Simulation with Societies of Behaviors

Badler, Norman I; Phillips, Cary B; Webber, Bonnie Lynn

doi:10.1093/oso/9780195073591.003.0005

Simulating Humans: Computer Graphics, Animation, and Control

Norman I Badler et al.

https://doi.org/10.1093/oso/9780195073591.001.0001

Published online:

12 November 2020

Published in print:

02 September 1993

Online ISBN:

9780197560273

Print ISBN:

9780195073591

- Title Pages
  - Notes
  - Notes
- Preface
  - Notes
  - Notes
1Chapter 1 Introduction and Historical Background
- Notes
- Notes
23Chapter 2 Body Modeling
- Notes
- Notes
67Chapter 3 Spatial Interaction
- Notes
- Notes
101Chapter 4 Behavioral Control
- Notes
- Notes
- 5.1 Forward Simulation with Behaviors 5.1 Forward Simulation with Behaviors
  - 5.1.1 The Simulation Model 5.1.1 The Simulation Model
  - 5.1.2 The Physical Execution Environment 5.1.2 The Physical Execution Environment
  - 5.1.3 Networks of Behaviors and Events 5.1.3 Networks of Behaviors and Events
  - 5.1.4 Interaction with Other Models 5.1.4 Interaction with Other Models
  - 5.1.5 The Simulator 5.1.5 The Simulator
    - The Class Hierarchy The Class Hierarchy
    - Objects, actions, and networks Objects, actions, and networks
  - 5.1.6 Implemented Behaviors 5.1.6 Implemented Behaviors
  - 5.1.7 Simple human motion control 5.1.7 Simple human motion control
- 5.2 Locomotion 5.2 Locomotion
  - 5.2.1 Kinematic Control 5.2.1 Kinematic Control
  - 5.2.2 Dynamic Control 5.2.2 Dynamic Control
  - 5.2.3 Curved Path Walking 5.2.3 Curved Path Walking
    - Specifying the Walk Specifying the Walk
    - Path of the Center Site Path of the Center Site
    - The Stance Leg The Stance Leg
    - The Swing Leg at the Double Stance Phase The Swing Leg at the Double Stance Phase
    - The Swing Leg at the Single Stance Phase The Swing Leg at the Single Stance Phase
  - 5.2.4 Examples 5.2.4 Examples
- 5.3 Strength Guided Motion 5.3 Strength Guided Motion
  - 5.3.1 Motion from Dynamics Simulation 5.3.1 Motion from Dynamics Simulation
  - 5.3.2 Incorporating Strength and Comfort into Motion 5.3.2 Incorporating Strength and Comfort into Motion
  - 5.3.3 Motion Control 5.3.3 Motion Control
    - Condition Monitor Condition Monitor
    - Path Planning Scheme Path Planning Scheme
    - Rate Control Process Rate Control Process
  - 5.3.4 Motion Strategies 5.3.4 Motion Strategies
    - Available Torque Available Torque
    - Reducing Moment Reducing Moment
    - Pull Back Pull Back
    - Added Joint, Recoil, and Jerk Added Joint, Recoil, and Jerk
  - 5.3.5 Selecting the Active Constraints 5.3.5 Selecting the Active Constraints
  - 5.3.6 Strength Guided Motion Examples 5.3.6 Strength Guided Motion Examples
  - 5.3.7 Evaluation of this Approach 5.3.7 Evaluation of this Approach
  - 5.3.8 Performance Graphs 5.3.8 Performance Graphs
  - 5.3.9 Coordinated Motion 5.3.9 Coordinated Motion
- 5.4 Collision-Free Path and Motion Planning 5.4 Collision-Free Path and Motion Planning
  - 5.4.1 Robotics Background 5.4.1 Robotics Background
  - 5.4.2 Using Cspace Groups 5.4.2 Using Cspace Groups
  - 5.4.3 The Basic Algorithm 5.4.3 The Basic Algorithm
  - 5.4.4 The Sequential Algorithm 5.4.4 The Sequential Algorithm
  - 5.4.5 The Control Algorithm 5.4.5 The Control Algorithm
  - 5.4.6 The Planar Algorithm 5.4.6 The Planar Algorithm
  - 5.4.7 Resolving Conflicts between Different Branches 5.4.7 Resolving Conflicts between Different Branches
  - 5.4.8 Playing Back the Free Path 5.4.8 Playing Back the Free Path
    - The Final Playback Algorithm The Final Playback Algorithm
    - The Single Branch Single Frame Playback Algorithm The Single Branch Single Frame Playback Algorithm
  - 5.4.9 Incorporating Strength Factors into the Planned Motion 5.4.9 Incorporating Strength Factors into the Planned Motion
  - 5.4.10 Examples 5.4.10 Examples
  - 5.4.11 Completeness and Complexity 5.4.11 Completeness and Complexity
- 5.5 Posture Planning 5.5 Posture Planning
  - 5.5.1 Functionally Relevant High-level Control Parameters 5.5.1 Functionally Relevant High-level Control Parameters
  - 5.5.2 Motions and Primitive Motions 5.5.2 Motions and Primitive Motions
  - 5.5.3 Motion Dependencies 5.5.3 Motion Dependencies
  - 5.5.4 The Control Structure of Posture Planning 5.5.4 The Control Structure of Posture Planning
  - 5.5.5 An Example of Posture Planning 5.5.5 An Example of Posture Planning
- Notes
- Notes
207Chapter 6 Task-Level Specifications
- Notes
- Notes
243Chapter 7 Epilogue
- Notes
- Notes
- Bibliography
  - Notes
  - Notes
- 267Index
  - Notes
  - Notes

Chapter

137Chapter 5 Simulation with Societies of Behaviors

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https://doi.org/10.1093/oso/9780195073591.003.0005

Pages

137–206

Published:

September 1993

Abstract

Recent research in autonomous robot construction and in computer graphics animation has found that a control architecture with networks of functional behaviors is far more successful for accomplishing real-world tasks than traditional methods. The high-level control and often the behaviors themselves are motivated lay the animal sciences, where the individual behaviors have the following properties: • they are grounded in perception. • they normally participate in directing an agent's effectors. • they may attempt to activate or deactivate one-auother. • each behavior by itself performs some task useful to the agent. In both robotics and animation there is a desire to control agents in environments, though in graphics both are simulated, and in both cases the move to the animal sciences is out of discontent with traditional methods. Computer animation researchers are discontent with direct kinematic control and are increasingly willing to sacrifice complete control for realism. Robotics researchers are reacting against the traditional symbolic reasoning approaches to control such as automatic planning or expert systems. Symbolic reasoning approaches are brittle and incapable of adapting to unexpected situations (both advantageous and disastrous). The approach taken is, more or less, to tightly couple sensors and effectors and to rely on what Brooks [Bro90] calls emergent behavior, where independent behaviors interact to achieve a more complicated behavior. From autonomous robot research this approach has been proposed under a variety of names including: subsumption architecture by [Bro86], reactive planning by [GL90, Kae90], situated activity by [AC87], and others. Of particular interest to us, however, are those motivated explicitly by animal behavior: new AI by Brooks [Bro90], emergent reflexive behavior by Anderson and Donath [AD90], and computational neuro-ethology by Beer, Chiel, and Sterling [BCS90]. The motivating observation behind all of these is that even very simple animals with far less computational power than a calculator can solve real world problems in path planning, motion control, and survivalist goal attainment, whereas a mobile robot equipped with sonar sensors, laser-range finders, and a radio-Ethernet connection to a, Prolog-based hierarchical planner on a supercomputer is helpless when faced with the unexpected.

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Contents

137Chapter 5 Simulation with Societies of Behaviors

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