Evolutionary route planner for unmanned air vehicles

Chen, Zheng; Li, Lei; Fei, Xu; Sun, Fei; Ding, Mingyue

doi:10.1109/tro.2005.844684

Cited by 240 publications

(39 citation statements)

References 25 publications

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“…The study includes the most comprehensive set of requirements including the ones overlooked in a wide range of studies. This study extends existing literature 1,3,20,21,[32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51] by placing a special importance on aviation rules and utilisation considerations. The model generates suitable paths that address UAV performance limitations, environmental factors, basic aviation rules, flight dynamics, UAV utilisation considerations and user requirements.…”

Section: Introductionsupporting

confidence: 73%

“…A complex dynamic environment is the one that includes many constraints with different characteristics and various mission, flight, and environment related requirements. Even though, studies in autonomous path planning 1,3,20,21,[32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51] address many challenges, domainspecific operational issues, such as utilisation considerations and aviation rules, did not receive adequate attention. This is important if the UAV under study is a medium altitude high endurance (MALE) UAV, because these types of UAVs are subject to aviation rules in addition to other criteria.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, decomposition methods are also used to reduce the number of samples in the generated graph by dividing the world space into regions 30,31 . UAV path planning studies addresses multiple flight objectives under several flight criteria 12,20,21,[32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51] . In this study, we try to address an extensive set of criteria (see Table 1) in online path planning by implementing multiple constraints to provide more effective and safer UAV paths.…”

mentioning

confidence: 99%

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Comparison of 3D Versus 4D Path Planning for Unmanned Aerial Vehicles

2016

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This research compares 3D versus 4D (three spatial dimensions and the time dimension) multi-objective and multi-criteria path-planning for unmanned aerial vehicles in complex dynamic environments. In this study, we empirically analyse the performances of 3D and 4D path planning approaches. Using the empirical data, we show that the 4D approach is superior over the 3D approach especially in complex dynamic environments. The research model consisting of flight objectives and criteria is developed based on interviews with an experienced military UAV pilot and mission planner to establish realism and relevancy in unmanned aerial vehicle flight planning. Furthermore, this study incorporates one of the most comprehensive set of criteria identified during our literature search. The simulation results clearly show that the 4D path planning approach is able to provide solutions in complex dynamic environments in which the 3D approach could not find a solution.Keywords: Unmanned aerial vehicles, UAV, path planning, modelling, simulation, 3D path planning, 4D path planning Vol. 66, No. 6, November 2016, pp. 651-664, DOI : 10.14429/dsj.66 , VOL. 66, NO. 6, NOVEMbER 2016 652 model used to create flight paths in 4D environment. The simulation model is more applicable and realistic compared to other studies examined during the literature review. The study includes the most comprehensive set of requirements including the ones overlooked in a wide range of studies. This study extends existing literature 1,3,20,21,[32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51] by placing a special importance on aviation rules and utilisation considerations. The model generates suitable paths that address UAV performance limitations, environmental factors, basic aviation rules, flight dynamics, UAV utilisation considerations and user requirements. It helps in online and offline planning of optimal paths based on distance, fuel consumption, or time objectives, while implementing the described flight criteria. To build the simulation model (SM), we first built a conceptual model (CM) to structure the problem 68 . In the simulations, for each objective, various scenarios are created by changing the number of static and dynamic obstacles and target types. In each scenario, the path planning approach found the shortest and least costly flight paths. The path search is performed by A* heuristic algorithm proven to be complete and optimal. Additionally, in the experiments, A* algorithms with different heuristic parameters are compared using various scenarios in static and dynamic environments under different constraints. Since current UAVs have to make predictions and estimations about the possible future locations of mobile objects, the simulations also include path searches in time varying environments represented with a 4D grid. The 4D grid is constructed using a combination of 3D grids. Each 3D grid is a possible configuration of the world space at a specific time. A time-dimensional search space consider...

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Section: Introductionsupporting

confidence: 73%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Comparison of 3D Versus 4D Path Planning for Unmanned Aerial Vehicles

2016

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“…The current methods first use preflight defined waypoints (WPs) that are set according to the given mission [1][2][3][4][5][6][7]. The UAVs mission may be specified for reconnaissance or implementation of some tasks [2,3], threat avoidance [1,4,5], or obstacle avoidance [8][9][10][11]. Second, the path is further refined to make it flyable by using the curves [3][4][5][6][7][11][12][13][14][15][16][17].…”

mentioning

confidence: 99%

“…For example, solution methods including dynamic programming [18] still require fairly powerful computers, and they may not be suitable for real-time operation. [10,11] describe the evolutionary algorithm (EA) approach to solve the path planning problem. The EA-based algorithms have disadvantages in online applications due to their slow behavior.…”

mentioning

confidence: 99%

Three-Dimensional Curvature-Constrained Trajectory Planning Based on In-Flight Waypoints

Babaei

Mortazavi

2010

Journal of Aircraft

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This paper proposes an efficient algorithm with a novel procedure for trajectory planning of unmanned aerial vehicles in three-dimensional space. This work has been motivated by a challenge to develop a fast trajectory planning algorithm for autonomous unmanned aerial vehicles through the midcourse waypoints. The waypoints that are defined as a preflight or in-flight procedure are described in a five-dimensional configuration: the position in three dimensions plus desired crossing heading and flight-path angles. For achieving the waypoints, the Dubins path is extended to three-dimensional applications by using the geometrical concepts. In addition, the trajectory planning algorithm is represented as a set of ordinary differential equations called optimal-constrained-trajectory kinematics by applying the differential geometry concepts. Optimal-constrained-trajectory kinematic is a closed-loop guidance law that generates the guidance commands based on the waypoint configuration and minimum turning radius and is solved in a real-time manner. The proposed algorithm includes an operational framework that leads to gradually generating the smooth three-dimensional trajectory, aimed at reaching the midcourse targets and final target so that they are smoothly connected to each other. Finally, the simulation results show the capability of the algorithm in dynamic trajectory planning in low computational burden. = index for variables defined in the P coordinate system t = index for variables defined in the local coordinate system 0 = switching index from the T plane to the P plane 1 = first switching index in the P plane 2 = second switching index in the P plane

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Obstacle Avoidance: Static Obstacles

Shanmugavel

Tsourdos

White

2010

Encyclopedia of Aerospace Engineering

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This article presents an overview of obstacle avoidance of static obstacles of autonomous vehicles. Two methods are discussed to handle the known and unknown obstacles. A brief introduction of road map methods are discussed which are used to avoid static obstacles which are known a priori. The pop‐up or unknown obstacles are handled by reactive method such as potential field and by replanning the paths. These methods avoid the obstacles by changing the original path produced by path planning. Relevant research on the obstacle avoidance is presented where optimization and other approaches are discussed.

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Evolutionary route planner for unmanned air vehicles

Cited by 240 publications

References 25 publications

Comparison of 3D Versus 4D Path Planning for Unmanned Aerial Vehicles

Comparison of 3D Versus 4D Path Planning for Unmanned Aerial Vehicles

Three-Dimensional Curvature-Constrained Trajectory Planning Based on In-Flight Waypoints

Obstacle Avoidance: Static Obstacles

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