This repository builds on prior work in social robot navigation and pedestrian simulation. The
references below preserve the provenance that previously lived in the root README.md.
Caruso, Matteo, Enrico Regolin, Federico Julian Camerota Verdu, Stefano Alberto Russo, Luca Bortolussi, and Stefano Seriani. "Robot Navigation in Crowded Environments: A Reinforcement Learning Approach." Machines 11, no. 2 (2023): 268. https://doi.org/10.3390/machines11020268
As stated in the paper's data-availability statement, related public material was made available in:
- https://github.com/EnricoReg/robot-sf
- https://github.com/EnricoReg/asynch-rl
- https://github.com/matteocaruso1993/crowd_nav_experimental
Additional repositories referenced in this project's lineage and implementation context:
- https://github.com/Bonifatius94/robot-sf
- https://github.com/yuxiang-gao/PySocialForce
- https://github.com/Bonifatius94/PySocialForce
The fast-pysf/ subtree and surrounding pedestrian-simulation work acknowledge the upstream model
and implementation lineage:
- Based on Sven Kreiss's implementation of the vanilla Social Force model: https://github.com/svenkreiss/socialforce
- Force-implementation details also drew inspiration from: https://github.com/srl-freiburg/pedsim_ros
- Helbing, D., and P. Molnar. "Social force model for pedestrian dynamics." Physical Review E 51, no. 5 (1995): 4282-4286. https://doi.org/10.1103/PhysRevE.51.4282
- Moussaid, M., N. Perozo, S. Garnier, D. Helbing, and G. Theraulaz. "The walking behaviour of pedestrian social groups and its impact on crowd dynamics." PLoS ONE 5, no. 4 (2010): 1-7. https://doi.org/10.1371/journal.pone.0010047