Name
Abstract | ||
|---|---|---|
Spacecraft missions to small celestial bodies face sensitive, strongly non-Keplerian dynamics that motivate the employment of automated sampling-based trajectory planning. However, the scarcity of onboard computing resources necessitates careful formulation of heuristics for efficiently searching the reachable sets, which exhibit complex and finely-detailed structure. We examine a global search heuristic that combines aspects of simulated annealing and hill-climbing to locate sparse regions of the planning domain that simultaneously satisfy numerous geometric and timing constraints associated with remote sensing objectives for points of interest on the central body surface. Subsequently, we demonstrate the use of a receding-horizon implementation of this maneuver-planning strategy to produce mission profiles that fulfill sets of such goals. |
Year | Venue | Field |
|---|---|---|
2015 | International Conference on Automated Planning and Scheduling | Simulated annealing,Mathematical optimization,Heuristic,Scarcity,Trajectory optimization,Computer science,Heuristics,Sampling (statistics),Point of interest,Spacecraft |
DocType | ISSN | Citations |
Conference | 2334-0835 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| David Allen Surovik | 1 | 4 | 3.86 |
| Daniel J. Scheeres | 2 | 30 | 10.31 |