Planning and Decision Making for Autonomous Robots Project, Fall 2021 @ETHZ
Required python version:
| Item | Value |
|---|---|
| Required Python Version | 3.9 |
| Deadline | 31.12.2021, 23:59:59 |
Running from the console, assuming you run this from the project root:
# install requirements
$ pip install -r requirements.txt
# export PYTHONPATH to run from console
$ export PYTHONPATH="$PWD/src"
# run app_main.py
$ python src/pdm4ar/app_main.pyRunning from the provided Docker container (Requires a recent docker version to be installed).
$ make run-final21Below you can find some visualizations of the scenarios we used to evaluate our approach.
EpisodeOutcome(
goal_reached = 1,
has_collided = 0,
distance_travelled = 133.38,
episode_duration = 10.68,
actuation_effort = 11.03,
max_acc_lat = 9.63,
max_acc_long = 23.56,
avg_computation_time= 0.10,
)
EpisodeOutcome(
goal_reached = 1,
has_collided = 0,
distance_travelled = 136.09,
episode_duration = 12.30,
actuation_effort = 10.77,
max_acc_lat = 9.63,
max_acc_long = 20.21,
avg_computation_time= 0.36,
)
EpisodeOutcome(
goal_reached = 1,
has_collided = 0,
distance_travelled = 134.12,
episode_duration = 9.57,
actuation_effort = 11.25,
max_acc_lat = 11.65,
max_acc_long = 22.88,
avg_computation_time= 0.55,
)
EpisodeOutcome(
goal_reached = 1,
has_collided = 0,
distance_travelled = 145.94,
episode_duration = 16.29,
actuation_effort = 8.31,
max_acc_lat = 42.71,
max_acc_long = 42.71,
avg_computation_time= 1.18,
)
- RRT* - run for each timestep:
- Create Motion Primitives
- KD-tree to compute distances
- Caution, use box-distance measure
- Collision checking via safety certificates
- For state x choose best motion primitive to get to new state y
- Optimal path in RRT* graph
- Cost function
- Time required for each motion primitive
The given setup has been evaluated on two additional, custom dgscenaries. In order to enable them, set the "custom_cases" flag in /src/pdma4ar/exercises_def/final21/ex.py:get_final21" to True.
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