Chapter 40 Springer Handbook of Robotics, ©2008 Presented by:Shawn Kristek

1. Task Complexity 2. Distributed tasks 3. Difficult to build ultimate robot 4. Parallelism 5. Robustness through redundancy

 Architectures  Communication  Variance  Task Allocation  Learning  Applications

 Architectures  Communication  Variance  Task Allocation  Learning  Applications

o Centralized o Hierarchical o Decentralized o Hybrid

Centralized  Single point of control Works best when controller oversees other robots X Vulnerable to single robot failure X Real-time difficulties due to communication requirements

Hierarchical  Similar to military command More resistant to single robot failures X Vulnerable to upper-level single robot failure

Decentralized  Most common  Each robot’s actions based on localized data Robust to single robot failure X Global coherency difficult -Incorporated high-level goals difficult to revise Matarić

Hybrid  Combinations of other architectures Advantages of levels of control and localized control -Robust to failures -Global coherency DIRA

Implementations  The NERD Herd  ALLIANCE  DIRA

Implementations: The NERD Herd Matarić  Decentralized  Stigmergic  Swarm robots  Homogeneous: 20 identical robots  Behavior-based  Applications -Foraging & Coverage -Flocking & Formations The NERD Herd

Implementations: ALLIANCE Parker  Decentralized  Minimal explicit communication  Heterogeneity possible  Behavior-based - Uses motivations  Applications -Box pushing & Cooperative Manipulation -Multitarget observation ALLIANCE

Implementations: DIRA DI stributed R obot A rchitecture Simmons  Hybrid  Explicit communication  Heterogeneity possible  Applications - Cooperative Manipulation DIRA

 Architectures  Communication  Variance  Task Allocation  Learning  Applications

o Stigmergy o Passive action recognition o Explicit

Stigmergy  Sense through the world Simple No communication channels & protocols X Limited by robot’s perception Melhuish and Holland

Passive action recognition  Communication through observation No limited bandwidth No fallible mechanism X Limited by robot’s perception X Difficult to analyze actions

Explicit  Direct communication -Synchronize actions -Exchange information -Negotiate Directness Ease of acquiring knowledge of teammates X Noisy, limited-bandwidth channel

 Architectures  Communication  Variance  Task Allocation  Learning  Applications

o Swarm Robots – Homogeneous o Heterogeneous

Swarm Robotics Collective robotics  Typically homogeneous  Biologically inspired -Ants -Bees  Stigmergic  Redundant

Heterogeneous More realistic: - Heterogeneity may emerge in homogeneous systems Provides various capabilities Can reduce costs X Unavoidable Parker Grabowski

 Architectures  Communication  Variance  Task Allocation  Learning  Applications

o Taxonomy o Approaches

What is it?  Efficiently assign tasks  Team goal defined as set of tasks  Each task can be subdivided Goal

Taxonomy Gerkey and Matarić  Tasks -SR : Single-robot task -MR: Multirobot task  Robots -ST: Single-task robot -MT: Multitask robot  Allocation Optimization - IA: Instantaneous Assignment -TA: Time-extended Assignment  SR-ST-IA

Approaches  Behavior-Based  Market-Based

Behavior-Based  Decentralized architecture  Avoids explicit communication  Task Allocation -Current state -Teammate capabilities

Market-Based  Negotiation/bidding based  Greedily assigned to robot with highest utility  Most focus on SR-ST-IA/TA  Centralized or Hybrid architecture  Explicit communication  M+ architecture of Botelho and Alami -First for multirobot -Individual plans merged for team benefit

 Architectures  Communication  Variance  Task Allocation  Learning  Applications

 Many difficulties -Exponential state spaces -Limited training time -Insufficient data -Uncertainty -Merging information  Applied Applications -Multitarget observation -Box pushing -Multirobot soccer  Techniques -Reinforcement -Parameter tuning -Particle swarm optimization

 Architectures  Communication  Variance  Task Allocation  Learning  Applications

o Foraging & Coverage o Flocking & Formations o Box Pushing & Cooperative Manipulation o Multitarget Observation o Traffic Control & Multirobot Path Planning o Soccer

Foraging & Coverage

Flocking & Formations Formation Control

Box Pushing & Cooperative Manipulation Kube

Multitarget Observation Spletzer and Taylor

Traffic Control & Multirobot Path Planning Bruce and Veloso

Soccer Bruce and Veloso RoboCup