unified optimal search search building example. search building 1.choose anw.location = window1,...

Unified optimal search

Search building example

Search Building1.Choose ANW.location = window1, window2// 2 windows broken out on different floors2. Fly to laboratoryA //gather vision data advance film, snapshot, camera = on3. Lower chem bots//4.Choose café or daycare //want to show backtrack to diff paths through diff plan5. If not daycare then daycare//5. Fly to labA6. Transmit msg to chem-bots7.Chembots ask if ANW1 = labA, then both go to rendzvous//focus on ANW1’s portion of the plan, fragment of rmpl code

(root USARteam ) ;; root object of RMPL program

(defdomain building_rooms (daycare cafeteria laboratory

window1 window2 office))

(defclass helicopter ()

(public state building_rooms location)

)

(defclass smallbot ()

(public state building_rooms loc)

)

;;Main Program(defclass USARteam () (public state helicopter ANW1) (public state smallbot chembots);; (Search_Building() (sequence

(choose ((ANW1.location = window1)[]) ((ANW1.location = window2)[])

)( parallel ((ANW1.location = laboratory)[]) ((ANW1.Take_Pictures(100))[] ))((ANW1.Lower_Chembots(50))[])(choose ((ANW1.location = daycare)[]) ((ANW1.location = cafeteria)[]) ((ANW1.location = office)[]))

;;(if-thennext(NOT(ANW1.location = daycare))

;; (( ANW1.location = daycare )[]);;)

);;end sequence );;end Search_Building);;end USARteam

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Search_Building() [ ]

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(ANW1= window1) [ ]

(ANW1 = window2)[ ]

(ANW1 = daycare)[ ]

(ANW1 = cafeteria)[ ]

(ANW1.TakePictures())[ ]

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A* on RRTPN

• A TPN with costs• Use algm from other slides• when 1st loc node is reached then it is

initialized with the SLD, on the arc before the

• then loc is expanded for a specific number of iterations, the

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1.Search Tree is initialized with S, and initial position (state) of ANW1(proapagated fwd)2. S refers to the start of parallel activities so each of its successor is added together, Sis completed and Closed3. G is expanded, completed and Closed4. A is expanded and creates two paths (CA) (BA), since both C and B begin a location constraintthen they are initialized with a SLD cost estimate.5. B grows an RRT for one step, then metric, m = dist of closest node/#iters is computed

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3 steps, 100 iters per stepinit:SLD/epsilon

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(ANW1 = daycare)[ ]



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4. A is expanded and creates two paths (CA) (BA), since both C and B begin a location constraintthen they are initialized with a SLD?? cost estimate.5. B grows an RRT (from current state) for one step, then metric, m = (#iters)/dist(n) is computed#iters = 100, dist = 35==> m= 100/35 = 2.9 (can scale it)B adds a step arc and node that represent the current RRT state, b16.B is expanded one step, #iters = 200, m = 200/20 = 107. C is expanded one step (really bad and finish off with B)

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show minimal partial expansion

show expansion with wall, #iters increases

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(ANW1 = window2)[ ]

(ANW1 = daycare)[ ]



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6.B is expanded one step, #iters = 200, m = 200/20 = 107. C is expanded one step (really bad and finish off with B)

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show minimal partial expansion

show expansion with wall, #iters increases

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ANW1 A

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Path-Planning State Space

Results

ok now

• just grow ‘em• just say there is a number of

optimazations that can be done to improve, take into account the #of iters

• step– step cost– max steps– can be optmized by preferring to step one

path over another

(ANW1.location = windowI)

Path-Planning SpaceANW1.start

ANW1.goal

Step Arc 1Step Node 1

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Search tree after expanding node A

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AND-node

AND-node

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start goal

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unified optimal search search building example. search building 1.choose anw.location = window1,...

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