An adaptive hierarchical control framework for quadrupedal robots in planetary exploration

2025-10-20

AI Summary

To address insufficient navigation robustness of quadrupedal robots in planetary exploration under unknown, complex terrain—caused by uncertainties in both environmental and robot-specific parameters—this paper proposes a modular hierarchical control framework. The framework integrates model-based dynamic control, online system identification for real-time model adaptation, contactless state estimation, and adaptive footstep planning, enabling runtime reconfiguration and cross-platform deployment. Fully implemented within the open-source ROS 2 ecosystem, it achieves real-time terrain–robot interaction modeling and regulation without external perception hardware. Evaluated on two distinct quadruped platforms across diverse hardware architectures, the method demonstrates effectiveness in field tests at a volcanic site, achieving over 700 meters of continuous autonomous locomotion. Results show significant improvements in adaptability, stability, and generalizability under extreme environmental conditions.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Multiagent Systems: Multiagent Planning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Agentic search
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Planetary exploration missions require robots capable of navigating extreme and unknown environments. While wheeled rovers have dominated past missions, their mobility is limited to traversable surfaces. Legged robots, especially quadrupeds, can overcome these limitations by handling uneven, obstacle-rich, and deformable terrains. However, deploying such robots in unknown conditions is challenging due to the need for environment-specific control, which is infeasible when terrain and robot parameters are uncertain. This work presents a modular control framework that combines model-based dynamic control with online model adaptation and adaptive footstep planning to address uncertainties in both robot and terrain properties. The framework includes state estimation for quadrupeds with and without contact sensing, supports runtime reconfiguration, and is integrated into ROS 2 with open-source availability. Its performance was validated on two quadruped platforms, multiple hardware architectures, and in a volcano field test, where the robot walked over 700 m.

Problem

• Addresses uncertain robot and terrain parameters in exploration
• Enables quadruped navigation on uneven and deformable terrains
• Combines dynamic control with online adaptation and planning

Innovation

• Model-based dynamic control with online adaptation
• Adaptive footstep planning for uncertain terrains
• ROS 2 integrated open-source modular framework