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Abstract
Bioinspired robotic systems have emerged as promising platforms capable of robust locomotion across unstructured and unpredictable terrains. Mimicking the adaptive strategies of biological organisms, these robots aim to overcome the limitations of traditional mechanical systems, particularly in navigation tasks where terrain variability presents significant challenges. This paper presents the development process and performance evaluation of a new generation of bioinspired robotic platforms optimized for adaptive locomotion. Drawing from principles observed in biological systems, we employ multi-modal sensing, dynamic morphology, and learning-based control architectures. Our empirical results demonstrate improved stability, energy efficiency, and terrain adaptability. These findings offer meaningful contributions to the fields of robotics, biomechanics, and autonomous exploration.
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