Name
Abstract | ||
|---|---|---|
In this paper we introduce a novel algorithm for online distributed non-myopic task-selection in heterogeneous robotic teams. Our algorithm uses a temporal probabilistic representation that allows agents to evaluate their actions in the team’s joint action space while robots individually search their own action space. We use Monte-Carlo tree search to asymmetrically search through the robot’s individual action space while accounting for the probable future actions of their team members using the condensed temporal representation. This allows a distributed team of robots to non-myopically coordinate their actions in real-time. Our developed method can be applied across a wide range of tasks, robot team compositions, and reward functions. To evaluate our coordination method, we implemented it for a series of simulated and fielded hardware trials where we found that our coordination method is able to increase the cumulative team reward by a maximum of \(47.2\%\) in the simulated trials versus a distributed auction-based coordination. We also performed several outdoor hardware trials with a team of three quadcopters that increased the maximum cumulative reward by \(24.5\%\) versus a distributed auction-based coordination. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s10514-018-9811-9 | Auton. Robots |
Keywords | Field | DocType |
Heterogeneous robotic teams, Non-myopic coordination, Robotic planning, Robotic coordination, Robotic fielded hardware trials | Simulation,Computer science,Artificial intelligence,Probabilistic logic,Robot | Journal |
Volume | Issue | ISSN |
43 | 3 | 1573-7527 |
Citations | PageRank | References |
2 | 0.40 | 29 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| a k smith | 1 | 26 | 6.65 |
| Graeme Best | 2 | 39 | 6.02 |
| Javier Yu | 3 | 3 | 1.10 |
| Geoffrey A. Hollinger | 4 | 334 | 27.61 |