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Software Overview

The code can be found at Pupper GitHub Repository

Directory

├───models
│   └───agent.py
│   └───goal.py
│   └───obstacle.py
├───plots
│   └───map.png
├───src
│   └───boundary_detection.py
│   └───controller.py
│   └───fiducial_vision.py
│   └───geometry.py
│   └───main.py
│   └───node.py
│   └───path_finder.py
│   └───boundary_profiler.py
│   └───transforms.py
│   └───path_test.py
│   └───viz.py
├───params.yaml

Components Overview

Models

  • The models can be found at /models/
  • The obstacle, agent, and goal models are of type Shape which can be found within src/boundary_detection.py
  • The parameters of the models can be found within params.yaml
  • The goal model is a shape containing only a single point
  • The obstacle and agent models are defined by 4 corner points which are then interpolated to create a series of points defining the boundary of the model
  • Each model also has a center as well which would be it's relative location to the Pupper

Computer Vision

  • The computer vision module can be found at /src/fiducial_vision.py
  • The fiducial and computer vision package requires numerous parameters to work correctly, these include fiducial tag size, lens size, and center pixel of the image
  • The module itself contains the Vision class which contains a method capture_continuous which returns a generator which yields the results of the fiducial detection module on frames of the RaspberryPi camera

Boundary Generation and Navigation

  • The modules relevant to boundary generation and planning are src/boundary_detection.py, src/path_finder.py, /src/path_profiler.py, and path_test.py
  • Boundary generation works by taking in the detected positions and rotations of fiducials and then creating obstacle classes to represent each fiducial. Then each obstacle.points of type Point[] are transformed to it's corresponding fiducials location by the src/transform.py class. Then The Pupper robot model models/agent.py which is at (0, 0) and each obstacle are used to calculate the configuration space for robot using the Minkowski Sum
  • Also, the models/goal.pyclass is used to represent the goal of the Pupper which corresponds to the fiducial with id = 0
  • Each point in the resulting configuration space is used to generate a graph of the area where vertices of the graph close to the points in the configuration space are removed so that when a shortest path search is performed the resulting path only includes valid positions from the robot's current position to the goal
  • Finally, the path is interpolated and converted into an array of distances to travel and at what angle it should travel at, which is then converted into command interfaces commands based on the velocity of the robot

Visualization

  • The visualization class in src/viz.py uses matplotlib to generate a plot of the agent model, obstacles, obstacle boundaries, position of the goal, graph nodes, and the path of the robot

Main

  • The program can be run simply by navigating to the root of the repository and then running python3 main.py