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Object Avoidance Milestones:

  1. Bootcamp and onboarding

  2. LIDAR LiDAR working - able to read and visualize outputs

  3. Get drone to stop on autonomous mission if obstacle is detected and return if obstacle is no longer detected

  4. Find path deflection algorithm in auto mode

  5. Implement path deflection algorithm in auto mode

  6. Get drone to stop on manual mode if obstacle is detected and return

  7. Path deflection in manual mode (not just stop, but stop the drone from crashing as pilot moves around the obstacle)

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Dates

Milestones

Weekly Task

Notes

1

May 6

Milestone 1

Bootcamp

  • Go through documentation on multiprocessing

2

May 13

Milestone 2

lidar LiDAR testing and visualization

  • Read output from lidarLiDAR

  • Create a 2D map from lidar LiDAR data

  • Indicate if an object is too close (within a certain bound)

3

May 20

Flight Interface

  • Good starter task, architecture will be very similar to airside’s flight interface worker

  • Gets telemetry data and constantly updates the current state of the drone

*Note: comp week

4

May 27

Detection and Decision

Detection (doesn’t have to have a class, depending on how complex the lidar LiDAR is)

  • Worker constantly scan with the lidar LiDAR and creates data struct

  • Passes data struct into output queue

Decision

  • Takes LIDAR LiDAR data and decides whether or not the drone should stop

  • If it should, passes command to output queue

  • If stopped, decide if it should go back to the mission

5

Jun 3

Milestone 3

Commands with Flight Interface

  • Add input queue to handle Detection Worker output

  • Reads from queue and calls upload command to stop the drone, or return the drone to the mission

6

Jun 10

Research Deflection Algos

Potential Algorithms:

  • Bendy Ruler

  • Vector Field Histogram

  • Artificial Potential Fields

7

Jun 17

Continue Research

8

Jun 24

Milestone 4

Decide on and implement algo

  • Depending on the algorithm the architecture will be pretty different

  • At this point, halfway through the term, the co-op has done several tasks and should have an idea of how we do system design

  • I want them to start designing the architecture themself and the milestones become less structured

* Note: midterm evaluations

9

Jul 1

Continue algo implementation

10

Jul 8

Milestone 5

Test algo

11

Jul 15

Simple stopping in manual mode

  • Go into loiter mode if we’re going to crash

  • Based on velocity, direction, and detected objects

12

Jul 22

Milestone 6

Test stopping

13

Jul 29

Research assistive avoidance

Potential Algorithms:

  • Proportional

  • Pure pursuit

  • Reactive

14

Aug 5

Implement algo

15

Aug 12

Continue algo implementation

16

Aug 19

Milestone 7

Test assistive method and off boarding

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