석사 디펜스 - KAISTsglab.kaist.ac.kr/thesis/GYLee12_slides.pdf · 2015-12-09 · 10 . Global...

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Transcript of 석사 디펜스 - KAISTsglab.kaist.ac.kr/thesis/GYLee12_slides.pdf · 2015-12-09 · 10 . Global...

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발표자
프레젠테이션 노트
석사 디펜스
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발표자
프레젠테이션 노트
Field: psychology, robotics, transport engineering, virtual environment, game, films
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Global Planning

• Find a path to the goal. Set the preferred velocity along the direction of the initial segment of the path.

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Global Planning

• Find a path to the goal. Set the preferred velocity along the direction of the initial segment of the path.

Local collision avoidance

• Steer the preferred velocity away from collision with other agents, yielding the actual velocity that the agent moves with.

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발표자
프레젠테이션 노트
International Conference on Internet Multimedia Computing and Service (ICIMCS) ACM SIGGRAPH / Eurographics Symposium on Computer Animation (SCA)
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발표자
프레젠테이션 노트
ACM SIGGRAPH / Eurographics Symposium on Computer Animation (SCA)
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RVO library

Self-consciousness theory

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Parameter Default value Range neighborDist 15.0 m 3 – 30 m

maxNeighbors 10 1 – 50

timeHorizon 10.0 s 1 – 30 s

radius 2.0 m 0.3 - 2.5 m

maxSpeed 2.0 m/s 1.2 - 2.2 m/s

affectNeighbor 3 0 – 10

escapeProbability 0.4 0 – 1

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1 2 3 4 5

𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝐷𝐷𝐷𝐷𝐷𝐷𝑆𝑆𝐷𝐷𝐷𝐷𝑆𝑆 𝐸𝐸𝐸𝐸𝐸𝐸

Range of parameter

Mean

?

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Danger 𝐸𝐸

Type of agent 𝐷𝐷(𝑃𝑃𝑒𝑒 ,𝑁𝑁𝑎𝑎) State

Multi-agent Simulation Planning

𝑃𝑃𝑒𝑒: 𝐸𝐸𝐸𝐸𝐸𝐸𝑆𝑆𝐸𝐸𝐷𝐷 𝑃𝑃𝑆𝑆𝑃𝑃𝑃𝑃𝑆𝑆𝑃𝑃𝑃𝑃𝐷𝐷𝑃𝑃𝑆𝑆𝑃𝑃 𝑁𝑁𝑎𝑎: Num of Affected Neighbor

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110.6

60.6

92.2

56.6

multipleagents

alone

Escape time

private agents public agents

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발표자
프레젠테이션 노트
사진 추가 동영상 나눠서 크게
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발표자
프레젠테이션 노트
사진 추가 동영상 나눠서 크게
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발표자
프레젠테이션 노트
사진 추가 동영상 나눠서 크게
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Ours PEN model

발표자
프레젠테이션 노트
사진 추가 동영상 나눠서 크게
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Public and Private Self-consciousness: Assessment and Theory. (A Fenigstein et al., CCP 1975)

Mean, STD, CORR

Psychology Paper Real Data (using )

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Low High

( Private SC, Public SC, Social Anxiety )

(H,H,H) : 8 (H,H,M) : 1 (H,H, L) : 3

(H,M,H) : 4 (H,M,M) : 2 (H,M, L) : 2

(H, L,H) : 1 (H, L,M) : 2 (H, L, L) : 2

(M,H,H) : 5 (M,H,M) : 2 (M,H, L) : 3

(M,M,H) : 8 (M,M,M) : 2 (M,M, L) : 6

(M, L,H) : 2 (M, L,M) : 2 (M, L, L) : 4

( L,H,H) : 2 ( L,H,M) : 2 ( L,H, L) : 0

( L,M,H) : 2 ( L,M,M) : 3 ( L,M, L) : 2

( L, L,H) : 1 ( L, L,M) : 5 ( L, L, L) : 3

Medium

Classification

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Mapping with RVO parameters

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