Latest revision as of 22:22, 30 November 2009

The simulator is a part of the Swarm Robot Project that attempts to model the robots using MATLAB. It is built around a modular control structure so that it can be easily adapted to run other algorithms. Features include asynchronous update cycles, a packet model, lossy communication, and flexible rendering options.

Versions

There are now two versions of the simulator that implement different algorithms. They both use the same control structure, which this article outlines in context of the formation control algorithm.

Formation control

File: File:Swarm Robot Project Simulator.zip

Here the robots orient themselves so that their moments (quantifiers of the group's shape and position) match a predefined set of goal moments.

The swarm will start centered around a random point and then converge on the goal moments (shown as the stationary green ellipse). The user may click a location on the grid to restart the simulation with the robots centered around that point.

Environment sensing

File: File:Swarm Robot Project Simulator environment.zip

Here the robots are placed in an environment with up to 3 variables that are functions of location. The robots collaboratively model these variables and relocate themselves to get optimal sensor readings.

The swarm starts clustered at a random location in an environment with randomly chosen parameters. The three environment variables are displayed on top of each other as red, green, and blue. Click on an agent to open a plot of its error (a black field indicates perfect estimate of all colors). The environment function is defined in basefunction.m and it can be changed to anything.

For this application, the agents' states have three extra data fields:

bots
- state
  - Y information matrix
  - Tau information vector
  - sensorR sensor covariance

Getting started

To run the simulator:

Download one of the zip files above.
Extract the Simulator folder.
Open simulator.m and press Run.
When prompted, select "Change Directory".

Code organization

Each M-file is a MATLAB function. The central one is simulator.m. At the top of simulator.m, you will find some constants (denoted by all-caps) that make it easy to change the performance and rendering of the simulation without having to read into the code. See the inline comments for details.

Control structure

Control structure flowchart

The control structure represents the decentralized code that runs on the individual e-pucks. It is written modularly and without extraneous dependencies so it can be readily adapted to other algorithms.

The whole control structure is called from updateBotComputations. Information about neighbors flows to and from the consensus estimator via the packet model.

Blue boxes

The blue boxes from the flowchart to the right represent functions that are defined in their own files. They are the building blocks of the consensus-based swarm model.

Plant

The interface between the agent and the environment. For the formation control implementation, this models an e-puck following a velocity vector.

Sensors

Consolidates information from the environment into a form readable by Consensus Input and the Controller.

Consensus Estimator

A discrete-time PI estimator that uses packet structs for its input and output.

Consensus Input

Injects the agent's own information into the estimator-communication cycle. In the formation control implementation, this step causes the estimates to follow the agents instead of converging to the starting location.

Controller

Uses the agent's sensory and consensus information to select a set of controls, sent to the plant.

Collision avoidance

The velocity output from the Controller is put through a collision avoidance algorithm. It is not shown in the flowchart because the algorithm has access to the positions of all the agents (information not known to any single agent).

Note: the present algorithm does not work very well.

Environment

The environment is a set of abstractions that encapsulate the control structure, simulating passage of time, motion of the agents, and lossy wireless communication.

Time model

The simulation advances in timesteps; equally spaced spans of time. Timesteps are the unit used in specifying the distance between discrete events, such as updating robot calculations or rendering. Their size (relative to other time-based units) is defined by the constant dT.

The main loop runs once per timestep. It updates the plant every timestep and makes calls to time-dependent update functions as needed.

Data model

Since all state data in the simulation can be associated with a robot, the only stored variable is a vector of N robots called bots. Each bot is individually represented as a struct, with the following fields:

bots vector of N robots
- state information the agent knows about itself
  - p position
  - angle rotational orientation
- packets vector of N received packets (packet i of bot i is that agent's own estimate and always has an age of 0.)
  - age timesteps since received
  - v PI decision variable
  - w PI estimator state
- u velocity chosen by Controller
- lastUpdate timestep of last update
- waitTime timesteps between last update and next update

Packet model

Consensus information is transferred between the e-pucks using wireless communications. The simulator transfers packets between robots on an asynchronous basis and includes the possibility of packet loss as a function of distance. Each robot's packets vector contains the most recently received packet from each of its neighbors. A robot will transmit its own consensus information just before its control structure is executed.

Packet loss is modeled by a cumulative Rice function, as advised by The problem of Wi-Fi radio-fading simulation: Solution and applications. As distance between agents increases, the probability of receiving a packet decreases.

Visualizing packet loss

Packet loss can be easily visualized by setting the constant DRAW_CONNECTIONS to true.

Between two agents...

a black line means at least one agent has a fresh packet from the other.
a red line means neither has fresh data, but at least one is using stale data.
no line means neither agent is using data from the other because the last communicated packet is older than PACKET_AGE_LIMIT.

Recording a movie

To record a run of the simulation, set MOVIE_FRAME_COUNT to the desired movie length, in frames. The movie will be saved as "movie.avi" in the same directory as the simulator M-file.

Movies are recorded as uncompressed AVIs because MATLAB does a poor job of compression. See Swarm Robot Project Documentation#Making Videos with Overlays.

@@ Line 1: / Line 1: @@
-The simulator is a part of the [[Swarm Robot Project]] that attempts to model the robots using MATLAB.  It is built around a generic control structure so that it can be easily adapted to run other algorithms.  Features include: asynchronous update cycles, a packet model, lossy communication, and flexible rendering options.
+The simulator is a part of the [[Swarm Robot Project]] that attempts to model the robots using MATLAB.  It is built around a modular control structure so that it can be easily adapted to run other algorithms.  Features include asynchronous update cycles, a packet model, lossy communication, and flexible rendering options.
+=Versions=
-==Getting started==
+There are now two versions of the simulator that implement different algorithms.  They both use the same control structure, which this article outlines in context of the formation control algorithm.
-The simulator is contained in a single executable M-file.  This version implements the formation control algorithm.  When it is run, the swarm will start centered around a random location and then move in on the goal moments (the stationary green ellipse).  The user may click a location on the grid to restart the simulation with the robots centered around that point.
+==Formation control==
-==Constants==
+File: [[Image:Swarm_Robot_Project_Simulator.zip]]
-The constants at the top of the M-file (denoted by all-caps) give the user a great deal of control over the performance and rendering of the simulation without having to understand the code.
+Here the robots orient themselves so that their moments (quantifiers of the group's shape and position) match a predefined set of goal moments.
-See the inline comments for more details.
+The swarm will start centered around a random point and then converge on the goal moments (shown as the stationary green ellipse).  The user may click a location on the grid to restart the simulation with the robots centered around that point.
-==Control structure==
-[[Image:swarm_simlulator_control_structure.png|300px|right|thumb|Control structure flow chart.  Blue boxes represent functions that can be found in the code.]]
-The control structure is the logic portion of the code, representing code that might actually be run on the e-pucks.  It is written modularly and without extraneous dependencies so it can be readily adapted to other algorithms.
+==Environment sensing==
-The whole control structure is called from the ''updateBotComputations''.  Information about neighbors flows to and from the consensus estimator via the packet model.  Currently, no information from the environment is used by ''sensors''.
+File: [[Image:Swarm_Robot_Project_Simulator_environment.zip]]
+Here the robots are placed in an environment with up to 3 variables that are functions of location.  The robots collaboratively model these variables and relocate themselves to get optimal sensor readings.
+The swarm starts clustered at a random location in an environment with randomly chosen parameters.  The three environment variables are displayed on top of each other as red, green, and blue.  Click on an agent to open a plot of its error (a black field indicates perfect estimate of all colors).  The environment function is defined in ''basefunction.m'' and it can be changed to anything.
+For this application, the agents' states have three extra data fields:
+* '''bots'''
+** '''state'''
+*** '''Y''' information matrix
+*** '''Tau''' information vector
+*** '''sensorR''' sensor covariance
+=Getting started=
+To run the simulator:
+# Download one of the zip files above.
+# Extract the ''Simulator'' folder.
+# Open ''simulator.m'' and press Run.
+# When prompted, select "Change Directory".
+==Code organization==
+Each M-file is a MATLAB function.  The central one is ''simulator.m''.  At the top of ''simulator.m'', you will find some constants (denoted by all-caps) that make it easy to change the performance and rendering of the simulation without having to read into the code.  See the inline comments for details.
+=Control structure=
+[[Image:swarm_simulator_control_structure.png|300px|right|thumb|Control structure flowchart]]
+The control structure represents the decentralized code that runs on the individual e-pucks.  It is written modularly and without extraneous dependencies so it can be readily adapted to other algorithms.
+The whole control structure is called from ''updateBotComputations''.  Information about neighbors flows to and from the consensus estimator via the packet model.
+==Blue boxes==
+The blue boxes from the flowchart to the right represent functions that are defined in their own files.  They are the building blocks of the consensus-based swarm model.
+===Plant===
+The interface between the agent and the environment.  For the formation control implementation, this models an e-puck following a velocity vector.
+===Sensors===
+Consolidates information from the environment into a form readable by Consensus Input and the Controller.
+===Consensus Estimator===
+A discrete-time PI estimator that uses packet structs for its input and output.
+===Consensus Input===
+Injects the agent's own information into the estimator-communication cycle.  In the formation control implementation, this step causes the estimates to follow the agents instead of converging to the starting location.
+===Controller===
+Uses the agent's sensory and consensus information to select a set of controls, sent to the plant.
+<br clear=all>
+==Collision avoidance==
+The velocity output from the Controller is put through a collision avoidance algorithm.  It is not shown in the flowchart because the algorithm has access to the positions of all the agents (information not known to any single agent).
+Note: the present algorithm does not work very well.
+=Environment=
+The environment is a set of abstractions that encapsulate the control structure, simulating passage of time, motion of the agents, and lossy wireless communication.
 ==Time model==
-The simulation advances in ''timesteps''; equally spaced spans of time.  Timesteps are the unit used in specifying the distance between discrete events (ie. updating robot calculations or rendering).
+The simulation advances in ''timesteps''; equally spaced spans of time.  Timesteps are the unit used in specifying the distance between discrete events, such as updating robot calculations or rendering.  Their size (relative to other time-based units) is defined by the constant ''dT''.
-The main loop runs once per timestep.  It moves the robots along their chosen velocity vectors every timestep and makes calls to time-dependent update functions as needed.
+The main loop runs once per timestep.  It updates the plant every timestep and makes calls to time-dependent update functions as needed.
 ==Data model==
-Since all state data in the simulation can be associated with a robot, the only stored variable is a vector of N robots called ''bots''.  Each bot is individually represented as a structure, with the following fields (''and initial value''):
+Since all state data in the simulation can be associated with a robot, the only stored variable is a vector of N robots called ''bots''.  Each bot is individually represented as a struct, with the following fields:
 * '''bots''' vector of N robots
-** '''state''' information the robot knows about itself
+** '''state''' information the agent knows about itself
+*** '''p''' position
-*** '''p ([''rand''; ''rand''])''' position (arranged randomly inside a square of side length SIDE, centered about a specified point)
-*** '''angle (''rand'')''' direction facing
+*** '''angle''' rotational orientation
-** '''packets''' vector of N received packets (packet i of bot i is that robot's own estimate and does not age like the rest)
+** '''packets''' vector of N received packets (packet ''i'' of bot ''i'' is that agent's own estimate and always has an age of 0.)
-*** '''age (PACKET_AGE_LIMIT+1)''' timesteps since received
+*** '''age''' timesteps since received
+*** '''v''' PI decision variable
-*** '''v ([0; 0; 0; 0; 0])''' moments (packet i of bot i has v initialized to [p(1), p(2), 0, 0, 0])
-*** '''w ([0; 0; 0; 0; 0])''' I have no idea
+*** '''w''' PI estimator state
-** '''u ([0; 0])''' velocity
+** '''u''' velocity chosen by Controller
-** '''lastUpdate (0)''' timestep of last update
+** '''lastUpdate''' timestep of last update
-** '''waitTime (''rand'')''' timesteps between previous update and next update
+** '''waitTime''' timesteps between last update and next update
+==Packet model==
+Consensus information is transferred between the e-pucks using wireless communications.  The simulator transfers packets between robots on an asynchronous basis and includes the possibility of packet loss as a function of distance.  Each robot's ''packets'' vector contains the most recently received packet from each of its neighbors.  A robot will transmit its own consensus information just before its control structure is executed.
+Packet loss is modeled by a cumulative Rice function, as advised by [http://www.springerlink.com/content/u34264k58822l872/ The problem of Wi-Fi radio-fading simulation: Solution and applications].  As distance between agents increases, the probability of receiving a packet decreases.
+===Visualizing packet loss===
+Packet loss can be easily visualized by setting the constant DRAW_CONNECTIONS to true.
+Between two agents...
+* '''a black line''' means at least one agent has a fresh packet from the other.
+* '''a red line''' means neither has fresh data, but at least one is using stale data.
+* '''no line''' means neither agent is using data from the other because the last communicated packet is older than PACKET_AGE_LIMIT.
+=Recording a movie=
+To record a run of the simulation, set MOVIE_FRAME_COUNT to the desired movie length, in frames.  The movie will be saved as "movie.avi" in the same directory as the simulator M-file.
+Movies are recorded as uncompressed AVIs because MATLAB does a poor job of compression.  See [[Swarm Robot Project Documentation#Making Videos with Overlays]].
+[[Category:SwarmRobotProject]]

Difference between revisions of "Swarm Robot Project Simulator"