ETH Zurich researchers use advanced learning methods to create an autonomous wheeled-legged robot's control system.
The team trained the robot using computer simulations, then used detailed maps and scans to help it navigate in the real world.ETH Zurich researchers have developed a locomotor control that can enable wheeled-legged robots to autonomously navigate various urban environments.
According to the team, the training involved simulated data, with controllers then integrated into a global navigation system using digital-twin models and high-resolution laser scans for experiment planning and onboard localization. Practical assessments highlighted the potential of wheeled-legged robots for achieving efficient and robust autonomy in real-world scenarios.The autonomous navigation system for wheeled-legged robots integrates multiple payloads for localization, terrain mapping, and safety, including LIDAR sensors, a stereo camera, a delivery box, a 5G router, and a GPS antenna.
Both controllers are neural networks trained via reinforcement learning , with the HLC representing a novel contribution. This helps to integrate local navigation planning and path-following control. Kilometer-scale autonomous deployments in the urban environments of Zurich and Seville demonstrate the system’s capability, covering in Zurich with minimal human intervention.
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