Computational Modeling for Automatic Path Planning Based on Evaluations of the Effects of Impacts of UAVs on the Ground

Medeiros, Felipe Leonardo Lôbo; Silva, José Demisio Simões da

doi:10.1007/978-94-007-1110-5_13

Cited by 6 publications

(7 citation statements)

References 13 publications

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“…Path planning algorithms mainly include roadmap method, cell decomposition method, potential field method, optimization-based planning method, and sampling-based planning method. In the roadmap method, networks of collision-free paths connecting start and target points are constructed by using the visibility graph method 2 and Voronoi graph method. 3 In the cell decomposition method, 4 the free planning space is decomposed into interconnected and nonoverlapping cells to form a connected graph.…”

Section: Introductionmentioning

confidence: 99%

Online cooperative path planning for multi-quadrotors in an unknown dynamic environment

Jia

Lin

Liu

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part G:

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The online cooperative path planning problem is discussed for multi-quadrotor maneuvering in an unknown dynamic environment. Based on the related basic concepts, typical three-dimensional obstacle models, such as spherical and cubic, and their collision checking criteria are presented in this article. An improved rapidly exploring random tree (RRT) algorithm with goal bias and greed property is proposed based on the heuristic search strategy to overcome the shortcomings of the classical RRT algorithm. Not only are the kinematic constraints of the quadrotor established but the time and space coordination strategy matching with the improved RRT algorithm is also presented in this article. Furthermore, a novel online collision avoidance strategy according to the partial information of the surrounding environment is proposed. On the basis of the above work, a distributed online path planning strategy is proposed to obtain the feasible path for each quadrotor. Numerical simulation results show that the improved RRT algorithm has better search efficiency than the classical RRT algorithm. And the satisfactory path planning and path tracking results prove that the above model and related planning strategies are reasonable and effective.

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Section: Introductionmentioning

confidence: 99%

Online cooperative path planning for multi-quadrotors in an unknown dynamic environment

Jia

Lin

Liu

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part G:

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“…UAVs in urban areas may fall and hit people and vehicles on the ground [6][7]. UAVs may also intrude into airport airspaces [8].…”

Section: Introductionmentioning

confidence: 99%

Risk Assessment Model for UAV Cost-Effective Path Planning in Urban Environments

Pang

Dai

et al. 2020

IEEE Access

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Increasing use of Unmanned Aerial Vehicle (UAV) in urban environments poses to an increased risk of fallen UAVs impacting people and vehicles on the ground, as well as colliding with manned aircraft in the vicinity of airports. Risk management of UAV flights for safe operations is essential. We proposed a comprehensive risk assessment model for UAV operation in urban environments. Three risk categories (people, vehicles, and manned aircraft) were considered and each risk cost was quantified using collision probability. We adjusted the risk costs in various magnitudes to a same scale and conducted a sensitivity analysis to determine the optimal coefficients of the three risk cost models. We then computed the total risk and generated a risk cost map for path planning. Modified path planning algorithms were used to produce a cost-effective path, and we compared their performances in terms of total risk cost and computational time. Lastly, we performed simulations to validate the feasibility and effectiveness of our proposed risk assessment model. The results show that the risk-cost-based path planning method can generate safer path for UAV operations than the traditional shortest-distance-based method. Our proposed model can be extended to complex urban environments by including more relevant parameters and data. INDEX TERMS Unmanned aerial vehicle, risk assessment model, risk cost map, path planning, urban environments Fuqing Dai holds the Dean's Chair in Air Traffic Management School at the Civil Aviation University of China. He is also a professor focusing on the research of airspace planning and management, flight procedures design and optimization, UTM and ATM systems. Professor Dai obtained his master's degree from Ecole Nationale de l'Aviation Civile, France and bachelor's degree from Nanjing University, China.

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“…Thus, the shortest path to the destination can be found. This path planning method includes Dijkstra algorithm [8] and further developed to the well-known A* algorithm [9][10][11][12]. These methods are able to avoid UAV crashing on obstacles by previously knowing the obstacle locations.…”

Section: Introductionmentioning

confidence: 99%

A New Path Planning Algorithm Based on GNSS Localization Error Map

Zhang¹,

Hsu²

2017

Proceedings of the 30th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS+ 201

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Computational Modeling for Automatic Path Planning Based on Evaluations of the Effects of Impacts of UAVs on the Ground

Cited by 6 publications

References 13 publications

Online cooperative path planning for multi-quadrotors in an unknown dynamic environment

Online cooperative path planning for multi-quadrotors in an unknown dynamic environment

Risk Assessment Model for UAV Cost-Effective Path Planning in Urban Environments

A New Path Planning Algorithm Based on GNSS Localization Error Map

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