Name
Abstract | ||
|---|---|---|
One way for a human and a robot to collaborate on a search task is for the human to specify constraints on the robot's path and then allow the robot to find an optimal path subject to these constraints. This paper presents an anytime solution to the robot's path-planning problem when the human specifies a path constraint and an acceptable amount of deviation from this path. The robot's objective is to maximize information gathered during the search subject to this constraint. We first discretize the path constraint and then convert the resulting problem into a multi-partite graph. Information maximization becomes a submodular orienteering problem on this topology structure. Backtracking is used to generate an efficient heuristic for solving this problem, and an expanding tree is used to facilitate an anytime algorithm. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/SMC.2014.6974170 | SMC |
Keywords | Field | DocType |
optimisation,backtracking,submodular orienteering problem,human path constraint,trees (mathematics),human-robot interaction,expanding tree,path planning,topology structure,information maximization,robot path-planning problem,informative path planning | Motion planning,Mathematical optimization,Any-angle path planning,Heuristic,Shortest path problem,Computer science,Submodular set function,Artificial intelligence,Anytime algorithm,Robot,Backtracking,Machine learning | Conference |
ISSN | Citations | PageRank |
1062-922X | 2 | 0.43 |
References | Authors | |
7 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daqing Yi | 1 | 15 | 4.31 |
| Michael A. Goodrich | 2 | 1738 | 171.30 |
| Kevin D. Seppi | 3 | 335 | 41.46 |