Sobot Rimulator - A Robot Programming Tool

This project is an extension of the sobot rimulator developed by Nick McCrea which allows the simulation of a mobile robot in a 2-dimensional map of obstacles.

Main features

• Simulation of a mobile robot using proximity sensors to avoid obstacles
• Estimation of the robot pose and obstacle locations using the EKF SLAM or FastSLAM algorithms
• Concurrent evaluation of estimation accuracies
• Reproducible experiments by saving and loading maps
• Simulation controllable by a variety of parameters, further described here

This project could serve as an accessible introduction to robot programming and enable further research of simple robotics algorithms. For example, possible extensions to this simulator could be the estimation of a robots traversed path or an improvement of its navigation by using the map estimations of the SLAM system.

Setup

It is recommended to run the simulator directly on your native machine. For this, the following must be installed

• Python 3: Please follow the instructions at http://www.python.org/download/
• PyGTK 3: Please follow the instructions at https://pygobject.readthedocs.io/en/latest/getting_started.html

Additional dependencies can then be installed using pip

pip3 install matplotlib
pip3 install pyyaml

The simulator is then started by

python rimulator.py

which uses the default config.yaml configuration file. A specific configuration file can be specified as an additional program parameter:

python rimulator.py original_config.yaml

Alternatively, the simulator can be run using docker, as described in documentation/docker.md.

Graphical User Interface

The graphical user interface consists of the visualization of the current simulated world as well as a control panel of buttons with which the user can interact. The robot is depicted in blue and aims to reach its goal depicted in green while avoiding collisions with the red objects. The buttons of the control panel are:

Control row

• Play: Starts or continues the simulation
• Stop: Stops the simulation
• Step: Progresses the simulation by a single simulation cycle. If the simulation is running, it will first be stopped.
• Reset Reset the robot to its initial position

Map row

• Save map: Opens a directory window, in which filename and path can be specified in order to save the current map. The default directory is /maps, where multiple example maps are already stored.
• Load map: Opens a directory window, where a saved map can be selected to be loaded into the simulator.
• Random map: Generates a random map and resets the robot to the initial origin pose. Map generation parameters are specified in the configuration file.

Additional information row

• Show/Hide invisibles: The button lets the user toggle whether additional information shall be visualized. These include
  • The true trajectory of the robot
  • The poximity sensors, where those detecting an obstacles are highlighted
  • The vector that the robot is currently headed towards
  • Additionally, the color of this vector displays the current control state of the robot
    • A green vector represents the go to goal state
    • An orange vector represents the follow wall state
    • A red vector represents the avoid obstacles state
  • The true obstacle positions for the SLAM frames
• Plot covariance matrix: This plots a visualization of the current covariance matrix of the EKF SLAM algorithm. Only displayed if the EKF SLAM is enabled in the configuration.
• Plot Slam Evaluation: Plots a graph for every enabled SLAM algorithm displaying the accuracy of its estimations over the course of the simulation. Only displayed if the SLAM evaluation is enabled in the configuration.

Configuration

The simulator can be configured by a variety of parameters. The default configuration file is config.yaml, where all parameters are documented. The configuration file original_config.yaml does not include any of the extensions made and performs completely identical to the original sobot rimulator.

The most important parameters in terms of the SLAM algorithms are:

• motion_noise: Specifies the motion noise used by the algorithm, in my thesis denoted as R_t. The currently used motion noise is very low to reflect the very accurate motion of the simulated robot. Increasing the motion noise increases the region that the robot is estimated to be in.
• sensor_noise: Specifies the motion noise used by the algorithm, in my thesis denoted as Q_t. The currently used sensor noise is relatively high, so the robot currently rarely makes large modifications of its pose estimate based on sensor readings.
• distance_threshold: Specifies a threshold to be used for the data association. Decreasing this value will increase the frequency of the SLAM algorithm considering a landmark as "new" instead of associating it with an encountered landmark.
• The evaluation interval specifies the interval of simulation cycles after which the SLAM accuracy shall be evaluated. Low intervals can lead to performance problems. The SLAM evaluation can also be disabled entirely to further improve performance.

Other interesting parameters are:

• the map configuration paramters, such as amount and shape of obstacles. It is however recommended to perform a SLAM simulation with small, circular obstacles, which can be better represented by point-like landmarks.
• the robots control parameters, particularly the caution_distance. This parameter controls the robots transition into the follow wall state and has been significantly decreased to avoid the problem of the robot looping around the small circular objects. Using large, rectangular objects allows the usage of a larger value.

The robot parameters are based on the Khepera III research robot and should only be modified if you know what you are doing, since some parameter values are not fully supported. Particularly the amount of sensors and their placements are currently partially hard-coded in the source code.