Name
Title | ||
|---|---|---|
Neural Network Approximation Based Near-Optimal Motion Planning With Kinodynamic Constraints Using RRT. |
Abstract | ||
|---|---|---|
In this paper, the problem of near-optimal motion planning for vehicles with nonlinear dynamics in a clustered environment is considered. Based on rapidly exploring random trees (RRT), we propose an incremental sampling-based motion planning algorithm, i.e., near-optimal RRT (NoD-RRT). This algorithm aims to solve motion planning problems with nonlinear kinodynamic constraints. To achieve the cost... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TIE.2018.2816000 | IEEE Transactions on Industrial Electronics |
Keywords | Field | DocType |
Robots,Planning,Measurement,Artificial neural networks,Heuristic algorithms,Cost function,Trajectory | Motion planning,Random search,Mathematical optimization,Nonlinear system,Control theory,Sampling (statistics),Engineering,Artificial neural network,Robot,Trajectory,Configuration space | Journal |
Volume | Issue | ISSN |
65 | 11 | 0278-0046 |
Citations | PageRank | References |
9 | 0.51 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yang Li | 1 | 20 | 4.15 |
| Rongxin Cui | 2 | 330 | 14.59 |
| Zhijun Li | 3 | 939 | 91.73 |
| Demin Xu | 4 | 86 | 11.13 |