wide range of applications such as traffic monitoring, surveillance, aerial photograph and rescue mission, many research studies were conducted related to UAV development. UAV are commonly called as ‘drones’ used to suit dull, dangerous and dirty missions that can be suited by manned aircraft. UAV can be controlled either remotely or using automation approaches so that it can be travelled into predefined path. To make the autonomous UAV, the most complex issue that is faced by UAV is obstacle / object avoidance. Obstacle detection and avoidance are important for UAV and it is the complex problem to solve due to the payload restriction. This will limit the sensor count mounted on the vehicle. Radar was used to find the distance between the object and vehicle. This can help to detect and track the moving objects speed and direction towards the vehicle. This paper considered the object avoidance problem as path planning problem. There were many path planning methods related to UAV which formulates the path planning as an optimization problem to avoid the obstacles. With the consideration, this paper proposed an efficient and optimal approach called Floyd Warshall- Differential evolution (FWDE) approach to detect the frontal obstacles of UAV. Finally, statistical analysis of the simulated environment reveals that the proposed evolutionary method can efficiently avoid both static and dynamic objects for UAVs. This efficient avoidance algorithm for UAV can be experimented with simulation environment with three kinds of scenarios having different number of cells. The obtained accuracy and recall value of the proposed system is 95.21% and 91.56%.
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Chen, Fankai, Qinyu Liu, Xiaohan Cong, Xiuhuan Dong, and Yuanyuan Zhang. "Three-dimensional path planning of UAV in complex urban environment." Frontiers in Computing and Intelligent Systems 3, no. 2 (April 13, 2023): 74–77. http://dx.doi.org/10.54097/fcis.v3i2.7514.
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Aiming at the three-dimensional path planning problem of UAV in complex urban environment, the improved grid method is used to simulate the flight environment, and the safety of path planning is improved by building a safe flight area and introducing a navigation safety cost function. In order to solve the problems of A* (A_Star) algorithm in path planning, such as large number of nodes, large amount of computation and low planning efficiency, we can reduce the redundant checking process in the path search process by expanding the line of sight strategy, improve the algorithm search efficiency and smooth the planned lines. Analyze the characteristics of each stage of path planning, adaptively adjust the weight factor, design the flight cost function, shorten the length of path planning, and improve the speed of path planning. As a result of the experiment, the improved A* algorithm clearly decreased over the time of the path planning, the number of nodes and the total cost of the flight, and it was clarified that the planned route was smoother and more feasible than the traditional algorithm. The performance of the improved algorithm has been fully verified under the complexity of different cities, which provides a reference for the research of UAV path planning in urban environment.
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Gao, Jianfeng, Yu Zheng, Kai Ni, Qiliang Mei, Bin Hao, and Long Zheng. "Fast Path Planning for Firefighting UAV Based on A-Star algorithm." Journal of Physics: Conference Series 2029, no. 1 (September 1, 2021): 012103. http://dx.doi.org/10.1088/1742-6596/2029/1/012103.
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Abstract When the long distance oil-gas pipeline accident occurs, the fire UAV can give priority to the place where the accident occurs. Emergency investigation and fire rescue, greatly reduces the harm of the accident. However, due to the limitation of the UAV navigation system, the UAV will accumulate the positioning errors over time in the flight process. If the positioning errors can not be corrected in time, it will make the UAV unable to reach the intended destination, thus leading to the failure of the rescue mission. In view of this phenomenon, we propose a UAV path planning scheme considering positioning errors. Taking the UAV’s total flight path as short as possible and errors correction points as few as possible, a multi-objective programming model is established considering errors correction constraints and path constraints. The Euler distance with coefficient is selected as the estimated cost function of A-Star algorithm, and the optimal flight path of UAV is quickly planned through heuristic search. Taking the data of a certain flight area as an example, we have simulation results showing that the A-Star algorithm can quickly and effectively solve the problem of UAV flight path planning considering positioning errors.
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Guo, Jing, Shuai Yang, Dongkun Lu, and Caixia Zhang. "Wireless communication system throughput maximization based on UAV path planning." Journal of Physics: Conference Series 2216, no. 1 (March 1, 2022): 012008. http://dx.doi.org/10.1088/1742-6596/2216/1/012008.
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Abstract This paper studies the path planning of unmanned aerial vehicle (UAV) in wireless information and energy transmission system of wireless sensor network. UAV transmits radio frequency energy to enable ground sensors, and the ground sensors transmit information to the UAV. In order to solve the non-convex optimization problem in path planning, a new alternate optimization framework is proposed to optimize the fly trajectory of the UAV and maximize the throughput of the entire system. The proposed framework firstly utlizes the lagrangian method to obtain relaxation solution without maximum speed constraint, then implements genetic algorithm and continuous convex optimization algorithm to obtain optimal trajectory and energy allocation strategy. Numerical simulations results show that the UAV-assisted wireless information and energy transmission system can realize self-powered sensor network and data collection in special environments, and improve the throughput of the sensor network system.
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Zhang, Ran, Sen Li, Yuanming Ding, Xutong Qin, and Qingyu Xia. "UAV Path Planning Algorithm Based on Improved Harris Hawks Optimization." Sensors 22, no. 14 (July 13, 2022): 5232. http://dx.doi.org/10.3390/s22145232.
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In the Unmanned Aerial Vehicle (UAV) system, finding a flight planning path with low cost and fast search speed is an important problem. However, in the complex three-dimensional (3D) flight environment, the planning effect of many algorithms is not ideal. In order to improve its performance, this paper proposes a UAV path planning algorithm based on improved Harris Hawks Optimization (HHO). A 3D mission space model and a flight path cost function are first established to transform the path planning problem into a multidimensional function optimization problem. HHO is then improved for path planning, where the Cauchy mutation strategy and adaptive weight are introduced in the exploration process in order to increase the population diversity, expand the search space and improve the search ability. In addition, in order to reduce the possibility of falling into local extremum, the Sine-cosine Algorithm (SCA) is used and its oscillation characteristics are considered to gradually converge to the optimal solution. The simulation results show that the proposed algorithm has high optimization accuracy, convergence speed and robustness, and it can generate a more optimized path planning result for UAVs.
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Zhang, Jiawei. "Analysis of UAV path planning effectiveness and evaluation index scores combined with urban logistics scenarios." Applied and Computational Engineering 9, no. 1 (September 25, 2023): 148–53. http://dx.doi.org/10.54254/2755-2721/9/20230068.
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Using logistics drones for distribution is an optional solution to the problems of saturated logistics industry, insufficient transportation capacity, and high work pressure on employees in real cities. In a realistic urban environment, it is of practical significance to establish a logistics drone path planning model to improve the efficiency of logistics drones by fully considering the multiple requirements of both logistics service parties, such as time and location, and aiming at minimizing transportation costs. Aiming at this problem, this paper analyzes and studies the path planning problem of unmanned aerial vehicles based on the factors that need to be considered in actual life logistics distribution. Firstly, the actual scenario requirements of logistics UAV applications are analyzed and a path planning algorithm for this scenario is enumerated. Subsequently, mathematical models are established from both path planning and customer satisfaction to assess the performance of logistics drones. Then, a comparative analysis of four existing classical path planning algorithms is conducted, and simulated annealing algorithm is used to optimize logistics delivery scenarios for path planning. Finally, combining the mathematical model and simulated annealing algorithm constructed in the previous article, logistics UAV simulation experiments are conducted in urban distribution scenarios and an ideal path planning model is obtained. The work of this article provides a certain reference basis for logistics companies in the application of logistics UAVs and the selection of path planning methods.
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Chen, Tuo, Feihong Dong, Hu Ye, Yun Wang, and Bin Wu. "Data Collection Mechanism for UAV-Assisted Cellular Network Based on PPO." Electronics 12, no. 6 (March 13, 2023): 1376. http://dx.doi.org/10.3390/electronics12061376.
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Unmanned aerial vehicles (UAVs) are increasingly gaining in application value in many fields because of their low cost, small size, high mobility and other advantages. In the scenario of traditional cellular networks, UAVs can be used as a kind of aerial mobile base station to collect information of edge users in time. Therefore, UAVs provide a promising communication tool for edge computing. However, due to the limited battery capacity, these may not be able to completely collect all the information. The path planning can ensure that the UAV collects as much data as possible under the limited flight distance, so it is very important to study the path planning of the UAV. In addition, due to the particularity of air-to-ground communication, the flying altitude of the UAV can have a crucial impact on the channel quality between the UAV and the user. As a mature technology, deep reinforcement learning (DRL) is an important algorithm in the field of machine learning which can be deployed in unknown environments. Deep reinforcement learning is applied to the data collection of UAV-assisted cellular networks, so that UAVs can find the best path planning and height joint optimization scheme, which ensures that UAVs can collect more information under the condition of limited energy consumption, save human and material resources as much as possible, and finally achieve higher application value. In this work, we transform the UAV path planning problem into an Markov decision process (MDP) problem. By applying the proximal policy optimization (PPO) algorithm, our proposed algorithm realizes the adaptive path planning of UAV. Simulations are conducted to verify the performance of the proposed scheme compared to the conventional scheme.
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Zhang, Changchun, Yifan Liu, and Chunhe Hu. "Path Planning with Time Windows for Multiple UAVs Based on Gray Wolf Algorithm." Biomimetics 7, no. 4 (December 3, 2022): 225. http://dx.doi.org/10.3390/biomimetics7040225.
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The Gray Wolf (GWO) algorithm aims to address the path planning problem of multiple UAVs, and the scene setting is mainly to avoid threats, meet the constraints of UAVs themselves and avoid obstacles between UAVs. The scene setting is relatively simple. To address such problems, the problem of time windows is considered in this paper, so that the UAV can arrive at the same time, and the Gray Wolf algorithm is used to optimize the problem. Finally, the experimental results verify that the proposed method can plan a safe flight path in the process of multi-UAV flight and reach the goal point at the same time. The mean error of flight time between UAVs of the GWO is 0.213, which is superior to PSO (0.382), AFO (0.315) and GA (0.825).
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Li, Sen, Ran Zhang, Yuanming Ding, Xutong Qin, Yajun Han, and Huiting Zhang. "Multi-UAV Path Planning Algorithm Based on BINN-HHO." Sensors 22, no. 24 (December 13, 2022): 9786. http://dx.doi.org/10.3390/s22249786.
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Multi-UAV (multiple unmanned aerial vehicles) flying in three-dimensional (3D) mountain environments suffer from low stability, long-planned path, and low dynamic obstacle avoidance efficiency. Spurred by these constraints, this paper proposes a multi-UAV path planning algorithm that consists of a bioinspired neural network and improved Harris hawks optimization with a periodic energy decline regulation mechanism (BINN-HHO) to solve the multi-UAV path planning problem in a 3D space. Specifically, in the procession of global path planning, an energy cycle decline mechanism is introduced into HHO and embed it into the energy function, which balances the algorithm’s multi-round dynamic iteration between global exploration and local search. Additionally, when the onboard sensors detect a dynamic obstacle during the flight, the improved BINN algorithm conducts a local path replanning for dynamic obstacle avoidance. Once the dynamic obstacles in the sensor detection area disappear, the local path planning is completed, and the UAV returns to the trajectory determined by the global planning. The simulation results show that the proposed Harris hawks algorithm has apparent superiorities in path planning and dynamic obstacle avoidance efficiency compared with the basic Harris hawks optimization, particle swarm optimization (PSO), and the sparrow search algorithm (SSA).
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Benzaid, Karima, Romain Marie, Noura Mansouri, and Ouiddad Labbani-Igbida. "Filtered Medial Surface Based Approach for 3D Collision-Free Path Planning Problem." Journal of Robotics 2018 (June 3, 2018): 1–9. http://dx.doi.org/10.1155/2018/4676720.
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This paper introduces an original 3D path planning approach for Unmanned Aerial Vehicle (UAV) applications. More specifically, the core idea is to generate a smooth and collision-free path with respect to the vehicle dimension. Given a 3D grid representation of the environment, the Generalized Voronoi Graph (GVG) is first approximated using a filtered medial surface (FMS) algorithm on the corresponding navigable space. Based on an efficient pruning criterion, the produced FMS excludes GVG portions corresponding to narrow passages unfitting safe UAV navigation constraints, and thus it defines a set of guaranteed safe trajectories within the environment. Given a set of starting and destination coordinates, an adapted A-star algorithm is then applied to compute the shortest path on the FMS. Finally, an optimization process ensures the smoothness of the final path by fitting a set of 3D Bézier curves to the initial path. For a comparative study, the A-star algorithm is applied directly on the input environment representation and relevant comparative criteria are defined to assert the proposed approach using simulation results.
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Xu, Yahao, Yiran Wei, Di Wang, Keyang Jiang, and Hongbin Deng. "Multi-UAV Path Planning in GPS and Communication Denial Environment." Sensors 23, no. 6 (March 10, 2023): 2997. http://dx.doi.org/10.3390/s23062997.
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This paper proposes a feature fusion algorithm for solving the path planning problem of multiple unmanned aerial vehicles (UAVs) using GPS and communication denial conditions. Due to the blockage of GPS and communication, UAVs cannot obtain the precise position of a target, which leads to the failure of path planning algorithms. This paper proposes a feature fusion proximal policy optimization (FF-PPO) algorithm based on deep reinforcement learning (DRL); the algorithm can fuse image recognition information with the original image, realizing the multi-UAV path planning algorithm without an accurate target location. In addition, the FF-PPO algorithm adopts an independent policy for multi-UAV communication denial environments, which enables the distributed control of UAVs such that multi-UAVs can realize the cooperative path planning task without communication. The success rate of our proposed algorithm can reach more than 90% in the multi-UAV cooperative path planning task. Finally, the feasibility of the algorithm is verified by simulations and hardware.
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Poudel, Sabitri, and Sangman Moh. "Hybrid Path Planning for Efficient Data Collection in UAV-Aided WSNs for Emergency Applications." Sensors 21, no. 8 (April 17, 2021): 2839. http://dx.doi.org/10.3390/s21082839.
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In unmanned aerial vehicle (UAV)-aided wireless sensor networks (UWSNs), a UAV is employed as a mobile sink to gather data from sensor nodes. Incorporating UAV helps prolong the network lifetime and avoid the energy-hole problem faced by sensor networks. In emergency applications, timely data collection from sensor nodes and transferal of the data to the base station (BS) is a prime requisite. The timely and safe path of UAV is one of the fundamental premises for effective UWSN operations. It is essential and challenging to identify a suitable path in an environment comprising various obstacles and to ensure that the path can efficiently reach the target point. This paper proposes a hybrid path planning (HPP) algorithm for efficient data collection by assuring the shortest collision-free path for UAV in emergency environments. In the proposed HPP scheme, the probabilistic roadmap (PRM) algorithm is used to design the shortest trajectory map and the optimized artificial bee colony (ABC) algorithm to improve different path constraints in a three-dimensional environment. Our simulation results show that the proposed HPP outperforms the PRM and conventional ABC schemes significantly in terms of flight time, energy consumption, convergence time, and flight path.
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Goje, Shubhankar. "Path Planning and Obstacle Avoidance for UAV in an Uncharted Environment." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (August 31, 2021): 2353–59. http://dx.doi.org/10.22214/ijraset.2021.37774.
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Abstract: The growing industry of unmanned aerial vehicles (UAV) requires an efficient and robust algorithm to decide the path of the UAV and avoid obstacles. The study of pathfinding algorithms is ongoing research not just useful in the domain of drones, but in other fields like video games (AI pathfinding), terrain traversal (mapped, unmapped, areal, underwater, land, etc.), and industries that require robots to deliver packages. This paper proposes a new pathfinding algorithm that aims to solve the problem of pathfinding in unknown 2-dimensional terrain. Based on a system of assumptions and using the help of a set of sensors aboard the UAV, the algorithm navigates the UAV from a start point to an endpoint while avoiding any shape or size of obstacles in between. To avoid multiple different types of “infinite loop” situations where the UAV gets stuck around an obstacle, a priority-based selector for intermediate destinations is created. The algorithm is found to work effectively when simulated in Gazebo on Robot Operating System (ROS). Keywords: Path Planning, UAV, Obstacle Avoidance, Drone Navigation, Obstacle Detection, Uncharted Environment.
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Xu, Haiqin, Xujian Yu, Jie Dong, and Ranfei Li. "An IPSO-SA algorithm based on feedback mechanism for smooth path planning of UAV." Journal of Physics: Conference Series 2216, no. 1 (March 1, 2022): 012003. http://dx.doi.org/10.1088/1742-6596/2216/1/012003.
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Abstract The traditional PSO algorithm has been widely used in the path planning of unmanned equipment, but its algorithm is faced with the problem of premature convergence, which is easy to fall into the local optimal solution. When the initial position is not suitable, it can’t even plan the path in complex environment. At the same time, its line trajectory does not meet the requirements of the path curvature of realistic unmanned equipment. To solve the above problems, this paper proposes a new path planning strategy for unmanned equipment, and designs a new path planning algorithm for unmanned aircraft by combining the improved particle swarm optimization (IPSO) algorithm with simulated annealing algorithm (SA). At the same time, it solves the two problems that PSO algorithm is easy to fall into local optimal solution and abandoning all possible feasible solution regions in the early stage because of an illegal path. The path planned by IPSO-SA algorithm is processed by cubic spline interpolation to solve the curvature problem of its motion path. In addition, in view of the problem that the initial point is not suitable in complex environment, this algorithm designs a feedback mechanism to correct the initial point in time. The effectiveness of the algorithm is verified by theoretical derivation and simulation experiments, that is, the algorithm only needs a small number of point sequences to plan the path of UAV in simple and complex environment, and has less search amount and space-time overhead.
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Li, Weimin, Lei Wang, Awei Zou, Jingcao Cai, Huijuan He, and Tielong Tan. "Path Planning for UAV Based on Improved PRM." Energies 15, no. 19 (October 3, 2022): 7267. http://dx.doi.org/10.3390/en15197267.
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In this paper, an improved probabilistic roadmap (IPRM) algorithm is proposed to solve the energy consumption problem of multi-unmanned aerial vehicle (UAV) path planning with an angle. Firstly, in order to simulate the real terrain environment, a mathematical model was established; secondly, an energy consumption model was established; then, the sampling space of the probabilistic roadmap (PRM) algorithm was optimized to make the obtained path more explicit and improve the utilization rate in space and time; then, the sampling third-order B-spline curve method was used to curve the rotation angle to make the path smoother and the distance shorter. Finally, the results of the improved genetic algorithm (IGA), PRM algorithm and IPRM algorithm were compared through a simulation. The data analysis shows that the IGA has significant advantages over other algorithms in some aspects, and can be well applied to the path planning of UAVs.
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Silva Arantes, Jesimar da, Márcio da Silva Arantes, Claudio Fabiano Motta Toledo, Onofre Trindade Júnior, and Brian Charles Williams. "Heuristic and Genetic Algorithm Approaches for UAV Path Planning under Critical Situation." International Journal on Artificial Intelligence Tools 26, no. 01 (February 2017): 1760008. http://dx.doi.org/10.1142/s0218213017600089.
Full textAbstract:
The present paper applies a heuristic and genetic algorithms approaches to the path planning problem for Unmanned Aerial Vehicles (UAVs), during an emergency landing, without putting at risk people and properties. The path re-planning can be caused by critical situations such as equipment failures or extreme environmental events, which lead the current UAV mission to be aborted by executing an emergency landing. This path planning problem is introduced through a mathematical formulation, where all problem constraints are properly described. Planner algorithms must define a new path to land the UAV following problem constraints. Three path planning approaches are introduced: greedy heuristic, genetic algorithm and multi-population genetic algorithm. The greedy heuristic aims at quickly find feasible paths, while the genetic algorithms are able to return better quality solutions within a reasonable computational time. These methods are evaluated over a large set of scenarios with different levels of diffculty. Simulations are also conducted by using FlightGear simulator, where the UAV’s behaviour is evaluated for different wind velocities and wind directions. Statistical analysis reveal that combining the greedy heuristic with the genetic algorithms is a good strategy for this problem.
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Xia, Shuang, and Xiangyin Zhang. "Constrained Path Planning for Unmanned Aerial Vehicle in 3D Terrain Using Modified Multi-Objective Particle Swarm Optimization." Actuators 10, no. 10 (September 29, 2021): 255. http://dx.doi.org/10.3390/act10100255.
Full textAbstract:
This paper considered the constrained unmanned aerial vehicle (UAV) path planning problem as the multi-objective optimization problem, in which both costs and constraints are treated as the objective functions. A novel multi-objective particle swarm optimization algorithm based on the Gaussian distribution and the Q-Learning technique (GMOPSO-QL) is proposed and applied to determine the feasible and optimal path for UAV. In GMOPSO-QL, the Gaussian distribution based updating operator is adopted to generate new particles, and the exploration and exploitation modes are introduced to enhance population diversity and convergence speed, respectively. Moreover, the Q-Learning based mode selection logic is introduced to balance the global search with the local search in the evolution process. Simulation results indicate that our proposed GMOPSO-QL can deal with the constrained UAV path planning problem and is superior to existing optimization algorithms in terms of efficiency and robustness.