3D Visibility Graph based Motion Planning and Control

You, Yangwei; Cai, Caixia; Wu, Yan

doi:10.1145/3373724.3373735

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…A. Knoll [3] proposed an extension of quadtrees to 3D, called octree, which is also widespread in image processing. A novel 3D visibility graph is proposed in [4] and applied in motion and control. Song [5] introduced dynamic Voronoi diagrams for moving disks.…”

Section: Related Workmentioning

confidence: 99%

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Trajectory planning for UAV navigation in dynamic environments with matrix alignment Dijkstra

Wang

et al. 2022

Soft Comput

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The trajectory planning for UAVs in the dynamic environments is a challenging task. Many restrictions should be taken into consideration, including dynamic terrain collision, no-fly zone criteria, power and fuel criteria and so on. However, some methods treat dynamic restrictions as static in order to reduce cost and obtain efficient and acceptable paths. To achieve optimal, efficient and acceptable paths, we first use a high-dimension matrix, Extended Hierarchical Graph (EHG), to model the unexplored dynamic grid environment and to convert weather conditions to passable coefficient. The original shortest path planning task is then translated to plan the safest path. Therefore, we propose a new forward-exploration and backward-navigation algorithm, Matrix Alignment Dijkstra (MAD), to pilot UAVs. We use matrix alignment operation to simulate the parallel exploration process of all cells from time t to t+1. This exploration process can be accelerated on a GPU. Finally, we recall the optimal path according to the navigator matrix. In addition, we can achieve UAV path planning from one source cell to multiple target cells within one single run. We validate our method on a real dynamic weather dataset and get competitive results in both efficiency and accuracy. We analyse the performance of MAD on a grid-based benchmark dataset and artificial maze data. Simulated results show MAD is especially helpful when the grid-based environment is large-scale and dynamic.

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Section: Related Workmentioning

confidence: 99%

“…In other words, the execution time will increase linearly as the searching space becomes larger. As is mentioned above, mazes in the benchmarks have different corridor sizes (1,2,4,8,16,32) in their passages. We choose mazes with corridor sizes of 1, 8, 16, 32 to verify the performance of MAD algorithm.…”

Section: Analysis Of Simulationmentioning

confidence: 99%

Trajectory planning for UAV navigation in dynamic environments with matrix alignment Dijkstra

Wang

et al. 2022

Soft Comput

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“…This requires a kind of discretization. There exist several methods to do this, e.g., using a grid [4], Voronoi regions [6] or visibility graphs [24].…”

Section: Free-place Turningsmentioning

confidence: 99%

Trajectory Planning for Narrow Environments That Require Changes of Driving Directions

Roth¹

2022

JAMRIS

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In the area of mobile robotics, trajectory planning is the task to find a sequence of primitive trajectories that connect two configurations, whereas non-holonomic constraints, obstacles and driving costs have to be considered. In this paper, we present an approach that is able to handle situations that require changes of driving directions. In such situations, optimal trajectory sequences contain costly turning maneuvers – sometimes not even on the direct path between start and target. These situations are difficult for most optimization approaches as the robot partly has to drive paths with higher cost values that seem to be disadvantageous. We discuss the problem in depth and provide a solution that is based on maneuvers, partial backdriving and free-place discovery. We applied the approach on top of our Viterbi-based trajectory planner.

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