The control technique and modeling of quadruped robots

Abstract

Quadruped robots, distinguished by their exceptional terrain adaptability, enabling seamless navigation across rough landscapes like uneven farmlands, cluttered construction sites, and debris-strewn disaster-stricken areas, and versatile task capabilities, have emerged as a pivotal technology in diverse fields. In agriculture, they utilize built-in sensors to conduct high-precision crop monitoring, detect pests early, and apply targeted precision spraying, thereby reducing manual labor and waste of resources. In the industry, they access narrow pipelines or high-risk, hazardous zones to perform equipment inspections, thereby avoiding potential threats to human workers. In public service, they play a critical role in search-and-rescue missions by locating survivors in collapsed buildings and support urban patrols to enhance public safety, significantly boosting operational efficiency and safety levels. Despite existing limitations, such as persistent challenges in dynamic balance control on slippery surfaces and energy consumption optimization for long-duration tasks, quadruped robots have advanced rapidly under the empowerment of AI. AI-driven algorithms have revolutionized core aspects, such as mechanical modeling, enabling more accurate simulation of limb movement mechanics and intelligent control techniques, thereby enhancing real-time responsiveness to sudden environmental changes. These advancements collectively fuel their promising prospects, positioning them as key players in the future of intelligent robotics.