Biological Inspirations for Distributed Robotics
Dr. Daisy Tang Outline
Biological inspirations
Understand two types of biological parallels
Understand key ideas for distributed robotics obtained from study of biological systems
Understand concept of stigmergy
Understand use of stigmergy for tasks in collective robotics Biology vs. Multi-Robot Teams Movies of Some Animal Collectives
School of fish
http://www.youtube.com/watch?v=_tGOKngtkt4&feature=related
http://www.youtube.com/watch?v=TL8diH-I9EQ
Etc. Why Biological Systems?
Key reasons:
Animal behavior defines intelligence
Animal behavior provides existence proof that intelligence is achievable
Typical objects of study:
Ants
Bees
Birds
Fish
Herding animals A Broad Classification of Animal Societies (Tinbergen, 1953) Societies that Differentiate
Innate differentiation of blood relatives
Strict division of work and social interaction
Individuals:
Exist for the good of society
Are totally dependent on society
Examples:
Bees
Ants, termites
Stay Together A Typical Bee Colony Societies that Integrate
Depend on the attraction of individual animals
Exhibit loose division of labor
Individuals:
Integrate ways of behavior
Thrive on support provided by society
Are motivated by selfish interests
Examples:
Wolf, hunting dogs, etc.
Bird colonies
Come Together Parallels to Cooperative Robotics Which Approach To Choose?
Differentiating approach :
For tasks that require numerous repetitions of same activity over a fairly large area
Examples:
Waxing floor
Removing barnacles off ships
Collecting rock samples on Mars
Integrating approach :
For tasks that require several distinct subtasks
Examples:
Search and rescue
Security, surveillance, or reconnaissance Key Ideas from Biological Inspiration
Communication
Auditory, chemical, tactile, electrical
Direct, indirect, explicit, implicit
Roles
Strict division vs. loose “assignments”
Hierarchies
Absolute linear ordering, partial ordering, relative ordering
Purpose: reduction in fighting, efficiency
Territoriality
Reduces fighting, disperses group, simplifies interactions
Imitation
Complex mechanism for learning Our Distributed Robotics Studies
First : low-level, homogeneous, swarm robots
Swarming, dispersion, homing, etc.
Search/coverage
Etc.
Then : higher-level strategies, heterogeneous robots
Multi-robot path planning, traffic management
Task allocation
Etc. Key Concept in “Swarm” Distributed Robotics: Stigmergy
Stigmergy :
Term used by some biologists to describe influence on behavior due to persisting environmental effects of previous behavior
Originally used by French biologist Pierre-Paul Grasse to describe behavior of nest-building termites and trails
Equivalent concept: implicit communication by means of modifying the environment
A mechanism for binding task state information to local features of a task site, and for communicating (implicitly) by modifying those features
Stigmergy is a powerful tool for coordination in a loosely coupled system Stigmergy in Nature
Ant trails
Ants find the shortest path to a food source in their vicinity using stigmergy to maintain traffic statistics
Termite nest-building
Termites build columns and arches using stigmergy to retain state about the building process
Ant corpse-gathering
Ants pick up dead ants and drop them in piles, preferring larger piles, until there is only one pile left Ants Finding The Shortest Path
Ants follow random paths, influenced by presence of pheromones
Ants returning with food leave stronger trails
Pheromones evaporate, causing frequent trails to dominate
Shortcuts result in higher traffic (more trips per ant per unit time) and thus are selected with greater probability
http://www.youtube.com/watch?v=kN0M49iqFRc Termites Building An Arch
Termites make mud balls with pheromones
Termites deposit mud balls near existing pheromone concentrations
As columns get taller pheromones on the bottom evaporate
Pheromones on neighboring columns cause the top to be built together to form an arch
http://www.youtube.com/watch?v=0m7odGafpQU&feature=Pl ayList&p=598428DDC4E49D85&index=0&playnext=1 Ant Corpse-Gathering
Scattered corpses are picked up and dropped
Small piles form
Gradually the piles are aggregated into a single large pile How Does Stigmergy Produce Complex Patterns?
The state of the environment , and the current distribution of agents within it, determine how the environment and the distribution of agents will change in the future
Any structure emerging is a result of self- organization
Self-organization :
A set of dynamic mechanisms whereby structures appear at global level of a system resulting from interactions among lower-level components
Rules specifying interaction are executed purely based on local information, without reference to global pattern Minimal Qualities of Agent and Environment to Support Stigmergy
Agent has 2 key abilities :
It can move through environment
It can act on environment
To enable stigmergy:
Environment must be changed locally by agents
Changes must persist long enough to affect the choice, parameters, or consequences of agents’ behavior Two Ways to Structure Behavioral Sequences
In solitary species
Execution of first movement in sequences sets internal state
With external cue, internal state initiates second movement, etc.
In solitary and social insects
No internal state required (in many, but not all, cases)
External cue alone is sufficient to invoke subsequent actions
Sets the stage for stigmergy Compare Stigmergy to Direct Communication
Direct communication requires :
Sending robot to encode and transmit message about what is to be done, and where it is to be done
Implies knowledge of location
Message is local in time and space, therefore only robots close enough and not otherwise engaged will be free to receive the message
Robots must decode received messages, and remember them long enough to get to the place and carry out the action, or even longer if they are currently carrying out a more important task
Stigmergic communication :
Requires no encoding or decoding
Requires no knowledge of place
Requires no memory
Is not transient Example Use of Stigmergy in Collective Robotics
Paper references:
“Stigmergy, Self-Organization, and Sorting in Collective Robotics ”, by Holland and Melhuish, Artificial Life 5: 173-202, 1999.
“From Local Actions to Global Tasks: Stigmergy and Collective Robotics ”, by Beckers, Holland and Deneubourg in Brooks and Maes (eds.), Artificial Live IV: 181-189, Cambridge, MA: MIT Press, 1994. Collective Pile Formation Task
The robots
~20cm square base with two wheels and a gripper
Battery powered
Infrared (IR) sensors for obstacle detection
Gripper force sensor
Environment Square arena, about Beckers’ approach 2.5x2.5m
81 circular pucks (4cm) arranged on a 25cm grid The Pile Formation Experiment
The initial task given the robots was to push all the pucks into a single pile
At the start of an experiment, robots are in the center, oriented randomly
After each 10 minute interval, the robots are stopped and sizes and positions of clusters noted
Experiment ends when all pucks are in single cluster Robot Behaviors
Very simple set of 3 behaviors:
If IR sensor active: turn away from obstacle through a random angle
If force sensor active:
Force sensor triggered when 3 or more pucks are pushed
When sensor activates, pucks are dropped
Reverse both motors for one second
Then turn away to a random angle
Default: move forward until sensor activated Back to Experiment (Becker)
How it works?
Robots move around randomly
If they bump into a puck, they will push it along
When they bump into their third puck, they drop
Initially, all piles are of size 1
Robots will pick them up and will not deposit until they have collected 3 pucks
A pile of 3 or more tends to get bigger
Robots that hit a pile of 3 or more head-on will add their pucks to pile How do Piles Aggregate?
Initially, a few small clusters form quickly
Then, gradually those clusters are aggregated
This occurs when pucks are stripped from the edge of a pile and then deposited elsewhere
Large piles have a larger ratio of areas in the middle to those on the edge. Therefore probability of hitting tangent to pile decreases with increasing pile size
Thus larger piles have a larger probability of increasing as a result of this process Where is the Stigmergy?
By adding pucks to a pile, a robot makes the pile larger, and “votes” (implicitly) for that pile to be largest
This stigmergically encodes a message “this is the largest pile, add more pucks to it”
The strongest such message (i.e. the largest pile) wins and eventually accretes all the pucks
Because all state information is encoded in observed pile size, new robots can be added with no “communication overhead ” Experimental Results (Beckers)
The experiment was performed with varying numbers of robots
Adding robots sped convergence, up to 3 robots
Why?
More than three robots got in each others’ way (i.e., interference)
Whenever they turn to avoid each other, they run the risk of scattering a nearby pile as they turn away
Because the frequency of interactions increases with more robots, 3 was experimentally determined to be optimal
Interference is a function of robot density
Experimental Results (Cont.)
1. Over time, size of the biggest 1. Over time, # of clusters cluster grows shrinks 2. More robots faster cluster 2. More robots faster growth up to a point of robot reduction, up to a point of interference robot interference Experimental Results (Cont.)
For these experiments, 3 robots was optimal
Number of interactions increased significantly with number of robots
Robot efficiency for these experiments was optimized at 3 robots Summary of Stigmergy
Stigmergy piggybacks communication on top of robot’s existing sensing and actuation
Allows system to scale to additional robots with additional communication overhead
Although high densities can lead to gridlock, etc.
Stigmergy stores state in the environment so that it is easily retrieved by specialized sensors
In nature, pheromones
In robotics, variety of sensors
Stigmergy can be regarded as the general exploitation of the environment as external memory resource Second Case Study
Title
Multi-robot System Based on Model of Wolf Hunting Behavior to Emulate Wolf and Elk Interactions
Authors:
John D. Madden, Ronald C. Arkin and Daniel R. MacNulty
IEEE International Conference on Robotics and Biomimetics, 2010 Goal of the Project
Models of behavior from biology are used to develop heterogeneous unmanned network teams (HUNT)
The ability to reduce communication and planning requirements for robot groups, while still achieving missions
Mission: pursuit-evasion tasks Wolf Behavior from Nature
No obvious pattern of coordinated hunting behavior
Rules of thumb:
Attack while minimizing risk of injury with no overall had behavioral constraints on actions Transitions Model Other Transitions are Possible
Statistical observational data of state transitions Coordination or Lack Thereof
Wolves show no signs of planned strategies and no noticeable communication while hunting
They do not make transitions together
Coordination is a byproduct where each individual is maximizing its own utility
Seeing elk being chased signals a sign of weakness of the prey, so they join the pursuit Implementation of Wolf Behaviors with MissionLab @GaTech List of Releasers and Transitions
Weighted roulette wheel was used to decide which transition to take List of Behaviors List of Behaviors, Cont’d Elk Behaviors Simulation Results Transition Results Similar to Observed Data Conclusion
High fidelity bio models can provide utility for a range of multi-robot applications
Byproduct mutualism can provide robust results for bio groups
The ability to reduce communication and planning for robot groups Summary of Biological Inspirations
Study social biological systems either to:
Obtain inspiration for how to build multi-robot systems
Validate theoretical models for how biological systems work
Two types of biological parallels: differentiating and integrative
Many possible sources of inspiration from biology
Stigmergy is important concept for swarm cooperation “through the world”