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…acting [Feature]: Cleaning up the github repository documentation
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doc/03_research/01_acting/04_research_Acting_Pylot_and_PAF21_2.md
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...search_Pylot-Planning_PAF21-Perception.md → ...erception/05_Research_PAF21-Perception.md
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# Sprint 0: Research Samuel Kühnel | ||
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## Pylot | ||
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## Planning | ||
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- 4 different options | ||
- **Waypoint Planner**: Auto follows predefined waypoints. It recognizes traffic lights and stops at obstacles, but cannot avoid them | ||
- **Freenet-Optimal-Trajecotry-Planner**: CPP code with Python wrapper ([GitHub](https://github.com/erdos-project/frenet_optimal_trajectory_planner)) | ||
→ Predefined line that is used for orientation → Can avoid obstacles! | ||
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![freenet_gif](https://github.com/erdos-project/frenet_optimal_trajectory_planner/raw/master/img/fot2.gif) | ||
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- **RRT\*-Planner**: RRT* algorithm for path planning ([GitHub](https://github.com/erdos-project/rrt_star_planner)) | ||
- Creates random nodes | ||
- Adds nodes to the graph that are not blocked by objects on the road | ||
- Generally terminates as soon as a node is found in the target area | ||
- RRT*: Searches for the shortest path | ||
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![rrt_star_gif](https://github.com/erdos-project/rrt_star_planner/raw/master/img/rrtstar.gif) | ||
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- **Hybrid A\* planner**: Hybrid A* algorithm for path planning ([GitHub](https://github.com/erdos-project/hybrid_astar_planner)) | ||
- Calculates the shortest path between two nodes from a graph | ||
- Similar to Dijkstra's algorithm | ||
- Nodes are estimated based on their costs and promising nodes are selected first | ||
- Hybrid A* algorithm: Not always optimal solution, but in the neighborhood of the optimal solution. | ||
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![hybrid_astar_gif](https://github.com/erdos-project/hybrid_astar_planner/raw/master/img/straight_obstacle.gif) |
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# Sprint 1 | ||
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- [ ] Namen auf Präsentationen packen | ||
- [ ] cleaning up github repo {Robert, Leon} | ||
- [ ] Research issues PR / merge | ||
- [ ] 5- 25 h work for submission fix (Work for 2) {Samuel, Maxi} | ||
- [ ] constructing & updating docker image (archive into docker image) | ||
- [ ] update of branches | ||
- [ ] Analyzation of projects AIM State and IS State (every group) | ||
- [ ] Start working on code | ||
- [ ] detailed understanding of components | ||
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Perception: {Leon, Maxi} | ||
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- [ ] removing panopticsegmentation (if non breaking) | ||
- [ ] what does happen when its removed? | ||
- [ ] (→ replacing it with object detection by Pylot [issue for backlog]) | ||
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Planning: {Julius, Samuel, Alex} | ||
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- [ ] creating visual architecture | ||
- [ ] understanding of components | ||
- [ ] understanding of current Decision Tree | ||
- [ ] does the global routing by the Decision Tree work? | ||
- [ ] lane decisions correct? (holding lane, switching lanes) | ||
- [ ] how well does it perform | ||
- [ ] limitations? | ||
- [ ] (Decision Tree, (vs. ) State Machine [issue for backlog]) | ||
- [ ] comparing to performance of State Machine by Driving Score (if Decision Tree hits its limitations) | ||
- [ ] Decision Tree might be better without needing to test State Machine | ||
- [ ] Composition of local planner | ||
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Acting: {Robert, Alex} | ||
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- [ ] Detailed understanding of components | ||
- [ ] Comparing trajectory with actual actions | ||
- [ ] which new interfaces are needed? (if needed) |