Name
Abstract | ||
|---|---|---|
Dead reckoning error is a common problem in robotics that can be caused by multiple factors related to sensors or actuators. These errors potentially cause landmarks recorded by a robot to appear in a different location with respect to the actual position of the object. In a foraging scenario with a swarm of robots, this error will ultimately lead to the robots being unable to return successfully to the food source. In order to address this issue, we propose a computationally low-cost finite state machine strategy with which robots divide the total travelling distance into a variable number of segments, thus decreasing accumulated dead-reckoning error. The distance travelled by each robot changes according to the success and failure of exploration. Our approach is more flexible than using a previously used fixed size approach for the travel distance, thus allowing swarms greater flexibility and scaling to larger areas of operation. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-44427-7_10 | SWARM INTELLIGENCE |
Keywords | Field | DocType |
Swarm robotics,Task partitioning,Fault tolerance,Foraging | Mathematical optimization,Swarm behaviour,Computer science,Finite-state machine,Real-time computing,Dead reckoning,Fault tolerance,Artificial intelligence,Robot,Foraging,Robotics,Swarm robotics | Conference |
Volume | ISSN | Citations |
9882 | 0302-9743 | 3 |
PageRank | References | Authors |
0.46 | 10 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Edgar Buchanan | 1 | 7 | 1.87 |
| Andrew Pomfret | 2 | 7 | 1.97 |
| Jon Timmis | 3 | 1237 | 120.32 |