Test Plan

Full Test Plan for Fall

Fall Validation

To demonstrate the entire navigation system on both the locobot and A1-Unitree
RequirementsM.F.R.1, M.F.R.3, M.N.F.R.1, M.N.F.R.2, M.N.F.R.4
ElementsSubsystems – SLAM, Planning, UI and Data Management (if present)
LocationNSH A Level
EquipmentLocobot, A1-Unitree, and Base Station (if using the Data Management block)
PersonnelAll 4 team members
  • A map of the testing area that has been pre-built using the SLAM subsystem will be loaded
  • The instructors will pick a start and goal location
  • The wheeled robot will be placed at the start location
  • All subsystems will be started
  • Run the navigation stack, continuously visualizing the global and local path, and the robot position
  • Robot reaches the goal location
  • Repeat the above steps for the legged robot
  • The planner adapts to different obstacle maps and varying robot parameters and generates a path that does not violate the following capability limitations:
    • Freely Steppable Object Height* (FSOH) – The robot can easily step over the object of height below this threshold.
    • Robot Radius – The radius of the circular approximation of the robot
  • The robots are able to follow the generated plan and follow it without crashing or colliding with obstacles
  • The UI and visualization should work as intended

Full Test Plan for Spring

Spring Validation

To demonstrate the progress made over the course of this semester in our Hardware Agnostic Path Planner and SLAM subsystem.
RequirementsM.F.R.2, M.F.R.3, M.F.R.1, M.N.F.R.1, M.N.F.R.4
ElementsSubsystems – SLAM, Planning
EquipmentBase Station, Locobot with an onboard computer (Nvidia Xavier), Realsense D435.
PersonnelAll 4 team members
  • Locobot is placed at the start of a predefined trajectory that it will follow using teleoperation.
  • The SLAM subsystem is started, and we move the robot along the predefined trajectory
  • The SLAM subsystem would generate the map incrementally, which will be visualized on the ground station computer in real-time.
  • Additionally, we will visualize the trajectory calculated by the SLAM subsystem’s localization module, alongside the ground-truth trajectory on the ground station computer in real-time
  • At the end of the trajectory, we will also calculate and show the relative pose error in translation and rotation
  • We will have a set of predefined obstacle maps in a simulator/visualizer
  • We will also have a set of robot parameters representing different types of robots.
  • We will run our hardware-agnostic path planner with combinations of these obstacle maps and robot parameters
  • We will then visualize the path generated by the planner for each of these combinations
  • The SLAM subsystem would generate an HD map of the area that the robot covers.
  • The localization module’s relative translational pose error is less than 15 cm, and the relative rotational pose error is less than 10 degrees
  • The planner adapts to changes in the obstacle map and the robot parameters and generates a path that leverages each robot’s capabilities.