Name
Abstract | ||
|---|---|---|
An important problem in human-robot interaction is for a human to be able to tell the robot go to a particular location with instructions on how to get there or what to avoid on the way. This paper provides a solution to problems where the human wants the robot not only to optimize some objective but also to honor "soft" or "hard" topological constraints, i.e. "go quickly from A to B while avoiding C". The paper presents the HARRT* (homotopy-aware RRT*) algorithm, which is a computationally scalable algorithm that a robot can use to plan optimal paths subject to the information provided by the human. The paper provides a theoretic justification for the key property of the algorithm, proposes a heuristic for RRT*, and uses a set of simulation case studies of the resulting algorithm to make a case for why these properties are compatible with the requirements of human-robot interactive path-planning.
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Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/HRI.2016.7451763 | HRI |
Keywords | Field | DocType |
homotopy-aware RRT* algorithm,human-robot topological path planning,human-robot interaction,soft topological constraints,hard topological constraints,HARRT* algorithm | Motion planning,Topology,Heuristic,Simulation,Computer science,Scalable algorithms,Artificial intelligence,Homotopy,Robot,Human–robot interaction,Goto | Conference |
ISSN | ISBN | Citations |
2167-2121 | 978-1-4673-8370-7 | 4 |
PageRank | References | Authors |
0.45 | 13 | 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 |