无人飞行器航迹规划的研究
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摘要
无人飞行器航迹规划就是在特定约束条件下,寻找满足无人飞行器机动性能及战场环境限制的,从出发点到目标点的最优飞行轨迹,是无人飞行器进行自主飞行的关键技术。本论文针对飞行器航迹规划的三个不同任务:静态规划、动态规划以及多飞行器协同规划,围绕飞行器航迹自动搜索方法进行了深入的研究,主要包括以下内容: (1)规划任务建模;(2)飞行器静态航迹规划;(3)飞行器动态航迹规划;(4)多飞行器协同航迹规划。
对于规划任务建模问题,作者进行了三个方面的研究:首先,根据航迹规划的特点,给出了规划空间和航迹的一般表示方法;其次,对于航迹生成中需要考虑的威胁和航迹长度,飞行时间等问题,给出了计算方便,较符合实际的代价函数表示方法;最后给出了基于极坐标的航迹点表示的新方法,该方法具有一些比用直角坐标方式优越的特性。
在静态航迹规划方面,作者提出了一种基于启发式遗传算法的航迹规划(Heuristic Genetic Algorithm Route Planning,HGARP)方法。该算法利用新的基于极坐标的航迹表示,把约束条件结合到航迹点的初始化及航迹迭代过程中。这样既可以有效地缩小搜索空间,又能保证生成的航迹为可行航迹,而不需要对规划出的航迹进行光顺处理。同时算法能够根据规划空间的特点采用变步长的思想,提高搜索效率。实验表明,该算法能在较短的时间内生成最优的飞行航迹。
动态航迹规划的研究采用边飞行边规划的思想,分两阶段进行规划。先规划出一条粗略航迹用作“航标”,再利用这些航标进行精细航迹规划。在精细规划时,粗略航迹点作为局部目标点,能够有效地缩小搜索范围,提高搜索效率。据此,本文结合设计了一种基于启发式遗传算法的实时航迹规划方法(Real Time-HGARP,RT-HGARP)。
最后论文针对多飞行器的协调航迹规划问题展开研究。针对多无人飞行器航迹的同步性和免碰撞的特点,引入多种群协同进化的概念,将各个无人飞行器看作不同的物种,各自独立进化;需要的航迹组的选取则是先选取个子种群中的优秀个体,再将其进行组合,然后通过一个协同代价来评价,最后得到最佳的航迹组。根据这一思路,本文设计了一种基于启发式遗传算法的多无人飞行器航迹规划方法(HGAMRP)。
The UAV(Unmanned Aerial Vehicle)route planning is to search the optimal fight route to satisfy the UAV machine performance and battlefield environment under special restrict condition. And the UAV route planning is the key technology guarantee that UAV can flight by itself. In this thesis, three different tasks of the route planning are discussed: static route planning, dynamic route planning and multi-UAVs cooperative route planning. According to automatic search methods of UAV route, the thesis addressed the following problems: (1) Modeling of mission planning; (2) Static route planning of UAV; (3) Dynamic route planning of UAV; (4) Cooperative route planning of multi-UAVs.
In the modeling of route planning, three works are conducted: Firstly, according to the characteristics of route planning, a representation of the planning space and flight route is proposed; Secondly, a representation that is convenience of calculation and realistic of the cost function is established, which maximizes the survival probability of the vehicle, minimizes route length and time of flight; lastly, a new representation of route point which is based on Polar coordinates is given, and the method has better features than that based on Cartesian coordinates.
In the static route planning of UAV, a new planner that is based on Heuristic Genetic Algorithm Route Planning (HGARP) is proposed. In the algorithm, the representation of the route is based on polar coordinates, and in the process of the route initialization and iteration, the constraints of the UAV are considered. In this way, the search space can be reduced effectively and without smoothing process, the route that is generated is feasible route. In addition, to improve the search efficiency, an idea of variable step length according to the search space’s environment is used. The experiences show that the algorithm can generate an optimal route in a short time.
In dynamic route planning of UAV, an idea of planning during flight is adopted. The route planner is a two-stage plan: first a rough route is planned as a“beacon”, and then a fine route is planned by using of this beacon. In the stage of fine route planning, the rough route points are used as local target points, which can reduce the search space and improve search efficiency. Accordingly, a heuristic algorithm based route planning in real time (RT-HGARP) is designed.
At last, the coordination route planning of multi-UAVs is discussed. In connecting with the synchronization and collision-free of the multi-UAVS route, the concept of multispecies co-evolution is introduced. In the algorithm, every UAV is regarded as a different species, and evolves only within its own sub-population. The selection of the UAVs group’s routes is to select outstanding individuals of every sub-population firstly, then to evaluate the co-cost of the combination of the routes, and lastly, to choose the best route group.According to this idea, a heuristic genetic algorithm based multi-UAVs route planning (HGAMRP) is designed.
对于规划任务建模问题,作者进行了三个方面的研究:首先,根据航迹规划的特点,给出了规划空间和航迹的一般表示方法;其次,对于航迹生成中需要考虑的威胁和航迹长度,飞行时间等问题,给出了计算方便,较符合实际的代价函数表示方法;最后给出了基于极坐标的航迹点表示的新方法,该方法具有一些比用直角坐标方式优越的特性。
在静态航迹规划方面,作者提出了一种基于启发式遗传算法的航迹规划(Heuristic Genetic Algorithm Route Planning,HGARP)方法。该算法利用新的基于极坐标的航迹表示,把约束条件结合到航迹点的初始化及航迹迭代过程中。这样既可以有效地缩小搜索空间,又能保证生成的航迹为可行航迹,而不需要对规划出的航迹进行光顺处理。同时算法能够根据规划空间的特点采用变步长的思想,提高搜索效率。实验表明,该算法能在较短的时间内生成最优的飞行航迹。
动态航迹规划的研究采用边飞行边规划的思想,分两阶段进行规划。先规划出一条粗略航迹用作“航标”,再利用这些航标进行精细航迹规划。在精细规划时,粗略航迹点作为局部目标点,能够有效地缩小搜索范围,提高搜索效率。据此,本文结合设计了一种基于启发式遗传算法的实时航迹规划方法(Real Time-HGARP,RT-HGARP)。
最后论文针对多飞行器的协调航迹规划问题展开研究。针对多无人飞行器航迹的同步性和免碰撞的特点,引入多种群协同进化的概念,将各个无人飞行器看作不同的物种,各自独立进化;需要的航迹组的选取则是先选取个子种群中的优秀个体,再将其进行组合,然后通过一个协同代价来评价,最后得到最佳的航迹组。根据这一思路,本文设计了一种基于启发式遗传算法的多无人飞行器航迹规划方法(HGAMRP)。
The UAV(Unmanned Aerial Vehicle)route planning is to search the optimal fight route to satisfy the UAV machine performance and battlefield environment under special restrict condition. And the UAV route planning is the key technology guarantee that UAV can flight by itself. In this thesis, three different tasks of the route planning are discussed: static route planning, dynamic route planning and multi-UAVs cooperative route planning. According to automatic search methods of UAV route, the thesis addressed the following problems: (1) Modeling of mission planning; (2) Static route planning of UAV; (3) Dynamic route planning of UAV; (4) Cooperative route planning of multi-UAVs.
In the modeling of route planning, three works are conducted: Firstly, according to the characteristics of route planning, a representation of the planning space and flight route is proposed; Secondly, a representation that is convenience of calculation and realistic of the cost function is established, which maximizes the survival probability of the vehicle, minimizes route length and time of flight; lastly, a new representation of route point which is based on Polar coordinates is given, and the method has better features than that based on Cartesian coordinates.
In the static route planning of UAV, a new planner that is based on Heuristic Genetic Algorithm Route Planning (HGARP) is proposed. In the algorithm, the representation of the route is based on polar coordinates, and in the process of the route initialization and iteration, the constraints of the UAV are considered. In this way, the search space can be reduced effectively and without smoothing process, the route that is generated is feasible route. In addition, to improve the search efficiency, an idea of variable step length according to the search space’s environment is used. The experiences show that the algorithm can generate an optimal route in a short time.
In dynamic route planning of UAV, an idea of planning during flight is adopted. The route planner is a two-stage plan: first a rough route is planned as a“beacon”, and then a fine route is planned by using of this beacon. In the stage of fine route planning, the rough route points are used as local target points, which can reduce the search space and improve search efficiency. Accordingly, a heuristic algorithm based route planning in real time (RT-HGARP) is designed.
At last, the coordination route planning of multi-UAVs is discussed. In connecting with the synchronization and collision-free of the multi-UAVS route, the concept of multispecies co-evolution is introduced. In the algorithm, every UAV is regarded as a different species, and evolves only within its own sub-population. The selection of the UAVs group’s routes is to select outstanding individuals of every sub-population firstly, then to evaluate the co-cost of the combination of the routes, and lastly, to choose the best route group.According to this idea, a heuristic genetic algorithm based multi-UAVs route planning (HGAMRP) is designed.
引文
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[2] T. Samad, J. S. Bay, and D. Godbole. Network-centric systems for military operations in urban terrain: The role of UAVs. Proc. IEEE, Jan. 2007, vol. 95, no. 1, pp. 92–107.
[3] J. A. Goldman. Path planning problems and solutions. Proc. Nat. Aerosp. Electron. Conf., 1994, pp. 105–108.
[4] Eva Besada-Portas, Luis de la Torre, Jes′us M. de la Cruz, and Bonifacio de Andr′es-Toro. Evolutionary Trajectory Planner for Multiple UAVs in Realistic Scenarios. IEEE Transactions on Robotics, Vol. 26, No. 4, August 2010.
[5]闵昌文,袁建平.军用飞行器航迹规划综述.飞行力学, 1998, Vol. 16(4), 14-19.
[6]程龙,陈昌金,张健康,黄俊.一支新兴的空中力量——无人作战飞机.飞航导弹. 2005, (5):33-37.
[7] Myers, David J. Calculating Flight Time for Unmanned Aerial Vehicles in the Presence of Obstacles and the Incorporation of Flight Dynamics [M.S. Thesis]. Department of Industrial & Systems Engineering, University at Bualo (State University of New York), Bualo, NY, USA. July, 2010.
[8] Andres-Toro, B., Besada-Portas, E., Fernandez-Blanco, P., Lopez Orozco, J.A., and de la Cruz, J.M. 2002. Multiobjective optimization of dynamic processes by evolutionary algorithms. In Proceeding of the 15thTriennial World Congress of the IFAC. (Barcelona, Spain, July 2002)
[9] J. Bellingham. Coordination and control of UAV ?eets using mixed-integer linear programming, [Ph.D. dissertation]. Mass. Inst. Technol., Cambridge, MA, 2002.
[10] Y. Kuwata and J. P. How. Three dimensional receding horizon control for UAVs. the AIAA Guid., Navigat., Control Conf. Exhib., AIAA, Monterey, CA, 2002.
[11] A. Richards and J. P. How. Aircraft trajectory planning with collision avoidance using mixed-integer linear programming. Proc. Amer. Control Conf., 2002, pp. 1936–1941.
[12] J.J.Ruz,O.Ar′evalo, J. M. de la Cruz, and G. Pajares. Using MILP for UAVs trajectory optimization under radar detection risk. Proc. 11th, IEEE Int. Conf. Emerging Technol. Factory Autom., 2006, pp. 1–4.
[13] P. Melchior, B. Orsoni, O. Lavialle, A. Poty, and A. Oustaloup. Con-sideration of obstacledanger level in path planning using A* and fast-marching optimisation: Comparative study. Signal Process. vol. 83, No. 11, pp. 2387–2396, 2003.
[14] R. J. Szczerba, P. Galkowski, I. Glickstein, and N. Ternullo. Robust algorithm for real-time route planning. IEEE Trans. Aerosp. Electron.Syst., vol. 36, no. 3, pp. 869–878, Jul. 2000.
[15] K. Trovato. A* planning in discrete configuration spaces of autonomoussystems [Ph.D. dissertation]. Amsterdam Univ., Amsterdam, The Netherlands, 1996.
[16] Y. Qu, Q. Pan, and J. Yan. Flight path planning of UAV based on heuristically search and genetic algorithms. Proc. 31st Annu. Conf. IEEE Ind.Electron. Soc., Nov. 2005, p. 5.
[17] A. Raghunathan, V. Gopal, D. Subramanian, L. T. Biegler, and T. Samad. Dynamic optimization strategies for 3d con?ict resolution of multiple aircraft. AIAA J. Guid., Control Dyn., vol. 27, no. 4, pp. 586–594, Jul.-Aug. 2004.
[18] S. Mittal and K. Deb. Three-dimensional of?ine path planning for UAVs using multiobjective evolutionary algorithms. Proc. IEEE Congr. Evol.Comput., 2007, vol. 7, pp. 3195–3202.
[19] I. K. Nikolos, K. P. Valavanis, N. C. Tsourveloudis, and A. N. Kostaras. Evolutionary algorithm based of?ine/online path planner for UAV navigation. IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 33, no. 6, pp. 898–912, Dec. 2003.
[20] I. Hasircioglu, H. R. Topcuoglu, and M. Ermis. 3-d path planning for the navigation of unmanned aerial vehicles by using evolutionary algorithms. in Proc. Genet. Evol. Comput. Conf., 2008, pp. 1499–1506.
[21] Y. V. Pehlivanoglu, O. Baysal, and A. Hacioglu. Vibrational geneticalgorithm based path planner for autonomous UAV in spatial data based environments. Proc. 3rd Int. Conf. Recent Adv. Space Technol., 2007, vol. 7, pp. 573–578.
[22] I. K. Nikolos, N. C. Tsourveloudis, and K. P. Valavanis. Evolutionary algorithm based path planning for multiple UAV cooperation. Advancesin Unmanned Aerial Vehicles. Berlin, Germany: Springer-Verlag, Jan.2007, pp. 309–340.
[23] I. K. Nikolos and N. C. Tsourvelouds. Path planning for cooperating unmanned vehicles over 3-d terrain. Inf. Control, Autom. Robot., vol. 24, pp. 153–168, Jan. 2009.
[24]郑昌文.无人机航路规划方法研究[博士论文].华中科技大学, 2003.
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