Paradigms of A.I. Robotics

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Magnitude Profile (a) Linear Drop Off (b) Exponential Drop Off Paradigms of A.I. Robotics – p.32/
Generating Potential Field Definition. Given S ⊂ R 2 , a vector field on S is a function F : S → R 2 . That is, F associates a vector to every point in S. FACT: Every potential field is a vector field, and most potential fields are conservative vector fields, i.e. they are (more or less) the gradient of potential energy functions. Remark. Think of potential energy functions as a landscape with hills and valleys and the potential field is the vector field generated by mapping the instantaneous change in each direction at every point in the landscape. FACT: Magnitude profiles define potential energy functions! I.e. Given a potential energy function, U : R 2 → R + , the potential field associated to U is F(⃗r) = −▽U(⃗r),⃗r ∈ R 2 . Paradigms of A.I. Robotics – p.33/
Page 1 and 2: Paradigms of A.I. Robotics Weiwei P
Page 3 and 4: Intelligent Robot Why do Robots Nee
Page 5 and 6: Primitive Functions of A.I. Robot
Page 7 and 8: Characteristics of H.P The Hierarch
Page 9 and 10: Pros and Cons of H.P Advantages: H
Page 11 and 12: The Reactive Paradigm Sense Act A r
Page 13 and 14: Characteristics of R.P 2. Overall b
Page 15 and 16: Architectures of R.P 1. Subsumption
Page 17 and 18: The Subsumption Architecture 2. Beh
Page 19 and 20: Subsumption Robot Robots built usin
Page 21 and 22: Following a Corridor Level 1: Explo
Page 23 and 24: Follow a Corridor LOOK middle STAY
Page 25 and 26: Toto the Robo Paradigms of A.I. Rob
Page 27 and 28: Primitive Potential Field (a) Unifo
Page 29 and 30: Movement via Potential Field The ro
Page 31: Magnitude Profile Question: How sho
Page 35 and 36: Following a Corridor Paradigms of A
Page 37 and 38: Robosoccer Paradigms of A.I. Roboti
Page 39 and 40: Difficulties with Potential Field P
Page 41 and 42: Summary of the Reactive Paradigm Po
Page 43: References [1] R. R. Murphy, Introd

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