Some Advanced Techniques in Reducing Time for Path Planning Based on Visibility Graph

Nguyet, Tran Thi Nhu; Hoài, Trần Văn; Thi, Nguyen

doi:10.1109/kse.2011.37

Cited by 15 publications

(17 citation statements)

References 17 publications

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“…• roadmap methods (e.g., Voronoi diagrams [23], visibility graphs [22], probabilistic roadmaps [24]), 1 For the most of the path planning methods, the optimal path means the shortest path. In this paper, the experimental work presents the solutions of the shortest path planning problem on 2D grid type maps as well as the optimal path planning problem with respect to applied optimization criteria.…”

Section: Introductionmentioning

confidence: 99%

L* Algorithm—A Linear Computational Complexity Graph Searching Algorithm for Path Planning

Niewola

Podsedkowski

2017

J Intell Robot Syst

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The state-of-the-art graph searching algorithm applied to the optimal global path planning problem for mobile robots is the A* algorithm with the heap structured open list. In this paper, we present a novel algorithm, called the L* algorithm, which can be applied to global path planning and is faster than the A* algorithm. The structure of the open list with the use of bidirectional sublists (buckets) ensures the linear computational complexity of the L* algorithm because the nodes in the current bucket can be processed in any sequence and it is not necessary to sort the bucket. Our approach can maintain the optimality and linear computational complexity with the use of the cost expressed by floating-point numbers. The paper presents the requirements of the L* algorithm use and the proof of the admissibility of this algorithm. The experiments confirmed that the L* algorithm is faster than the A* algorithm in various path planning scenarios. We also introduced a method of estimating the execution time of the A* and the L* algorithm. The method was compared with the experimental results.

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Section: Introductionmentioning

confidence: 99%

L* Algorithm—A Linear Computational Complexity Graph Searching Algorithm for Path Planning

Niewola

Podsedkowski

2017

J Intell Robot Syst

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“…An optimal path can be defined as the shortest path generated by a path planning algorithm among all the produced path commencing from the starting point Sp to the end point Tp [2], [8][9][10][11]. A path planning algorithm holds the completeness criterion if it is able to produce a path if one exists.…”

Section: Introductionmentioning

confidence: 99%

“…VG is a complete algorithm and capable of producing a path with the shortest distance but its drawback is the computation time increases when the number of obstacles in C-spaces increases [9], [10], [12]. VD, on the other hand, uses equidistant techniques to discover a path for which an optimal path cannot be accomplished although it has relatively lower computation time and the algorithm is complete [2], [13].…”

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confidence: 99%

Optimal Path Planning using Equilateral Spaces Oriented Visibility Graph Method

Latip¹,

Omar²,

Debnath³

2017

IJECE

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Path planning has been an important aspect in the development of autonomous cars in which path planning is used to find a collision-free path for the car to traverse from a starting point Sp to a target point Tp. The main criteria for a good path planning algorithm include the capability of producing the shortest path with a low computation time. Low computation time makes the autonomous car able to re-plan a new collision-free path to avoid accident. However, the main problem with most path planning methods is their computation time increases as the number of obstacles in the environment increases. In this paper, an algorithm based on visibility graph (VG) is proposed. In the proposed algorithm, which is called Equilateral Space Oriented Visibility Graph (ESOVG), the number of obstacles considered for path planning is reduced by introducing a space in which the obstacles lie. This means the obstacles located outside the space are ignored for path planning. From simulation, the proposed algorithm has an improvement rate of up to 90% when compared to VG. This makes the algorithm is suitable to be applied in real-time and will greatly accelerate the development of autonomous cars in the near future.

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“…By ignoring redundant obstacles which do not affect the result of path planning, it improves the efficiency of path planning. Some techniques are used to improve the time complexity such as reducing the amount of visibility edges, simplifying obstacles to rectangles, and combining the tiny obstacles [14,15]. Some intelligence algorithms such as quantized algorithm [16] and ant colony [17] are used in path planning based on visibility graph to improve the computational efficiency.…”

Section: Introductionmentioning

confidence: 99%

A Global Path Planning Algorithm Based on Bidirectional SVGA

Lv¹,

Zhao²,

Bao³

2017

Journal of Robotics

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For path planning algorithms based on visibility graph, constructing a visibility graph is very time-consuming. To reduce the computing time of visibility graph construction, this paper proposes a novel global path planning algorithm, bidirectional SVGA (simultaneous visibility graph construction and path optimization by A⁎). This algorithm does not construct a visibility graph before the path optimization. However it constructs a visibility graph and searches for an optimal path at the same time. At each step, a node with the lowest estimation cost is selected to be expanded. According to the status of this node, different through lines are drawn. If this line is free-collision, it is added to the visibility graph. If not, some vertices of obstacles which are passed through by this line are added to the OPEN list for expansion. In the SVGA process, only a few visible edges which are in relation to the optimal path are drawn and the most visible edges are ignored. For taking advantage of multicore processors, this algorithm performs SVGA in parallel from both directions. By SVGA and parallel performance, this algorithm reduces the computing time and space. Simulation experiment results in different environments show that the proposed algorithm improves the time and space efficiency of path planning.

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Some Advanced Techniques in Reducing Time for Path Planning Based on Visibility Graph

Cited by 15 publications

References 17 publications

L* Algorithm—A Linear Computational Complexity Graph Searching Algorithm for Path Planning

L* Algorithm—A Linear Computational Complexity Graph Searching Algorithm for Path Planning

Optimal Path Planning using Equilateral Spaces Oriented Visibility Graph Method

A Global Path Planning Algorithm Based on Bidirectional SVGA

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