Project Management

Schedules

Gantt Chart

Gantt chart for fall semester showing schedules for work packages

Presenters

Spring

Sensors and Motors Control Lab: Dan Bronstein

Progress Review 1: Ben Kolligs

Progress Review 2: Kelvin Kang

Progress Review 3: Jacqueline Liao

Progress Review 4: Dan Bronstein

Fall

Progress Review 7: Dan Bronstein

Progress Review 8: Kelvin Kang

Progress Review 9: Ben Kolligs

Progress Review 10: Jacqueline Liao

Progress Review 11: Dan Bronstein

Test Plans

Spring Validation

Graphical depiction of spring validation setup. Each number corresponds to the same step in “Procedure” listed below.

Goal:

Showcase our complete hardware along with the basic functionalities of our control, situation awareness, and human detection subsystems in a well-lit and smokeless room.

Location:

NSH Basement, CMU, Pittsburgh, PA

Environment:

A well-lit room no smaller than 10m² with no smoke in the room.

Equipment:

Dragoon, visualizer, WiFi network, indoor room, room geometry/obstacle proxies(cardboard boxes, furniture, trash bins etc.), human body, external light source

Demonstrations:

1. Power and Startup
   • Procedure: Operator turns on robot and visualizer, then sets robot at the entrance to the room
   • Validation:
     • The robot connects to the remote control
     • Jetson AGX turns on and connects to RealSense and Seek 
     • RealSense depth and RGB, Seek Thermal streams operational and stream to AGX
     • VLP-16 starts scanning, Cartographer begins mapping using VLP-16 and RealSense IMU
     • Visualizer turns on and begins to display 2D map and RGB stream at 10Hz (M.P.4)
2. Locomotion
   • Procedure: Operator moves robot forwards, backwards, using remote control at a max speed of 0.5 m/s and can turn about its location
   • Validation:
     • Robot moves forwards, backwards, at a min of 0.5 m/s and turns in place at a speed of 18 degrees per second (M.P.5)
3. SLAM
   • Procedure: Operator moves robot at a max speed of 0.2 m/s through the obstacles in the room for at least 5 minutes using the 2D map and RGB stream displayed on the visualizer
   • Validation:
     • 2D map of room for up to 10m from the robot is displayed and updated on visualizer; the shape of major obstacles are captured in the map (M.P.3)
     • The position of the robot is displayed on a 2D map on the visualizer
4. Basic Human Detection
   • Procedure: Robot detects and localizes human
   • Validation:
     • The robot detects and localizes human 8m away within 5 seconds of complete, unobstructed entrance into the RGB and Seek FOV with 75% accuracy (M.P.0, M.P.1)
     • The location of the human is displayed on a 2D map on the visualizer. Check that the centroidal accuracy of the bounding box is within 1 foot relative to the mapped room geometry

Fall Validation

Goal:

Demonstrate that Dragoon can autonomously navigate and operate in a dimly lit room and is able to determine the presence and location of a partially occluded human, which is displayed real-time on a 2D map. 

Location:

NSH Basement, CMU, Pittsburgh, PA

Environment:

A dimly-lit room no smaller than 10m².

Equipment:

Dragoon, visualizer, WiFi network, indoor room, room geometry/obstacle proxies(cardboard boxes, furniture, trash bins etc.), human body, luxmeter

Demonstrations:

1. Human Detection and Localization Test
   • Procedure:
     • A human subject is placed in a supine position 3-7m directly in front of the robot, in profile view
     • An occluding obstacle is placed blocking 0-25% of the subject
     • The location of the detected human on the visualized map is noted
     • Steps i-iii are repeated with varying quality of occlusion, subject pose, distance from the robot and level of ambient lighting
   • Validation:
     • Robot detects and localizes human who is at maximum 25% occluded 3m away in minimum 150 lux low-visibility lighting (M.P.1, M.P.2, M.N.0)
     • The robot detects and localizes non-occluded humans 7m away (M.P.1, M.P.2, M.N.0)
     • Human detected in real-time
     • Accurate location (within 0.5m radius) of human(s) displayed on 2D map on visualizer in real-time (M.P.3, M.F.3)
2. Autonomous Navigation Test
   • Procedure:
     • Place robot in a simulated disaster scenario and let it generate paths
     • Command the robot to follow a generated path
   • Validation:
     • Robot plans paths that avoid obstacles and generates goals in unexplored territory
     • Robot follows the generated path
3. Autonomous Search and Rescue Test:
   • Procedure:
     • Place robot in a simulated 30m² disaster scenario, with 2 human ‘victims’, and give the robot a signal to begin autonomous exploration
   • Validation:
     • Robot gives signal that exploration is done within 5 minutes (M.P.8, M.F.7)
     • RGB stream and 2D map of room for up to 10m from the robot is displayed and updated on visualizer; the shape of major obstacles are captured in the map (M.P.2, M.P.3, M.N.0)
     • 2D map is displayed and visualized in real-time (M.P.3)
     • Validation of the Human Detection and Localization Test for both victims

Fall Progress Review Milestones

1. PR 1 (9/15)
   • IR human detection Integrated into pipeline
   • Local planner/obstacle avoidance implemented
2. PR 2 (9/29)
   • DevOps for retraining RGB and IR networks complete
   • Architecture for global planner complete
3. PR 3 (10/13)
   • Global planner implementation complete
   • Initial retraining of RGB and IR networks complete
4. PR 4 (10/27)
   • Global and local planners integrated with each other
   • Retraining of weights for supine positions and dark conditions complete
5. PR 5 (11/10)
   • Entire planning stack integrated with full system, including human machine interface
   • Retraining of weights finalized and integrated with full system

Full Fall Semester Test Plan (external link)

Parts List

HOWDE Parts List (external link)

Issues Log

HOWDE Issues Log (external link)