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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Urban Systems

Background:

  • Urban autonomous navigation faces challenges in varied terrains and dynamic obstacles.
  • Wheeled-legged robots offer potential for enhanced logistics and adaptability.
  • Existing systems often lack integrated locomotion and navigation control.

Purpose of the Study:

  • To develop and validate a fully integrated system for autonomous wheeled-legged robot navigation in urban environments.
  • To enhance robot adaptability and efficiency in complex, real-world settings.
  • To demonstrate the effectiveness of hierarchical reinforcement learning for robot control.

Main Methods:

  • Utilized model-free reinforcement learning (RL) and privileged learning for a versatile locomotion controller.
  • Implemented a hierarchical RL framework integrating locomotion and navigation controllers.
  • Developed mobility-aware local navigation and large-scale path planning modules.
  • Validated the system through autonomous, kilometer-scale navigation missions in urban areas.

Main Results:

  • Achieved efficient and robust locomotion over diverse rough terrains with seamless mode transitions (walking/driving).
  • Demonstrated effective high-speed navigation through challenging terrain and dynamic obstacles.
  • Successfully completed autonomous navigation missions in Zurich and Seville, validating system robustness and adaptability.

Conclusions:

  • Integrated control systems are crucial for seamless autonomous navigation in complex urban environments.
  • Wheeled-legged robots and hierarchical RL are feasible for advanced autonomous navigation.
  • The developed system has significant implications for last-mile delivery and future logistics.