Name
Abstract | ||
|---|---|---|
We consider mobile robot navigation in dense human crowds. In particular, we explore two questions. Can we design a navigation algorithm that encourages humans to cooperate with a robot? Would such cooperation improve navigation performance? We address the first question by developing a probabilistic predictive model of cooperative collision avoidance and goal-oriented behavior by extending the interacting Gaussian processes approach to include multiple goals and stochastic movement duration. We answer the second question with an extensive quantitative study of robot navigation in dense human crowds (488 runs completed), specifically testing how cooperation models effect navigation performance. We find that the “multiple goal” interacting Gaussian processes algorithm performs comparably with human teleoperators in crowd densities near 1 person/m2, while a state of the art noncooperative planner exhibits unsafe behavior more than 3 times as often as this multiple goal extension, and more than twice as often as the basic interacting Gaussian processes. Furthermore, a reactive planner based on the widely used “dynamic window” approach fails for crowd densities above 0.55 people/m2. Based on these experimental results, and previous theoretical observations, we conclude that a cooperation model is important for safe and efficient robot navigation in dense human crowds. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICRA.2013.6630866 | Robotics and Automation |
Keywords | Field | DocType |
Gaussian processes,collision avoidance,cooperative systems,human-robot interaction,mobile robots,probability,stochastic processes,telerobotics,cooperation model testing,cooperative collision avoidance,dense human crowds,dynamic window approach,goal-oriented behavior,human teleoperators,mobile robot navigation,multiple goal interacting Gaussian process algorithm,navigation algorithm design,navigation performance improvement,probabilistic predictive model development,reactive planner,state of the art noncooperative planner,stochastic movement duration,unsafe behavior | Crowds,Control engineering,Human–computer interaction,Artificial intelligence,Gaussian process,Probabilistic logic,Human–robot interaction,Computer vision,Mobile robot navigation,Engineering,Robot,Telerobotics,Mobile robot | Conference |
ISSN | ISBN | Citations |
1050-4729 | 978-1-4673-5641-1 | 32 |
PageRank | References | Authors |
1.18 | 25 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peter Trautman | 1 | 143 | 5.60 |
| Jeremy Ma | 2 | 181 | 9.93 |
| Richard M. Murray | 3 | 12322 | 1223.70 |
| Andreas Krause | 4 | 5822 | 368.37 |