Vehicle dynamics on a robot in the water can be challenging. This challenge is a first pass on understanding the challenges behind how vehicle controls in varying sea states can be difficult.
We want two predictors:
-
Predict the next vehicle state given a sequence of vehicle controls and the current vehicle state.
- vehicle state is an AHRS message, see the ahrs.log for an example
-
Predict the sequence of vehicle controls to achieve a desired vehicle state given a current vehicle state
- vehicle state is an AHRS message, see the ahrs.log for an example
We have provided two dataset files which can be fetched using git lfs pull after installing git lfs:
-
vehicle_control.csv
- This is a csv with ts, throttle and turn values
- ts is a timestamp in UTC
- throttle is a float value between 0.0-1.0
- turn is a float value between -1.0 and 1.0
- 0.0 to 1.0 is a right hand turn
- -1.0 to 0.0 is a left hand turn
- gear is a discrete motor setting:
- 0 neutral
- 1 forward
- 2 reverse
- trim is the discrete trim command that affects boat dynamics, taking on vals 0, 1 or 2.
- This is a csv with ts, throttle and turn values
-
ahrs.csv
- This is a csv with ts, roll_deg, pitch_deg, yaw_deg, ve_mps,
vn_mps, vu_mps, omega_x_dps, omega_y_dps, omega_z_dps, ax_mps2,
ay_mps2, az_mps2
- ts is a timestamp in UTC
- roll_deg, pitch_deg, and yaw_deg are degrees values
- ve_mps, vn_mps, and vu_mps are velocities in meters per second in the ENU frame
- omega_x_dps, omega_y_dps, and omega_z_dps are angular rates in the FRD frame
- ax_mps2, ay_mps2, and az_mps2 are meter per second^2 accelerations in FRD frame
- This is a csv with ts, roll_deg, pitch_deg, yaw_deg, ve_mps,
vn_mps, vu_mps, omega_x_dps, omega_y_dps, omega_z_dps, ax_mps2,
ay_mps2, az_mps2
- Send us a git repo with code for the two predictors and instructions on how to run it
- Also add a README/document with a description explaining the design for your predictor
- Any visualizations or plots to accompany your code are encouraged in your document that explains your design