On the training of a neural network for online path planning with offline path planning algorithms

Sung, Inkyung; Choi, Bongjun; Nielsen, Peter

doi:10.1016/j.ijinfomgt.2020.102142

Cited by 92 publications

(54 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Many environmental maps were considered and used to verify the performance of various path-planning algorithms including the RRT algorithm, [ 23 , 24 , 25 , 26 ]. Which environment map to use is important because the expected performance measure varies depending on the obstacles’ placement and shape among other properties.…”

Section: Resultsmentioning

confidence: 99%

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

Kang

Lim

Choi

et al. 2021

Sensors

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

confidence: 99%

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

Kang

Lim

Choi

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…This section introduces the environment map used in the simulation and the simulator used in the simulation with the computer's performance. Many environmental maps were considered and used to verify the performance of various pathplanning algorithms including the RRT algorithm, [23][24][25][26]. Which environment map to use is important because the expected performance measure varies depending on the obstacles' placement and shape among other properties.…”

Section: Experimental Environmentmentioning

confidence: 99%

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

Kang¹,

Lim²,

Choi³

et al. 2020

Preprint

View full text Add to dashboard Cite

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.

show abstract

“…And all maps are 600 (horizontal) * 600 (vertical) pixels. To verify the performance of various path planning algorithms including the RRT algorithm, many environmental maps were considered and used [22][23][24][25]. It is important which environment map to use because the expected performance measure varies depending on the placement, shape, and other property of obstacles.…”

Section: Experimental Environmentmentioning

confidence: 99%

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

Kang¹,

Lim²,

Choi³

et al. 2020

Preprint

View full text Add to dashboard Cite

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.

show abstract

On the training of a neural network for online path planning with offline path planning algorithms

Cited by 92 publications

References 20 publications

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

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

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

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

Contact Info

Product

Resources

About