Task for Scientific Seminar
I developed a Nash equilibrium-based decision-making system within a Gazebo simulation environment to model and control autonomous agent behavior in dynamic scenarios. The project focused on implementing game-theoretic strategies that allow multiple agents to make optimal decisions by considering the actions and potential responses of others. By integrating the algorithm into Gazebo, I simulated realistic interactions between agents, enabling them to reach stable outcomes where no participant benefits from unilaterally changing their strategy. This work combined simulation, algorithm design, and multi-agent coordination to demonstrate intelligent and adaptive behavior in complex environments.
Key Skills: Game theory (Nash equilibrium), algorithm design, multi-agent systems, ROS/Gazebo simulation, decision-making under uncertainty What I Worked On: Implementation of Nash equilibrium logic, agent interaction modeling, integration with Gazebo simulation, testing of strategic behaviors in dynamic environments Impact: Demonstrated stable and optimal decision-making in multi-agent scenarios, enhanced understanding of strategic AI systems, and showcased the application of game theory in autonomous robotics simulations
ros2 run teleop_twist_keyboard teleop_twist_keyboard
--ros-args -r /cmd_vel:=/r1/cmd_vel
ros2 run teleop_twist_keyboard teleop_twist_keyboard
--ros-args -r /cmd_vel:=/r2/cmd_vel
python3 ~/seminar/src/multi_robot/scripts/odom.state.py
python3 ~/seminar/src/multi_robot/scripts/nash.controller.py
Keep in mind that this is just the structure of my folders used in my own computer.
ros2 launch multi_robot agents.launch.py world:=../src/multi_robot/worlds/agentworld1
ros2 topic echo /r1/odom --once
You are authorized to use this code for academic and professional purposes, including research, teaching, and commercial work, as long as you provide appropriate credit to the original author.