A ROS-based 2D pedestrian simulator using the social force model for robot navigation experiments in crowded scenes.
Pedestrian Simulator is a ROS-based 2D simulation tool that models pedestrian behavior using the social force model. It allows researchers and developers to test robot navigation algorithms in crowded environments by generating realistic crowd dynamics and sensor data. The simulator is particularly useful for scenarios where real-world data collection is challenging or impractical.
Robotics researchers and developers working on autonomous navigation, human-robot interaction, or crowd-aware robotics who need to simulate pedestrian-rich environments.
Developers choose Pedestrian Simulator for its realistic social force model implementation, seamless ROS integration, and ability to simulate large crowds in real time, which is essential for prototyping and testing in robotics applications.
Pedestrian simulator powered by the social force model
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Implements the social force model from Helbing et al. for individual and group walking, enabling simulation of large crowds in real time as highlighted in the README.
Provides extensive visualization using Rviz and generates sensor data in the robot frame, making it easy to integrate with existing ROS ecosystems for navigation testing.
Uses XML-based configuration for designing custom simulation environments, allowing researchers to rapidly prototype different pedestrian scenarios.
Generates point clouds for people and walls, useful for developing perception algorithms without the need for real-world data collection.
Tested on older ROS versions like hydro and indigo, with melodic requiring a separate branch, indicating limited maintenance and potential compatibility issues with newer distributions.
Explicitly stated as a 'work in progress' used in research prototyping, which may lead to instability, bugs, or incomplete features for production deployment.
Focuses solely on 2D environments, which might not suffice for applications requiring 3D pedestrian dynamics or more complex spatial interactions.