Name
Abstract | ||
|---|---|---|
Reliable real-time planning for robots is essential in today's rapidly expanding automated ecosystem. In such environments, traditional methods that plan by relaxing constraints become unreliable or slow-down for kinematically constrained robots. This paper describes the algorithm Dynamic Motion Planning Networks (Dynamic MPNet), an extension to Motion Planning Networks, for non-holonomic robots that address the challenge of real-time motion planning using a neural planning approach. We propose modifications to the training and planning networks that make it possible for real-time planning while improving the data efficiency of training and trained models' generalizability. We evaluate our model in simulation for planning tasks for a non-holonomic robot. We also demonstrate experimental results for an indoor navigation task using a Dubins car. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/IROS45743.2020.9341283 | IROS |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jacob J. Johnson | 1 | 2 | 3.50 |
| Linjun Li | 2 | 1 | 0.69 |
| Fei Liu | 3 | 0 | 0.34 |
| Ahmed Hussain Qureshi | 4 | 54 | 8.83 |
| Michael C. Yip | 5 | 140 | 24.72 |