Integration of a visibility graph based path planning method in the ACT/FHS rotorcraft

Zimmermann, Michael; König, Christian

doi:10.1007/s13272-016-0197-0

Cited by 9 publications

(7 citation statements)

References 10 publications

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“…the turning angle, the slope angle, and the flight height. The constraint function C is defined as 3 v=1 C v (P) = C collision (P) + C turning (P) + C slope (P) (10) where C collision (•) is used to penalize the routes that collide with the ground, C turning (•) penalizes the routes requiring larger turning angle than the maximum available turning angle, and C slope (•) penalizes the routes requiring slope angle beyond the given slope angle. The constraint function of a flight route is depicted in detail as follows:…”

Section: B Constrain Function Of Flight Routementioning

confidence: 99%

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A Novel Route Planning Method of Fixed-Wing Unmanned Aerial Vehicle Based on Improved QPSO

Huang

Fei

2020

IEEE Access

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This paper proposes a quick and accurate method based on an improved quantum-behaved particle swarm optimization (QPSO) algorithm for route planning of fixed-wing unmanned aerial vehicle (UAV). To overcome the deficiencies of local optima and slow global convergence speed, a novel strategy of particle dimension search is proposed in the QPSO algorithm. It is implemented by transforming original evaluation function into evaluation function of waypoint to more easily escape from local optima and accelerate global convergence speed. In addition, an efficient pretreatment technology for the initial trajectory is set to shorten the calculation time of route planning. Compared with other representative route planners, the comparison results indicate that the proposed route planner is more effective and feasible, which can take on faster convergence speed and better global search ability. The proposed route planner can provide a valuable reference for the route planning of fixed-wing UAVs in different environments. INDEX TERMS Unmanned aerial vehicle, route planning, QPSO, particle dimension search.

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Section: B Constrain Function Of Flight Routementioning

confidence: 99%

“…At present, there are three classes of approaches to solve offline path planning problem. The first type is based on graph algorithms, such as Voronoi diagram [9] and Visibility Graph approach [10], [11]. However, the kinematic and dynamic constraints of UAV are unable to be taken into account based on graph algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Novel Route Planning Method of Fixed-Wing Unmanned Aerial Vehicle Based on Improved QPSO

Huang

Fei

2020

IEEE Access

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“…As curves or straight lines are used for constructing PRMs, a robot may go anywhere in its free space. Visibility graph [15][16][17] and Voronoi diagram [18][19][20] are two typical road-map approaches, which have achieved better results with dramatically various types of roads. RRT algorithms randomly build a space-filling tree to efficiently handle problems with obstacles and differential constraints by searching nonconvex and high-dimensional spaces [21].…”

Section: Related Workmentioning

confidence: 99%

Path Planning with Obstacle Avoidance Based on Normalized R-Functions

Tao

Tan

2018

Journal of Robotics

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Existing methods for path planning with obstacle avoidance need to check having the interference between a moving part and an obstacle at iteration and even to calculate their shortest distance in the case of given motion parameters. Besides, the tasks like collision-checking and minimum-distance calculating themselves are complicated and time-consuming. Rigorous mathematical analysis might be a practical way for dealing with the above-mentioned problems. An R-function is a real-valued function whose properties are fully determined by corresponding attributes of their parameters, which is usually applied to express a geometrical object. Thus, a signed distance function based on R-functions is created to represent whether two objects intervene and their level of intervention or separation. As the signed function is continuous and differentiable, the gradient information of the objective function guides a moving part to avoid its obstacles and to approach its target position rapidly. Therefore, a path planning approach with obstacle avoidance based on normalized R-functions is proposed in this paper. A discrete convex hull approach is adopted to solve the problem that R-function is inappropriate to represent a geometric object with some curves or surfaces, and pendent points and edges are generated in Boolean operations. Besides, a normalized approach ensures accuracy calculation of signed distance function. Experimental results have shown that the presented approach is a feasible way for path planning with obstacle avoidance.

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“…The takeoff planning was tested successfully in the ground-based system simulator which motivates future flight tests. The approach is planned from level flight at constant altitude to the final hover point [27,28]. The standard approach uses an airpath inclination angle between −8 • and −12 • .…”

Section: Path Planning Algorithmsmentioning

confidence: 99%

Results of the pilot assistance system “Assisted Low-Level Flight and Landing on Unprepared Landing Sites” obtained with the ACT/FHS research rotorcraft

Greiser¹,

Lantzsch²,

Wolfram³

et al. 2015

Aerospace Science and Technology

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Integration of a visibility graph based path planning method in the ACT/FHS rotorcraft

Cited by 9 publications

References 10 publications

A Novel Route Planning Method of Fixed-Wing Unmanned Aerial Vehicle Based on Improved QPSO

A Novel Route Planning Method of Fixed-Wing Unmanned Aerial Vehicle Based on Improved QPSO

Path Planning with Obstacle Avoidance Based on Normalized R-Functions

Results of the pilot assistance system “Assisted Low-Level Flight and Landing on Unprepared Landing Sites” obtained with the ACT/FHS research rotorcraft

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