Konstantin Yakovlev, Anton Andreychuk, Roni Stern |
PosterID:
17
PDF
Poster
BibTeX
|
| Safe-interval path planning (SIPP) is a powerful approach for finding a path in the presence of dynamic obstacles and continuous time. SIPP is based on the A* algorithm and returns provably optimal solutions. However, in many practical applications of SIPP such as path planning for robots, one would like to trade-off solution cost optimality for shorter planning time. In this paper we explore different ways to build a bounded-suboptimal SIPP and discusses their pros and cons. We compare the different bounded-suboptimal versions of SIPP experimentally. While there is no universal winner, the results provide insights into when each method should be used. | |
| Canb | 10/28/2020, 00:00 – 01:00 |
| 10/29/2020, 17:00 – 18:00 |
| Paris | 10/27/2020, 14:00 – 15:00 |
| 10/29/2020, 07:00 – 08:00 |
| NYC | 10/27/2020, 09:00 – 10:00 |
| 10/29/2020, 02:00 – 03:00 |
| LA | 10/27/2020, 06:00 – 07:00 |
| 10/28/2020, 23:00 – 00:00 |
Revisiting Bounded-Suboptimal Safe Interval Path Planning
Konstantin Yakovlev, Anton Andreychuk, Roni Stern
Solving the Watchman Route Problem on a Grid with Heuristic Search
Shawn Seiref, Tamir Jaffey, Margarita Lopatin, Ariel Felner
Solving the Longest Simple Path Problem with Heuristic Search
Yossi Cohen, Roni Stern, Ariel Felner
Convex Hull Monte-Carlo Tree-Search
Michael Painter, Bruno Lacerda, Nick Hawes