RimJump: Edge-based Shortest Path Planning for a 2D Map

Yao, Zhuo; Zhang, Weimin; Shi, Yongliang; Li, Mingzhu; Liang, Zhenshuo; Li, Fangxing; Qiang, Huang

doi:10.1017/s0263574718001236

Cited by 8 publications

(5 citation statements)

References 38 publications

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“…In the study of the GWO algorithm, path planning found that the path is affected by the number of path node, path node generally set for developers, violates the principle of the shortest distance between two points, the fitness function value is affected by the path node number at the same time, generally will increase the final optimization results of the algorithm. Zhuo et al (2018) use RimJump to decrease the number of path node in A* and Dijkstra algorithm, which inspire us [45].…”

Section: Algorithmmentioning

confidence: 99%

Improved gray wolf optimization algorithm integrating A* algorithm for path planning of mobile charging robots

Liu,

Xiao

2023

Robotica

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With the popularization of electric vehicles, early built parking lots cannot solve the charging problem of a large number of electric vehicles. Mobile charging robots have autonomous navigation and complete charging functions, which make up for this deficiency. However, there are static obstacles in the parking lot that are random and constantly changing their position, which requires a stable and fast iterative path planning method. The gray wolf optimization (GWO) algorithm is one of the optimization algorithms, which has the advantages of fast iteration speed and stability, but it has the drawback of easily falling into local optimization problems. This article first addresses this issue by improving the fitness function and position update of the GWO algorithm and then optimizing the convergence factor. Subsequently, the fitness function of the improved gray wolf optimization (IGWO) algorithm was further improved based on the minimum cost equation of the A* algorithm. The key coefficients AC1 and AC2 of two different fitness functions, Fitness1 and Fitness2, were discussed. The improved gray wolf optimization algorithm integrating A* algorithm (A*-IGWO) has improved the number of iterations and path length compared to the GWO algorithm in parking lots path planning problems.

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Section: Algorithmmentioning

confidence: 99%

Improved gray wolf optimization algorithm integrating A* algorithm for path planning of mobile charging robots

Liu,

Xiao

2023

Robotica

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“…The major difference between the tangent graph and the visibility graph is that the tangent graph can be applied to obstacles with arbitrary boundaries. Then, we proposed a series of tangent graphbased path planning method [10,11] which search the global shortest path in form of path tree instead of a queue, which is always used by general path planning. These approaches can always find the shortest path, and the time cost is insensitive to the map scale increase.…”

Section: @Qqcommentioning

confidence: 99%

“…1. The available range ( [10], [11]) that limit the orientation of tangent during tangent graph searching; 2. Remove paths that contain tangents which cross static or dynamic obstacles; 3.…”

Section: B Path Planning With Distinctive Topologiesmentioning

confidence: 99%

Tangent Based-Path Path Planning with Distinctive Topologies in a Dynamic Environment

Yao¹

2021

Preprint

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This manuscript proposes a novel tangent-graph-based method that provides all distinctive topology paths, as a set of trajectories that can not be transformed into each other by gradual bending and stretching without colliding with obstacles. As the global optimal trajectory (limited by dynamic constraints) may not correspond to the shortest path (almost without dynamic constraints), it is important to provide all distinctive topology paths rather than just the globally shortest one. One of the application is provide initial paths for trajectory planning methods, such as time elastic band (TEB) and dynamic window approach (DWA). Considering that a mobile platform is always working in a dynamic changing environment and that update the whole graph is time consuming, a dynamic rectify method is proposed to update tangents according to local map provided by sensor reading. Finally, results under real grid maps are presented.

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“…The major difference between the tangent graph and the visibility graph is that the tangent graph can be applied to curved obstacles. Then, we proposed a tangent graph based on a 2D path planning method [35,34] using a path tree to search the graph instead of a graph search. The approach can always find the shortest path, and the time cost is insensitive to the map scale increasing.…”

Section: Introductionmentioning

confidence: 99%

RimJump*: Tangent Based Shortest Path Planning for Cluttered 3D Environment

Zhang¹

2021

Preprint

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Path planning in 3D environment is a fundamental research area for robots and autonomous vehicles. Based on the principle ``the shortest path consists of tangents'', RimJump* is proposed as a tangent-based path planning method suitable for finding the shortest path (both off-ground and on-ground) in 3D space (e.g., octomap and point cloud) for mobile platform to follow. RimJump* searches the tangent graph in the form of a path tree and considers the geometrical properties of the locally shortest path. Therefore, the method can provide all of the locally shortest paths that connect the starting point and the target, including the globally shortest path. And the time cost of RimJump* is insensitive to map scale increases in comparison to methods that search the whole passable space rather than the surface of the obstacle, e.g., Dijkstra and A*. In the Results, RimJump* is compared with other methods in terms of path length and time cost.

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RimJump: Edge-based Shortest Path Planning for a 2D Map

Cited by 8 publications

References 38 publications

Improved gray wolf optimization algorithm integrating A* algorithm for path planning of mobile charging robots

Improved gray wolf optimization algorithm integrating A* algorithm for path planning of mobile charging robots

Tangent Based-Path Path Planning with Distinctive Topologies in a Dynamic Environment

RimJump*: Tangent Based Shortest Path Planning for Cluttered 3D Environment

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