Engineering topics
Byon, Amos
Publications and source records attributed to Byon, Amos.
Area Coverage Planning with 3-axis Steerable, 2D Framing Sensors
Existing algorithms for Agile Earth Observing Satellites((Lemaitre et al. 2002)) were largely created for 1D line sensors that acquire images in linear swaths. However, imaging satellites increasingly use 2D framing sensors (cameras) that capture discrete rectangular images. We describe tiling step-stare approaches that are more suited to rectangular image footprints than are 1D swath-based algorithms. Optimal area planning for these 2D framing instruments is an NPcomplete problem and intractable for large areas, so we present four approximation algorithms. Strategies are compared against a prior 2D framing instrument algorithm (Knight 2014) in three computational experiments. The impact of observer agility on schedule makespan is examined. Makespans vary more as observer agility decreases toward a critical point, then vary less after the critical point, suggesting a possible problem phase transition.
Algorithms for area coverage planning with 3-axis steerable, 2D framing sensors
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A Hybrid Traveling Salesman Problem - Squeaky Wheel Optimization Planner for Earth Observational Scheduling
We outline a hybrid planner for scheduling Observa- tion Requests on an Earth observing satellite, subject to a variety of constraints for the ASPEN (Chien et al. 2000) Eagle Eye adaptation (Knight, Donnellan, and Green 2013) that combines Squeaky Wheel Optimiza- tion (Joslin and Clements 1999) with sliding observa- tion planning (Aldinger et al. 2013). The Earth Ob- serving Satellite (EOS) planning problem (Globus et al. 2004) is reformulated as time-varying travel time TSP with interval constraints (Ichoua, Gendreau, and Potvin 2003). The replanning/ ll stage of the hybrid scheduler marginally improves schedule quality for all bus agilities examined, but has more impact on lower agility observers. The squeaky wheel stage primarily a ects overall schedule quality by satisfying high pri- ority requests, while the replanner reduces starvation of lower value requests.