Robot path planning using improved rapidly-exploring random tree algorithm

2018 ◽  
Author(s):  
Dong-Qing He ◽  
Hong-Bo Wang ◽  
Peng-Fei Li
Keyword(s):  
Path Planning ◽  
Random Tree ◽  
Robot Path Planning ◽  
Tree Algorithm ◽  
Robot Path

Path Planning Based on Heuristic Rapidly-Exploring Random Tree for Nonholonomic Mobile Robot

2014 ◽  
Vol 494-495 ◽  
pp. 1161-1164
Author(s):  
Liang Kang ◽  
Lian Cheng Mao
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Random Tree ◽  
Robot Path Planning ◽  
Heuristic Function ◽  
Kinematic Constraints ◽  
Nonholonomic Mobile Robot ◽  
Calculation Results ◽  
Trajectory Tracking Controller ◽  
Robot Path

The RRT algorithm and the heuristic function are combined in the mobile robot path planning, so a novel path planning is proposed. Based on the dynamic model and kinematic constraints of the nonholonomic mobile robot, a trajectory tracking controller is designed. Theory and calculation results prove that, as a new method for mobile robot path planning, the heuristic Rapidly-Exploring Random Tree for nonholonomic mobile robot path planning is feasible and effective.

scholarly journals Improved RRT-Connect Algorithm Based on Triangular Inequality for Robot Path Planning

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 333
Author(s):  
Jin-Gu Kang ◽  
Dong-Woo Lim ◽  
Yong-Sik Choi ◽  
Woo-Jin Jang ◽  
Jin-Woo Jung
Keyword(s):  
Path Planning ◽  
Path Length ◽  
Experimental Results ◽  
Random Tree ◽  
Robot Path Planning ◽  
Planning Time ◽  
Triangular Inequality ◽  
Planning Algorithm ◽  
Path Planning Algorithm ◽  
Robot Path

This paper proposed a triangular inequality-based rewiring method for the rapidly exploring random tree (RRT)-Connect robot path-planning algorithm that guarantees the planning time compared to the RRT algorithm, to bring it closer to the optimum. To check the proposed algorithm’s performance, this paper compared the RRT and RRT-Connect algorithms in various environments through simulation. From these experimental results, the proposed algorithm shows both quicker planning time and shorter path length than the RRT algorithm and shorter path length than the RRT-Connect algorithm with a similar number of samples and planning time.

scholarly journals An Enhancement Method of Rapidly-exploring Random Tree Robot Path Planning using Midpoint Interpolation

2021 ◽  
Author(s):  
Jin-Gu Kang ◽  
Yong-Sik Choi ◽  
Jin-Woo Jung
Keyword(s):  
Path Planning ◽  
Path Length ◽  
Interpolation Method ◽  
Optimal Path ◽  
Random Tree ◽  
Interpolation Process ◽  
Robot Path Planning ◽  
Planning Time ◽  
Enhancement Method ◽  
Robot Path

To solve the problem that sampling-based Rapidly-exploring Random Tree (RRT) method is difficult to guarantee optimality. This paper proposed the Post Triangular Processing of Midpoint Interpolation method minimized the planning time and shorter path length of the sampling-based algorithm. The proposed Post Triangular Processing of Midpoint Interpolation method makes a closer to the optimal path and somewhat solves the sharp path problem through the interpolation process. The experiments were conducted to verify the performance of the proposed method. Applying the method proposed in this paper to the RRT algorithm increases the efficiency of optimization compared to the planning time.

scholarly journals Improved RRT-Connect Algorithm Based on Triangular Inequality for Robot Path Planning

2020 ◽  
Author(s):  
Jin-Gu Kang ◽  
Dong-Woo Lim ◽  
Yong-Sik Choi ◽  
Woo-Jin Jang ◽  
Jin-Woo Jung
Keyword(s):  
Path Planning ◽  
Path Length ◽  
Experimental Results ◽  
Random Tree ◽  
Robot Path Planning ◽  
Planning Time ◽  
Triangular Inequality ◽  
Planning Algorithm ◽  
Path Planning Algorithm ◽  
Robot Path

This paper proposed a triangular inequality-based rewiring method for the Rapidly exploring Random Tree (RRT)-Connect robot path-planning algorithm that guarantees the planning time compared to the RRT algorithm, to bring it closer to the optimum. To check the proposed algorithm’s performance, this paper compared the RRT and RRT-Connect algorithms in various environments through simulation. From these experimental results, the proposed algorithm shows both quicker planning time and shorter path length than the RRT algorithm and shorter path length than the RRT-Connect algorithm with a similar number of samples and planning time.

scholarly journals Improved RRT-Connect Algorithm Based on Triangular Inequality for Robot Path Planning

2020 ◽  
Author(s):  
Jin-Gu Kang ◽  
Dong-Woo Lim ◽  
Yong-Sik Choi ◽  
Woo-Jin Jang ◽  
Jin-Woo Jung
Keyword(s):  
Path Planning ◽  
Path Length ◽  
Experimental Results ◽  
Random Tree ◽  
Robot Path Planning ◽  
Planning Time ◽  
Triangular Inequality ◽  
Planning Algorithm ◽  
Path Planning Algorithm ◽  
Robot Path

This paper proposed a triangular inequality-based rewiring method for the Rapidly exploring Random Tree (RRT)-Connect robot path-planning algorithm that guarantees the planning time compared to the RRT algorithm, to bring it closer to the optimum. To check the proposed algorithm’s performance, this paper compared the RRT and RRT-Connect algorithms in various environments through simulation. From these experimental results, the proposed algorithm shows both quicker planning time and shorter path length than the RRT algorithm and shorter path length than the RRT-Connect algorithm with a similar number of samples and planning time.

A New Robot Path Planning Method Based on LSTM Neural Network and Rapidly-Exploring Random Tree Algorithm

2021 ◽  
Author(s):  
Weifei Hu ◽  
Feng Tang ◽  
Zhenyu Liu ◽  
Jianrong Tan
Keyword(s):  
Neural Network ◽  
Path Planning ◽  
Short Term Memory ◽  
Computational Cost ◽  
Random Tree ◽  
Robot Path Planning ◽  
Path Information ◽  
Planning Methods ◽  
Robot Path ◽  
Path Quality

Abstract As an important field of robot research, robot path planning has been studied extensively in the past decades. A series of path planning methods have been proposed, such as A* algorithm, Rapidly-exploring Random Tree (RRT), Probabilistic Roadmaps (PRM). Although various robot path planning algorithms have been proposed, the existing ones are suffering the high computational cost and low path quality, due to numerous collision detection and exhausting exploration of the free space. In addition, few robot path planning methods can automatically and efficiently generate path for a new environment. In order to address these challenges, this paper presents a new path planning algorithm based on the long-short term memory (LSTM) neural network and traditional RRT. The LSTM-RRT algorithm first creates 2D and 3D environments and uses the traditional RRT algorithm to generate the robot path information, then uses the path information and environmental information to train the LSTM neural network. The trained network is able to promptly generate new path for randomly generated new environment. In addition, the length of the generated path is further reduced by geometric relationships. Hence, the proposed LSTM-RRT algorithm overcomes the shortcomings of the slow path generation and the low path quality using the traditional RRT method.

scholarly journals On the Enhancement of Optimality in the RRT-Connect Robot Path Planning Algorithm

2020 ◽  
Author(s):  
Jin-Gu Kang ◽  
Dong-Woo Lim ◽  
Yong-Sik Choi ◽  
Woo-Jin Jang ◽  
Jin-Woo Jung
Keyword(s):  
Path Planning ◽  
Experimental Results ◽  
Random Tree ◽  
Convergence Time ◽  
Robot Path Planning ◽  
Triangular Inequality ◽  
Planning Algorithm ◽  
Path Planning Algorithm ◽  
Robot Path

This paper proposed a Triangular Inequality based rewiring method for the RRT(Rapidly exploring Random Tree)-Connect robot path planning algorithm that guarantees the convergence time than the RRT algorithm, to enhance the optimality. To check the performance of the proposed algorithm, this paper compared with the RRT and RRT-Connect algorithms in various environments through simulation. From these experimental results, the proposed algorithm shows both quick convergence time and better optimality than the RRT algorithm, and more optimal than RRT-Connect algorithm with the similar number of sampling and convergence time.

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