Progress Review 8

December 9, 2022 rachelzh
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Progress

GUI

  • Mission control GUI implemented with PyQT5
  • Displays:
    • Agent status: active, battery, feedback frequency, IP
    • Task allocator visualization
    • High-level system metrics: # active agents, # tasks completed, # victims found
    • Mission timer
    • User controls: start, stop, homing
  • Integrating with system
    • Changed architecture of task allocator to be wrapped in new MissionCommander object
    • Getting battery, feedback frequency
    • Challenging figuring out PyQt GUI design quirks

Perception

  • Goal of the subsystem : Detect victims (dummied as apriltags) and report relative pose to the server for localisation
  • Runs on every robot in the fleet!
  • Able to detect apriltags at < 4m distance
  • Challenges:
    • Latency of image capture with sufficient resolution ~ 1sec
    • Mass deployment of software (like all our other software) isn’t possible in the fleet because of camera calibration
    • Associating apriltag detections with our dynamic pose graph! Perception x MrSLAM subsystem!

MRSLAM

  • Initial research on how to extend slam_toolbox to be able to do multi-robot SLAM
  • Understanding slam_toolbox codebase and open issues
  • Working prototype in Gazebo sim
    • Added capability of multiple lasers
    • Since robots will start in same room, they will know relative initial positions
    • Build single pose-graph for all agents
    • Visualization of built graph + constraints
    • Loop closure tests look successful

Navigation

Lazy Traffic Controller

  • Our new centralised multi-robot controller
  • Converts 2D paths into feasible collision free velocities for each robot in the fleet
  • Lazy:
    • Each robot has a narrow neighbourhood used for collision detection
    • Avoid collision checking unless something detected in this neighbourhood
    • Collision avoidance with velocity obstacles
  • Traffic:
    • Couple preferred velocities with attractive and repulsive forces to adopt swarm behaviours
  • Controller:
    • Generates velocities for the robots to execute!
  • Initial research into velocity obstacles
    • Model khepera robots as discs
    • Transform these discs to the velocity space for each robot : this is a velocity obstacle
    • Choose feasible velocities for each robot outside these velocity obstacles closest to the preferred velocities

Progress
Progress