F1Tenth Two-Agent Simulator

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F1Tenth Two-Agent Simulator is an open-source ROS-integrated environment for testing and benchmarking two autonomous racecar agents. It provides realistic 1/10th scale vehicle dynamics, customizable control stacks, real-time visualization, and logging support for multi-agent coordination and competition scenarios.
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May 15 2025
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F1Tenth Two-Agent Simulator

F1Tenth Two-Agent Simulator

0 Reviews
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F1Tenth Two-Agent Simulator
F1Tenth Two-Agent Simulator is an open-source ROS-integrated environment for testing and benchmarking two autonomous racecar agents. It provides realistic 1/10th scale vehicle dynamics, customizable control stacks, real-time visualization, and logging support for multi-agent coordination and competition scenarios.
Added on:
Social & Email:
Platform:
May 15 2025
--
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What is F1Tenth Two-Agent Simulator?

The F1Tenth Two-Agent Simulator is a specialized simulation framework built on ROS and Gazebo to emulate two 1/10th scale autonomous vehicles racing or cooperating on custom tracks. It supports realistic tire-model physics, sensor emulation, collision detection, and data logging. Users can plug in their own planning and control algorithms, adjust agent parameters, and run head-to-head scenarios to evaluate performance, safety, and coordination strategies under controlled conditions.

Who will use F1Tenth Two-Agent Simulator?

  • Autonomous driving researchers
  • Robotics and control engineers
  • Graduate students in robotics
  • AI/ML algorithm developers
  • University robotics course instructors

How to use the F1Tenth Two-Agent Simulator?

  • Step1: Install ROS (Noetic or Melodic) and Gazebo on a Linux machine.
  • Step2: Clone the repository: git clone https://github.com/JZ76/f1tenth_simulator_two_agents.git
  • Step3: Build the workspace: catkin_make in the workspace root.
  • Step4: Source the workspace: source devel/setup.bash.
  • Step5: Launch the simulator: roslaunch f1tenth_simulator two_agents.launch.
  • Step6: Configure agent controllers via ROS parameters or replace control nodes.
  • Step7: Start training or evaluation routines and monitor in Rviz/Gazebo.
  • Step8: Collect logs and analyze performance metrics.

Platform

  • linux

F1Tenth Two-Agent Simulator's Core Features & Benefits

The Core Features

  • Dual-agent simulation in Gazebo
  • ROS integration for sensor and control nodes
  • Realistic vehicle dynamics and tire models
  • Customizable planning and control interfaces
  • Real-time 3D visualization and logging

The Benefits

  • Rapid prototyping of multi-agent algorithms
  • Benchmarking head-to-head racing strategies
  • Open-source and extensible architecture
  • Reproducible experiments with logging
  • Educational tool for autonomous driving courses

F1Tenth Two-Agent Simulator's Main Use Cases & Applications

  • Comparing collision avoidance algorithms in multi-agent scenarios
  • Benchmarking reinforcement learning controllers for racing
  • Teaching autonomous vehicle control in robotics courses
  • Evaluating cooperative mapping and navigation strategies
  • Testing real-time planning under dynamic track conditions

FAQs of F1Tenth Two-Agent Simulator

F1Tenth Two-Agent Simulator Company Information

F1Tenth Two-Agent Simulator Reviews

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F1Tenth Two-Agent Simulator's Main Competitors and alternatives?

  • Carla Simulator
  • LGSVL Simulator
  • Microsoft AirSim
  • TORCS
  • Gazebo multi-robot racing setups

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