A Miniature Robotic Gait Algorithm for Terrain-Adaptive Walking in Soft Ground Environments

Abstract

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.