arXiv:2606.06790v1 Announce Type: cross Abstract: This paper presents ERNEST, a four-wheeled planetary rover concept equipped with a two-degree-of-freedom Active Gimbal Suspension that combines yaw and roll actuation to enable wheel reconfiguration, steering, and active load redistribution. A single neural network controller, trained to track a desired path across challenging terrain, fully unlocks the capabilities of this actuated suspension system for autonomous obstacle negotiation. A reinforcement learning framework is developed using the high-fidelity DARTS simulation engine, which combin
The paper leverages recent advancements in reinforcement learning and high-fidelity simulation engines to address complex robotic locomotion challenges, pushing the envelope for autonomous planetary exploration.
This development significantly enhances the autonomy and capability of robotic systems in extreme and unstructured environments, reducing the need for direct human intervention and expanding exploration possibilities.
The ability of rovers to autonomously negotiate challenging terrains through learning-based control changes how planetary missions can be designed and executed, increasing efficiency and mission success rates.
- · Space Exploration Agencies
- · Robotics Companies
- · AI/Machine Learning Researchers
- · Defence/Aerospace Contractors
Planetary rovers become more agile and capable of traversing previously inaccessible areas on other celestial bodies.
The learned locomotion principles could be adapted for terrestrial applications such as search and rescue, agriculture, or industrial inspection in complex environments.
Enhanced autonomous capabilities in space could accelerate the timeline for off-world resource utilization and sustained human presence beyond Earth.
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