The ability of miniature robots to move efficiently across soft or deformable terrain remains a challenge, particularly when facing limits on power and control. To solve this problem, a robotic gait algorithm was developed to modify gait parameters in response to real-time feedback from the environment. The system combines a hierarchical command system with machine-learning terrain classification to move with precision and minimal power on unstable surfaces like sand, soil, and gravel. A visual perception component uses BoW representations and SVM to classify the terrain before contact, enabling prior strategy formulation and improving performance control of gait adaptation. Stridelengh, advancing joint torque, and foot movement through inverse kinematics are implemented alongside terrain cost mapping to balance slippage. System performance evaluation through simulations and real-world field experiments validates the algorithm's ability to enhance locomotion versatility and precision. Enhanced miniature robotic mobility critical in disaster response, planetary exploration, and environmental surveillance is made possible by this solution.
Moh et al. (2025) studied this question.