Rapidly Exploring Random Tree Algorithm-Based Path Planning for Robot-Aided Optical Manipulation of Biological Cells

In this paper, the problem of online path planning for autonomous inspection of distribution network lines by UAV is studied. Because the distribution lines are mostly distributed around cities, counties and mountainous areas, the lines and their surrounding environment are uncertain and dynamic. These factors will affect the safety of UAV inspection, making the off-line pre-planned path for UAV unavailable. This paper designs an improved iteration random tree algorithm (IRRT) algorithm, which can quickly plan the path of UAV in dynamic environment.

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An Adaptive Rapidly-Exploring Random Tree Algorithm for Assembly Path Planning in Complex Environments

Volume 2: 31st Computers and Information in Engineering Conference, Parts A and B ◽

10.1115/detc2011-47535 ◽

2011 ◽

Author(s):

Wei Shang ◽

Jian-hua Liu

Keyword(s):

Path Planning ◽

Performance Improvement ◽

Random Tree ◽

Complex Environments ◽

Tree Algorithm ◽

The Future ◽

Constrained Environments ◽

Significant Performance

We present a refined Rapidly-exploring Random Tree (RRT) algorithm for assembly path planning in complex environments. This algorithm adapts its expansion automatically to explore complex environments with narrow passages and cluttered obstacles more efficiently. In this algorithm, the nodes in the tree are classified by various criterions and different extending values are assigned on them indicating the nearby environment and are used to control the future expansion. A series of tree extending schemes are designed and selectively used based on the attributes of the node and the extending result in each step. We show that the algorithm becomes greedy in constrained environments and promising nodes have higher priority to extend than the non-promising ones. The algorithm is evaluated and applied in assembly path planning. The results show significant performance improvement over the standard RRT planner.

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Dynamic path planning in robot-aided optical manipulation of biological cells

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems ◽

10.1109/iros.2012.6385831 ◽

2012 ◽

Cited By ~ 1

Author(s):

Tao Ju ◽

Shuang Liu ◽

Jie Yang ◽

Dong Sun

Keyword(s):

Path Planning ◽

Optical Manipulation ◽

Biological Cells ◽

Dynamic Path Planning ◽

Dynamic Path

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Path planning for mobile robot based on modified rapidly exploring random tree method and neural network

International Journal of Advanced Robotic Systems ◽

10.1177/1729881418784221 ◽

2018 ◽

Vol 15 (3) ◽

pp. 172988141878422 ◽

Cited By ~ 2

Author(s):

Pengchao Zhang ◽

Chao Xiong ◽

Wenke Li ◽

Xiaoxiong Du ◽

Chuan Zhao

Keyword(s):

Neural Network ◽

Path Planning ◽

Mobile Robot ◽

Real Time ◽

Random Tree ◽

Target Point ◽

Planning Problem ◽

Tree Algorithm ◽

Time Performance ◽

Path Planning Problem

In the course of the task, the mobile robot should find the shortest and most smooth obstacle-free path to move from the current point to the target point efficiently, which is namely the path planning problem of the mobile robot. After analyzing a large number of planning algorithms, it is found that the combination of traditional planning algorithm and heuristic programming algorithm based on artificial intelligence have outstanding performance. Considering that the basic rapidly exploring random tree algorithm is widely used for some of its advantages, meanwhile there are still defects such as poor real-time performance and rough planned path. So, in order to overcome these shortcomings, this article proposes target bias search strategy and a new metric function taking both distance and angle into account to improve the basic rapidly exploring random tree algorithm, and the neural network is used for curve post-processing to obtain a smooth path. Through simulating in complex environment and comparison with the basic rapidly exploring random tree algorithm, it shows good real-time performance and relatively shorter and smoother planned path, proving that the improved algorithm has good performance in handling path planning problem.

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Path planning for flexible needle insertion system based on Improved Rapidly-Exploring Random Tree algorithm

2015 IEEE International Conference on Information and Automation ◽

10.1109/icinfa.2015.7279532 ◽

2015 ◽

Cited By ~ 2

Author(s):

Jing Xiong ◽

Xia Li ◽

Yangzhou Gan ◽

Zeyang Xia

Keyword(s):

Path Planning ◽

Needle Insertion ◽

Random Tree ◽

Tree Algorithm

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Path Planning and Obstacle Avoidance for Automated Driving Systems Using Rapidly-Exploring Random Tree Algorithm

SAE International Journal of Connected and Automated Vehicles ◽

10.4271/12-04-03-0018 ◽

2021 ◽

Vol 4 (3) ◽

Author(s):

Kuen Yehliu

Keyword(s):

Path Planning ◽

Obstacle Avoidance ◽

Random Tree ◽

Automated Driving ◽

Tree Algorithm

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Rapidly exploring Random Tree* with a sampling method based on Sukharev grids and convex vertices of safety hulls of obstacles

International Journal of Advanced Robotic Systems ◽

10.1177/1729881419825941 ◽

2019 ◽

Vol 16 (1) ◽

pp. 172988141982594 ◽

Cited By ~ 3

Author(s):

Luiz GDO Véras ◽

Felipe LL Medeiros ◽

Lamartine NF Guimarães

Keyword(s):

Path Planning ◽

Unmanned Aerial Vehicles ◽

Computational Cost ◽

Sampling Strategy ◽

Random Tree ◽

Sample Collection ◽

Planning Time ◽

Tree Algorithm ◽

Aerial Vehicles ◽

Intelligent Sampling

The path planning for an Unmanned Aerial Vehicles ensures that a dynamically feasible and collision-free path is planned between a start and an end point within a navigation environment. One of the most used algorithms for path planning is the Rapidly exploring Random Tree, where each one of its nodes is randomly collected from the navigation environment until the start and end navigation points are connected through them. The Rapidly exploring Random Tree algorithm is probabilistically complete, which ensures that a path, if one exists, will be found if the quantity of sampled nodes increases infinitely. However, there is no guarantee that the shortest path to a navigation environment will be planned by Rapidly exploring Random Tree algorithm. The Rapidly exploring Random Tree* algorithm is a path planning method that guarantees the shorter path length to the UAV but at a high computational cost. Some authors state that by informing sample collection to specific positions on the navigation environment, it would be possible to improve the planning time of this algorithm, as example of the Rapidly exploring Random Tree*-Smart algorithm, that utilize intelligent sampling and path optimization procedures to this purpose. This article introduces a novel Rapidly exploring Random Tree*-based algorithm, where a new sampling process based on Sukharev grids and convex vertices of the security hulls of obstacles is proposed. Computational tests are performed to verify that the new sampling strategy improves the planning time of Rapidly exploring Random Tree*, which can be applied to real-time navigation of Unmanned Aerial Vehicles. The results presented indicate that the use of convex vertices and grid of Sukharev accelerate the planning time of the Rapidly exploring Random Tree* and show better performance than the Rapidly exploring Random Tree*-Smart algorithm in several navigation environments with different quantities and spatial distributions of polygonal obstacles.

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Path Planning of Anti ship Missile based on Voronoi Diagram and Binary Tree Algorithm

Defence Science Journal ◽

10.14429/dsj.69.14062 ◽

2019 ◽

Vol 69 (4) ◽

pp. 369-377

Author(s):

Yan Shi ◽

Lihua Zhang ◽

Shouquan Dong

Keyword(s):

Path Planning ◽

Voronoi Diagram ◽

Binary Tree ◽

Dijkstra Algorithm ◽

Tree Algorithm ◽

Multiple Missiles ◽

Intractable Problem ◽

The Relationship

The path planning of anti-ship missile should be considered both cruising in safety and striking in quick, which is an intractable problem. In particular, it is difficult to consider the safety of each missile path in the path planning of multiple missiles. To solve this problem, the “AREA Algorithm” is presented to divide the relative relations of areas：relative security area of the threat areas and fast-attack area of target approaching. Specifically，it is a way to achieve area division through the relationship between the target and the center of the operational area. The Voronoi diagram topology network, Dijkstra algorithm and binary tree algorithm have been used in the above process as well. Finally, Simulations have verified the feasibility and obvious advantages of “AREA Algorithm” compared with the single algorithm, and the tactical meaning in path planning of multiple missiles.

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