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Multi-Robot Communication
Dr. Daisy Tang
Objectives
� Understand key issues in multi-robot communication
� Understand impact of communication in Balch’s case study with Foraging, Consuming, and Grazing
� Understand how limited communication influences multi-robot coverage behaviors
Multi-Robot Communication
� Objective of communication:� Enable robots to exchange state and
environmental information with a minimum bandwidth requirement
� Issues of particular importance:� Information content
� Explicit vs. Implicit
� Local vs. Global
� Impact of bandwidth restrictions
� Medium: radio, IR, chemical scents, “breadscrumbs”, etc.
� Symbol grounding
Multi-Robot Communication Taxonomy
� By Dudek (1993)
� Communication range:� None, Near, Infinite
� Communication topology:� Broadcast, Addressed, Tree, Graph
� Communication bandwidth:� High (communication is essentially “free”)
� Motion-related (motion and communication costs are about the same)
� Low (communication costs are very high)
� Zero (no communication is available)
Nature of Communication
� One definition of communication:
� “An interaction whereby a signal is generated by an emitter and ‘interpreted’ by a receiver”
� Emission and reception may be separated in space and/or time
� Signaling and interpretation may be innate or learned
� Cooperative communication examples:
� Pheromones laid by ants foraging food
� Time delayed, innate
� Posturing by animals during conflicts/mating etc.
� Separate in space, learned with innate biases
� Writing
� Possibly separated in space & time, mostly learned with innate support
Explicit Communication
� Defined as those actions that have the express goal of transferring information from one to another
� Usually involves:� Intermittent requests
� Status information
� Update of sensory or model information
� Need to determine:� What / When / How / To whom to communicate
� Communications medium has significant impact� Range, bandwidth, rate of failure
Implicit Communication
� Defined as communication “through the world”
� Two primary types:
� Robot senses aspect of world that is a side-effect of another’s actions
� Robot senses another’s actions
Key Considerations in MR Communication
� Is communication needed at all?
� Over what range should communication be permitted?
� What should the information content be?
Is Communication Needed at All?
� Keep in mind:
� Communication is not free and can be unreliable
� In hostile environments, electronic countermeasures may be in effect
� Major roles of communication:
� Synchronization of action: ensuring coordination in task ordering
� Information exchange: sharing different information gained from different perspectives
� Negotiations: who does what?
� Many studies have shown:
� Significantly higher group performance using communication
� However, communication does not always need to be explicit
Over What Range?
� Tacit assumption: wider range is better
� But, not necessarily the case
� Studies have shown: higher communication range can lead to decreased societal performance
Information Content Can Be?
� Research studies have shown:
� Explicit communication improves performance significantly in tasks involving little implicit communication
� Communication is not essential in tasks that include implicit communication
� More complex communication strategies often offer little benefit over basic information
Paper Presentations
� “Communication in reactive multiagent robotic systems”, by Balch and Arkin, Autonomous Robots, 1994.
� “Tenacles: Self-Configuring Robotic Radio Networks in Unknown Environments”, by H. Chiu, et al., 2009.
Introduction
� How should design decisions be made?
� What type, speed, complexity and structure
� Three tasks were devised to discover the impacts of communication
� Performance can be compared across different tasks
� Factors: task, communication type, number of robots, number of attractors, mass of attractors, and percentage of obstacle coverage
Three Tasks: Forage, Consume and Graze
� Mass of attractor determines robot moving speed, consuming speed
� Several robots cooperating can increase the speed
� Size of swath that a robot can graze and the coverage percentage determines grazing time
Task Parameters
� Number of attractors
� Mass of attractors
� Graze coverage
FSAs
In simulation, Graze is implemented by maintaining and marking a high resolution grid corresponding to the environment.
Forms of Inter-Agent Communications
� No communication
� Robots are able to discriminate other robots, obstacles, and attractors
� State communication
� Robots communicate internal states (0: wander, 1: other)
� Behaviors are modified
� Goal communication
� Transmit goal-orientation information
� Behaviors modified accordingly
Explicit vs. Implicit Communication
� The implementation of goal and state communication requires explicit signaling and reception
� Internal states can be observed by other robots
� Robots can also communicate through environment
� Graze
Performance Metric
� Cost
� Reduce cost and minimize # of robots used
� Time
� Maximum # of robots operating without interference
� Energy
� Reliability and survivability
� Greater probability to completion at the expense of time or cost
Environment
� A static flat environment with randomly scattered obstacles
� No a priori map knowledge of obstacles’ location available
� Varied from 5% to 20%
� With 15% as a baseline
Time To Complete
Foraging: Improvement with Communication
Consuming: Improvement with Communication
More On Improvements
Physical Experiments
� Performed on 3 real robots
� Ren, Stimpy and George
� Ren and Stimpy are homogeneous
� Many real world limitations came into play
� Sensors not accurate
� Experimental results still followed simulated results
“Take Home” Message from Balch Communications Study”
� Communication improves performance significantly in tasks with little environmental communication (i.e., with little communication “through the world”, or no stigmergy)
� Communication is not essential in tasks which include implicit communication (i.e., communication “through the world”, or stigmergy)
� More complex communication strategies offer little or no benefit over low-level communication
Summary
� Many types:� Implicit vs. explicit
� Local vs. global
� Iconic vs. symbolic
� Proper approach to communication dependent upon application:� Communication availability
� Range of communication
� Bandwidth limitations
� Language of robots
� Etc.
Tenacles: Self-Configuring Robotic Radio Networks in Unknown Environments
By H. Chiu et al., IROS 2009
Presented by Chris Vigus
Background
� Unknown and dynamic environment
� Unknown locations and distances between agents
� Solution:
� Deploy a group of intelligent robots to explore the environment and position themselves to provide relays
� Robots must self-configure into an effective network, self-heal changes and damages, and adapt to movements of critical entities
Example Problem
Main Contribution
� A distributed coordination algorithm for self-organization and self-healing of robotic networks to establish radio links between critical entities despite the unpredictability and noise of radio signals and unknown locations of entities and relay nodes
� Assumption:
� Critical entities (non-robots) are stationary
Problem Description
� Si and Gi are critical entities to be connected (static)
� Nodes Ni are relay radios
� Goal:
� Nodes move to positions where they relay communication between G and S
� Approach:
� Growing tentacles
� Non-critical tentacles should be removed
Tentacles
� A tentacle consists of a series of stationary robotic radio nodes stretching out from entities
� Entities can communicate when tentacles meet one another
� Useless tentacles (carrying no communication traffic) can be freed and join new tentacle (by detecting radio signal strength)
� Nodes that are part of a tentacle or are relaying network traffic remain relatively stationary
Tentacle Building Algorithm
� Tentacle building
� Tentacle rebuild
� Radio guided exploration
� Local flow optimization
� Node structure:
� nodeID
� A tentacle array (ancestors up to an entity)
� distanceVector
� Boolean flag: tentacle or not
� A probe message is sent periodically to a node’s 1-hop neighbors
� Once a node ceases to receive new messages from its parent, it becomes free
Tentacle Building
� Incrementally connect free nodes to leaf of a tentacle
� A free node joins a tentacle based on:
� Good signal between node and leaf
� No branching. Tentacles may not branch. Multiple tentacles can grow from same entity if children are not within Good signal range
� Weak grandparents
� Avoid previous parents
Tentacle Rebuild
� Evaluating criticality of radio nodes and freeing up stationary nodes
� Non-critical:
� Not connected to other entities; or
� Not essential for carrying traffic between entities
� Closed vs. Open nodes
� Closed: on shortest route from one entity to another
� Determined by distance vectors
� A closed node has its distance vector with at least 2 entries and not dominated
� A rebuild message is initiated from an open leaf and propagates to all ancestors
� A closed node will stop the propagation while an open node
will forward
Radio Guided Exploration
� Goal: move a free node from any region to the boundary of region 4 & 5
Local Flow Optimization
� Local flow optimization mode (LocalOpt) is activated for a node with a low traffic flow
� The node goes forward for a fixed distance with one random direction
� No two neighboring nodes will be in the LocalOpt mode at the same time
Simulation
� Radio signal strength decreases over distance based on an inverse square model
� Strength is fractioned when it penetrates a wall
Simulation Results
� Three initial condition:
� Excellent: radio nodes connect source to gateway with Good link quality
� Fair: source and gateway are initially disconnected and only one radio node next to each entity
� Poor: same as Fair condition, but radio nodes are far away from gateway entity
Self-Healing in Simulation
Experiments on Real Robots
� iCreate platform
� Built-in wall following behavior for exploration
� 7-robot radio network
� 30mx40m environment
Snapshot
Summary
� A bio-inspired tentacle algorithm to stretch tentacle from stationary entities to connect another entity by incrementally connecting radio nodes to leaf of tentacle
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