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| Content Provider | ACM Digital Library |
|---|---|
| Author | Goodrich, Michael A. Seppi, Kevin D. Yi, Daqing |
| 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. |
| Starting Page | 279 |
| Ending Page | 286 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781467383707 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2016-03-07 |
| Access Restriction | Subscribed |
| Subject Keyword | Homotopy Human-robot teaming Topology Interactive path-planning |
| Content Type | Text |
| Resource Type | Article |
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