Core application scenarios
· Industrial Manufacturing: The core is intelligent flexible production. AI vision guides the robotic arm to complete complex assembly and dynamic grasping; AGVs/AMRs achieve autonomous navigation and obstacle avoidance through reinforcement learning; AI quality inspection robots use deep learning to detect tiny product defects, far surpassing manual efficiency in their work.
· Service and Business: Shifting from "mechanical execution" to "proactive service." Hotel/restaurant robots integrate SLAM positioning with multimodal interaction to complete welcoming, guidance, and delivery; Sales assistant robots use AI to identify customer preferences and provide personalized recommendations; Cleaning robots use computer vision to identify types of waste and optimize cleaning routes.
· Healthcare: From an auxiliary tool to an operating entity. Surgical robots use AI-enhanced vision systems to analyze images in real time, assisting doctors in high-precision operations; Rehabilitation robots dynamically adjust and assist based on patient data; Delivery robots solve the problem of transporting supplies within hospitals.
· Special applications: Replacing humans in hazardous environments. Security inspection robots use thermal imaging and gas sensors to identify fire sources and leaks; Agricultural robots visually identify ripe fruits and pick them; Search and rescue robots autonomously search and rescue in complex environments such as earthquakes and fires.
Key technology modules
· Environmental Perception: The core is multi-sensor fusion. By integrating 3D vision (structured light/binocular/ToF), LiDAR, IMU, millimeter-wave radar, and more, it builds maps in real time and identifies objects.
· Intelligent decision-making: Using reinforcement learning to train robots to complete complex tasks, such as quadruped robots learning to climb stairs; Imitation learning allows the robot to learn actions by observing human teaching.
· Motion control: Multi-joint coordination is achieved through whole-body motion control algorithms, such as the stable walking of bipedal robots. At the same time, smooth control is used to perform tasks requiring force control such as grinding and assembly.
· Human-Computer Interaction: Integrates large language models (such as ChatGPT, BERT) to understand complex instructions and respond naturally through multimodal sentiment recognition (voice, facial expressions, gestures).
Hardware Core and Trends
· AI computing platform: the robot's "brain." Edge computing platforms commonly used include NVIDIA Jetson (Orin/Xavier) and Horizon Sunrise, which need to balance power consumption and computing power.
· Mainstream hardware architecture: Currently, heterogeneous computing is mostly used, i.e., CPU (scheduling) + GPU (parallel computing) + FPGA/ASIC (acceleration) to efficiently handle different algorithms.
· Actuator evolution: Integrated joints (including motor, driver, reducer, encoder) have become mainstream. The widespread adoption of high torque density motors and flexible actuators (SEA) has made robot movement safer and smoother.
· Technology trends: including large model implementation (enabling robots to understand fuzzy commands like "clean the table"), cloud-edge collaboration (cloud training, edge inference), and embodied intelligence.
