networked robots ken goldberg, uc berkeley

berkeley automation sciences lab ieor and eecs depts

Telegarden ( )

networked robot:

tele-actor:

Networked robot cameras:

Frame Selection Problem: Given n requests, find optimal frame One Optimal Frame

Related Work Facility Location Problems –Megiddo and Supowit [84] –Eppstein [97] –Halperin et al. [02] Rectangle Fitting, Range Search, Range Sum, and Dominance Sum –Friesen and Chan [93] –Kapelio et al [95] –Mount et al [96] –Grossi and Italiano [99,00] –Agarwal and Erickson [99] –Zhang [02]

Related Work Similarity Measures –Kavraki [98] –Broder et al [98, 00] –Veltkamp and Hagedoorn [00] CSCW, Multimedia –Baecker [92], Meyers [96] –Kuzuoka et al [00] –Gasser [00], Hayes et al [01] –Shipman [99], Kerne [03], Li [01]

Problem Definition Assumptions –Camera has fixed aspect ratio: 4 x 3 –Candidate frame  = [x, y, z] t –(x, y)  R 2 (continuous set) – z  Z (discrete set) (x, y) 3z 4z

Problem Definition Requested frames :  i =[x i, y i, z i ], i=1,…,n

Problem Definition “Satisfaction” for user i: 0  S i  1 S i = 0 S i = 1  =    i  =  i

Symmetric Difference Intersection-Over-Union Similarity Metrics Nonlinear functions of (x,y)…

Intersection over Maximum: Requeste d frame  i, Area= a i Candidate frame  Area = a pipi

(for fixed z) 4z x 3z 4(z i -z) Satisfaction Function – s i (x,y) is a plateau One top plane Four side planes Quadratic surfaces at corners Critical boundaries: 4 horizontal, 4 vertical

Global Satisfaction: for fixed z Find  * = arg max S(  )

“Plateau” Vertices Intersection between boundaries –Self intersection: –Plateau intersection : y x

Line Sweeping Sweep horizontally: solve at each vertical boundary –Sort critical points along y axis: O(n log n) –1D problem at each vertical boundary O(n) –O(n) 1D problems –O(n 2 ) total runtime x

Continuous Resolution Version Lemma: At least one optimal frame has its corner at a virtual corner. –Align origin with each virtual corner, expand frame –O(n 2 ) Virtual corners –3D problem→ O(n 2 ) 1D sub problems r6r6 r2r2 r5r5 r3r3 x y r4r4 r1r1 O z S(z) Candidate frame Piecewise polynomial with n segments

Processing Zoom Type Complexity Centralized Discrete Exact O(n 2 ) Centralized Discrete Approx O(nk log(nk)), k=(log(1/ε)/ε) 2 Centralized Contin Exact O(n 3 ) Centralized Contin Approx O((n + 1/  3 ) log 2 n) Distributed Discrete Exact O(n), Client: O(n) Distributed Contin Approx O(n), Client O(1/  3 ) Frame Selection Algorithms

robotic video cameras Collaborative Observatories for Natural Environments (CONE) Dez Song (Texas A&M), Ken Goldberg (UC Berkeley) motion sensors timed checks sensor network s humans: amateurs and profs

Ivory Billed Woodpecker

Alpha Lab (UC Berkeley) Tiffany Shlain Dez Song (CS, Texas A&M) Jane McGonigal, Irene Chien, Kris Paulsen (UCB) Dana Plautz (Intel Research Lab, Oregon) Eric Paulos (Intel Research Lab, Berkeley) Judith Donath (Media Lab, MIT) Frank van der Stappen (CS, Utrecht) Vladlen Koltun (EECS, UC Stanford) George Bekey (CS, USC) Karl Bohringer (CS, UW) Anatoly Pashkevich (Informatics, Belarus) Thank you