Arena Rosnav 3.0 is a platform for developing and benchmarking navigation algorithms in human-centric social environments. We offer a wide variety of different social force models, robots, planners, and world generation algorithms, and many more to use. All functions are abstracted and can be run across three widely used simulators: Flatland 2D, Gazebo, and Unity 3D. Arena Rosnav also offers a complete evaluation pipeline for benchmarking the performance of robots and planners based on standard metrics, and a trainings pipeline for navigational models based on DRL and PPO. With this pipeline our own DRL planner ROSNavRL was created.
Task Generator
- Universal 2D/3D Model Database
- Easy Task Mode Integration with Python, pre-provided:
| Task Mode | Description |
|---|---|
| scenario | static scenario file |
| random | random positions |
| parametrized | fine-tuned random |
| guided | waypoint sequence |
| explore | explore map |
Simulators
Training
In today’s dynamic environments, traditional planning methods often struggle to adapt, while Deep Reinforcement Learning (DRL) shows promise in obstacle avoidance but faces challenges in long-range navigation due to local minima.
That’s where we come in. We recognize the importance of combining the strengths of both traditional planning methods and DRL-based approaches to create a robust and efficient navigation system. Our platform offers a comprehensive framework that seamlessly integrates these two paradigms, providing you with the tools and resources needed to train and test deep reinforcement learning algorithms alongside classic methods.
With our platform, you have the flexibility to choose from a variety of robot models and define your own Deep Neural Network (DNN) or select from predefined networks. Training is simplified through a user-friendly interface and a single config file, making the process accessible to users of all skill levels.
Furthermore, our platform supports multiprocessed rollout collection for training with debug mode, ensuring efficient training and evaluation of your navigation system. You can also enable and modify a custom training curriculum to tailor the training process to your specific requirements.
Once trained, your agent can be seamlessly deployed onto your mobile robot, ready to navigate dynamic environments with confidence. And with optional data logging, you can track the performance of your navigation system over time, ensuring continuous improvement and optimization.
