An Improved Heuristic Algorithm for UCAV Path Planning

The paper presents the concept of planning the optimal trajectory of fixed-wing unmanned aerial vehicle (UAV) of a short-range tactical class, whose task is to recognize a set of ground objects as a part of a reconnaissance mission. Tasks carried out by such systems are mainly associated with an aerial reconnaissance using Electro-Optical/Infrared (EO/IR) systems and Synthetic Aperture Radars (SARs) to support military operations. Execution of a professional reconnaissance of the indicated objects requires determining the UAV flight trajectory in the close neighborhood of the target, in order to collect as much interesting information as possible. The paper describes the algorithm for determining UAV flight trajectories, which is tasked with identifying the indicated objectives using the sensors specified in the order. The presence of UAV threatening objects is taken into account. The task of determining the UAV flight trajectory for recognition of the target is a component of the planning process of the tactical class UAV mission, which is also presented in the article. The problem of determining the optimal UAV trajectory has been decomposed into several subproblems: determining the reconnaissance flight method in the vicinity of the currently recognized target depending on the sensor used and the required parameters of the recognition product (photo, film, or SAR scan), determining the initial possible flight trajectory that takes into account potential UAV threats, and planning detailed flight trajectory considering the parameters of the air platform based on the maneuver planning algorithm designed for tactical class platforms. UAV route planning algorithms with time constraints imposed on the implementation of individual tasks were used to solve the task of determining UAV flight trajectories. The problem was formulated in the form of a Mixed Integer Linear Problem (MILP) model. For determining the flight path in the neighborhood of the target, the optimal control algorithm was also presented in the form of a MILP model. The determined trajectory is then corrected based on the construction algorithm for determining real UAV flight segments based on Dubin curves.

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“…The method presented by Zhang et al. [ 27 ] is also worth noting. This paper presents an improved heuristic algorithm based on Sparse

Search for UAV path planning problem.…”

Section: Uav Flight Planning Procedures Using Sensors To Recognize mentioning

confidence: 99%

Determining UAV Flight Trajectory for Target Recognition Using EO/IR and SAR

Stecz

Gromada

2020

Sensors

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“…Zhang Kun et al [80] proposed an improved heuristic algorithm based on SAS considering the UAV flight constraints to find a feasible path efficiently. Sikha Hota and Debasish Ghose [81] proposed a geometric approach for optimal path planning in 3D space considering UAV minimum turn radius.…”

Section: Background and Related Workmentioning

confidence: 99%

A Fast Global Flight Path Planning Algorithm Based on Space Circumscription and Sparse Visibility Graph for Unmanned Aerial Vehicle

Majeed

Lee

2018

Electronics

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This paper proposes a new flight path planning algorithm that finds collision-free, optimal/near-optimal and flyable paths for unmanned aerial vehicles (UAVs) in three-dimensional (3D) environments with fixed obstacles. The proposed algorithm significantly reduces pathfinding computing time without significantly degrading path lengths by using space circumscription and a sparse visibility graph in the pathfinding process. We devise a novel method by exploiting the information about obstacle geometry to circumscribe the search space in the form of a half cylinder from which a working path for UAV can be computed without sacrificing the guarantees on near-optimality and speed. Furthermore, we generate a sparse visibility graph from the circumscribed space and find the initial path, which is subsequently optimized. The proposed algorithm effectively resolves the efficiency and optimality trade-off by searching the path only from the high priority circumscribed space of a map. The simulation results obtained from various maps, and comparison with the existing methods show the effectiveness of the proposed algorithm and verify the aforementioned claims.

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“…(1) Based on searching: Dijkstra algorithm [4] and A * algorithm [5] (2) Based on probability: rapidly exploring Random Trees (RRT) [6] and rapidly exploring Random Trees * (RRT * ) [7] (3) Based on metaheuristic algorithm: particle swarm optimization (PSO) [8] In this study, a metaheuristic algorithm is employed to optimize the robot path planning. Compared with other algorithms, a metaheuristic algorithm can achieve a stable convergence and avoid trapping into a local optimal solution, especially when facing a complex environment.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Parallel Arithmetic Optimization Algorithm for Robot Path Planning

Wang

et al. 2021

Journal of Advanced Transportation

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Path planning is one of the hotspots in the research of automotive engineering. Aiming at the issue of robot path planning with the goal of finding a collision-free optimal motion path in an environment with barriers, this study proposes an adaptive parallel arithmetic optimization algorithm (APAOA) with a novel parallel communication strategy. Comparisons with other popular algorithms on 18 benchmark functions are committed. Experimental results show that the proposed algorithm performs better in terms of solution accuracy and convergence speed, and the proposed strategy can prevent the algorithm from falling into a local optimal solution. Finally, we apply APAOA to solve the problem of robot path planning.

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An Improved Heuristic Algorithm for UCAV Path Planning

Cited by 5 publications

References 10 publications

Determining UAV Flight Trajectory for Target Recognition Using EO/IR and SAR

Determining UAV Flight Trajectory for Target Recognition Using EO/IR and SAR

A Fast Global Flight Path Planning Algorithm Based on Space Circumscription and Sparse Visibility Graph for Unmanned Aerial Vehicle

An Adaptive Parallel Arithmetic Optimization Algorithm for Robot Path Planning

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