Name
Abstract | ||
|---|---|---|
We propose a new sampling-based path planning algorithm, the Min-Max Rapidly Exploring Random Tree (MM-RRT*), for robot path planning under localization uncertainty. The projected growth of error in a robot's state estimate is curbed by minimizing the maximum state estimate uncertainty encountered on a path. The algorithm builds and maintains a tree that is shared in state space and belief space, with a single belief per robot state. Due to the fact that many states will share the same maximum uncertainty, resulting from a shared parent node, the algorithm uses secondary objective functions to break ties among neighboring nodes with identical maximum uncertainty. The algorithm offers a compelling alternative to sampling-based algorithms with additive cost representations of uncertainty, which will penalize high-precision navigation routes that are longer in duration. |
Year | Venue | Field |
|---|---|---|
2016 | 2016 IEEE 55TH CONFERENCE ON DECISION AND CONTROL (CDC) | Motion planning,Mathematical optimization,Rapidly exploring random tree,Computer science,Robot path planning,Sampling (statistics),Robot,State space |
DocType | ISSN | Citations |
Conference | 0743-1546 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Brendan Englot | 1 | 221 | 21.53 |
| Tixiao Shan | 2 | 13 | 4.33 |
| Shaunak D. Bopardikar | 3 | 1 | 5.48 |
| Alberto Speranzon | 4 | 332 | 30.26 |