Autonomous reconfigurable robotic system

This paper presents an autonomous reconfigurable robotic system that significantly enhances terrain adaptability and operational reliability through a "morphology-task-safety" co-evolutionary framework. Conventional rigid-body robots exhibit critical limitations in dynamic environments, including fixed morphologies incompatible with multi-terrain operations, reliance on manual teleoperation, limited functional versatility, and an inability to perceive complex physical threats. To overcome these challenges, this paper proposes a neuromorphic morphogenesis engine combining hierarchical deep reinforcement learning (HDRL) with multimodal topographic cognition, enabling real-time transitions between wheeled, legged, and folded configurations through terrain conductivity and acoustic signature analysis; and a neuroadaptive collaboration framework implementing dynamic authority allocation via Q-learning with energy-aware exploration. Experimental validation in simulated high-risk scenarios shows that 90% operational success rate under intermittent GPS-denied conditions, 20% higher adaptability metrics through optimal morphology-task matching. This work establishes a transformative paradigm for next-generation field robotics in unstructured, GPS-denied environments.