RRT-GD: An efficient rapidly-exploring random tree approach with goal directionality for redundant manipulator path planning

2016 ◽  
Author(s):  
Junxiang Ge ◽  
Fuchun Sun ◽  
Chunfang Liu
Keyword(s):  
Path Planning ◽  
Random Tree ◽  
Redundant Manipulator ◽  
Tree Approach

scholarly journals Optimal randomized path planning for redundant manipulators based on Memory-Goal-Biasing

2018 ◽  
Vol 15 (4) ◽  
pp. 172988141878704 ◽  
Author(s):  
Dong Han ◽  
Hong Nie ◽  
Jinbao Chen ◽  
Meng Chen
Keyword(s):  
Path Planning ◽  
Search Tree ◽  
Random Tree ◽  
Complex Environments ◽  
Redundant Manipulator ◽  
Optimal Method ◽  
Key Points ◽  
Industrial Manipulators ◽  
Novel Method ◽  
Better Than

Planning path rapidly and optimally is one of the key technologies for industrial manipulators. A novel method based on Memory-Goal-Biasing–Rapidly-exploring Random Tree is proposed to solve high-dimensional manipulation planning more rapidly and optimally. The tree extension of Memory-Goal-Biasing–Rapidly-exploring Random Tree can be divided into random extension and goal extension. In the goal extension, the nodes extended to the goal are recorded in a memory, and then the node closest to the goal is selected in the search tree excepting the nodes in the memory for overcoming the local minimum. In order to check collisions efficiently, the manipulator is simplified into several key points, and the obstacle area is appropriately enlarged for safety. Taking the redundant manipulator of Baxter robot as an example, the proposed algorithm is verified through MoveIt! software. The results show that Memory-Goal-Biasing–Rapidly-exploring Random Tree only takes a few seconds for the path planning of the redundant manipulator in some complex environments, and within an acceptable time, its optimization performance is better than that of traditional optimal method in terms of the obtained path costs and the corresponding standard deviation.

A Novel Path Planning Method Based on Regional Index Algorithm for Hyper-redundant Manipulator

2021 ◽  
pp. 669-679
Author(s):  
Longfei Jia ◽  
Yaxing Guo ◽  
Yunfei Tao ◽  
He Cai ◽  
Tianhua Fu ◽  
...  
Keyword(s):  
Path Planning ◽  
Planning Method ◽  
Redundant Manipulator ◽  
Regional Index

An Improvement of the Rapidly-Exploring Random Tree Method for Path Planning of a Car-Like Robot in Virtual Environment

2015 ◽  
Vol 772 ◽  
pp. 471-476 ◽  
Author(s):  
Teodora Gîrbacia ◽  
Gheorghe Mogan
Keyword(s):  
Computational Complexity ◽  
Path Planning ◽  
Real Time ◽  
Virtual Environment ◽  
Minimum Distance ◽  
Optimal Path ◽  
Random Tree ◽  
Real Time Application ◽  
Simulation Time ◽  
Tree Method

In this paper we present a method of reducing the computational complexity necessary in path planning for a car-like robot in order to generate the optimal path according to the constrains set by the user. The proposed method implies adding the following constrains: setting the maximum and minimum distance between the possible paths and the obstacles placed in the virtual environment in order to reduce the simulation time and to obtain a real-time application and to remove the paths that contain unnecessary turns around the environment without avoiding an obstacle. By applying this method the simulation complexity is reduced and the optimal path is easier to find.

scholarly journals Collision-Free Path Planning Method for Robots Based on an Improved Rapidly-Exploring Random Tree Algorithm

2020 ◽  
Vol 10 (4) ◽  
pp. 1381 ◽  
Author(s):  
Xinda Wang ◽  
Xiao Luo ◽  
Baoling Han ◽  
Yuhan Chen ◽  
Guanhao Liang ◽  
...  
Keyword(s):  
Path Planning ◽  
Control Mechanism ◽  
Random Tree ◽  
High Dimensional ◽  
Control Factor ◽  
Sampling Area ◽  
Planning Algorithm ◽  
Search Speed ◽  
The Many ◽  
Path Planning Algorithm

Sampling-based methods are popular in the motion planning of robots, especially in high-dimensional spaces. Among the many such methods, the Rapidly-exploring Random Tree (RRT) algorithm has been widely used in multi-degree-of-freedom manipulators and has yielded good results. However, existing RRT planners have low exploration efficiency and slow convergence speed and have been unable to meet the requirements of the intelligence level in the Industry 4.0 mode. To solve these problems, a general autonomous path planning algorithm of Node Control (NC-RRT) is proposed in this paper based on the architecture of the RRT algorithm. Firstly, a method of gradually changing the sampling area is proposed to guide exploration, thereby effectively improving the search speed. In addition, the node control mechanism is introduced to constrain the extended nodes of the tree and thus reduce the extension of invalid nodes and extract boundary nodes (or near-boundary nodes). By changing the value of the node control factor, the random tree is prevented from falling into a so-called “local trap” phenomenon, and boundary nodes are selected as extended nodes. The proposed algorithm is simulated in different environments. Results reveal that the algorithm greatly reduces the invalid exploration in the configuration space and significantly improves planning efficiency. In addition, because this method can efficiently use boundary nodes, it has a stronger applicability to narrow environments compared with existing RRT algorithms and can effectively improve the success rate of exploration.

Sign in / Sign up

Export Citation Format

Share Document