Sandra Mau

Commentary on: “FUSE Planning and Scheduling Under One Wheel Attitude Control” : 

Commentary on: “FUSE Planning and Scheduling Under One Wheel Attitude Control” Sandra Mau Robotics Institute, Carnegie Mellon University

Problem: Manual Labour: 

Problem: Manual Labour Additional constraints imposed by the failure of reaction wheels Much of the scheduling is done manually Very time consuming: 4-5 days to produce the next weekly plan Seems like a full-time job!

Possible Alternative: Automation: 

Possible Alternative: Automation Observation: Scheduling is done offline The task set is reasonably small (for the short term planning) Problem is under constrained (many solutions) It seems like there may be an optimal solution that can be found through combinatorial optimization Difficulty: Some constraints are global and order dependant Many permutations possible and takes long to compute! Solution: Settle for a sub-optimal solution that is faster to calculate (as manual planners are currently doing.)

What’s currently done manually?: 

What’s currently done manually?

Possible Methods: 

Possible Methods Greedy scheduling using partially ordered list from Spike Search algorithm (i.e. DFS, DFID, BFS, heuristic search) with initial state of empty schedule and final goal of satisfiable schedule. Can use a heuristic such as priority to get more important tasks scheduled first. Both would require modification of short-term scheduler to work incrementally

Additional Questions: 

Additional Questions Spike is capable of taking into account quite a few of the constraints when it produces a schedule, including TA windows, beta and pole constraints, and hemisphere campaigns. What additional necessary considerations or constraints is it lacking? Can a modified short-term scheduler satisfy this? Cost-Benefit - time required now, time it takes to develop automated software, FUSE lifespan?

More Details?: 

More Details?

Greedy Scheduling : 

Greedy Scheduling Modified short term scheduling software keeps track of previous slew and momentum and calculates how new observation would affect new state Pop observation with desired +/- momentum from Spike list Spike’s 7-day list of partially ordered observations (START) Initially empty 7-day MPS list of ordered observations (GOAL: FILL UP) Slew & momentum still within bounds Return unscheduled observation to Spike list Compare to TA & momentum change plot tool to determine next desired momentum Append schedulable observation to MPS list NO YES ITERATE

Search Tree: 

Search Tree Similar steps to greedy but in a different order: Pop only feasible neighbours (tasks) at each iteration (checks before popping) DFID with a priority heuristic might work well to find a satisfiable solution for this under constrained problem