Name
Abstract | ||
|---|---|---|
Autonomous navigation in real-world industrial environments is a challenging task in many respects. One of the key open challenges is fast planning and execution of trajectories to reach arbitrary target positions and orientations with high accuracy and precision, while taking into account non-holonomic vehicle constraints. In recent years, lattice-based motion planners have been successfully used to generate kinematically and kinodynamically feasible motions for non-holonomic vehicles. However, the discretized nature of these algorithms induces discontinuities in both state and control space of the obtained trajectories, resulting in a mismatch between the achieved and the target end pose of the vehicle. As endpose accuracy is critical for the successful loading and unloading of cargo in typical industrial applications, automatically planned paths have not been widely adopted in commercial AGV systems. The main contribution of this paper is a path smoothing approach, which builds on the output of a lattice-based motion planner to generate smooth drivable trajectories for non-holonomic industrial vehicles. The proposed approach is evaluated in several industrially relevant scenarios and found to be both fast (less than 2 s per vehicle trajectory) and accurate (end-point pose errors below 0.01 m in translation and 0.005 radians in orientation). |
Year | DOI | Venue |
|---|---|---|
2014 | 10.3390/robotics3040400 | ROBOTICS |
Keywords | Field | DocType |
motion planning, motion and path planning, autonomous navigation | Motion planning,Discretization,Computer vision,Classification of discontinuities,Lattice (order),Planner,Control engineering,Smoothing,Artificial intelligence,Engineering,Accuracy and precision,Trajectory | Journal |
Volume | Issue | ISSN |
3 | 4 | 2218-6581 |
Citations | PageRank | References |
2 | 0.42 | 15 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Henrik Andreasson | 1 | 544 | 38.96 |
| Jari Saarinen | 2 | 146 | 13.82 |
| Marcello Cirillo | 3 | 128 | 11.33 |
| Todor Stoyanov | 4 | 260 | 26.07 |
| Achim J. Lilienthal | 5 | 1468 | 113.18 |