查看購物車
  • English
  • Japanese
  • Korean
封面
市場調查報告書
商品編碼
2109332

具身智慧（人形機器人）MCU市場：2026年

Embodied Artificial Intelligence (& Humanoid Robot) MCU Research Report, 2026
出版日期: | 出版商: ResearchInChina | 英文 500 Pages | 商品交期: 最快1-2個工作天內

價格
簡介目錄

人形機器人MCU研究－從通用控制到整合邊緣AI功能的高價值專用晶片解決方案的演變

微控制器（MCU）是周邊設備到單一晶片上。它也被稱為微電腦。

目前，大多數用於具身智慧（EAI）的微控制器（MCU）都是通用型MCU。然而，隨著EAI效能要求和應用場景的不斷擴展，專用MCU正被推向市場，以確保EAI在各種場景下的穩定運作。未來，這些專用MCU可望逐步滲透通用型MCU市場，並在EAI的製造上廣泛應用。

在機器人領域，微控制器（MCU）並非以「單晶片集中控制」模式部署，而是分散式部署並整合到所有系統層中，以處理感知、控制、通訊和執行等任務。微控制器在增強型航空航太整合（EAI）的核心控制系統、關節控制系統、感知系統、電源管理系統和通訊系統中發揮著至關重要的作用。

核心控制系統：MCU 作為「神經中樞」和「小腦」，以分佈式節點的形式整合到人體的關節、感覺模組和通訊模組中，負責從即時運動控制到系統安全的所有環節的功能。

關節控制系統：微控制器（MCU）作為執行單元廣泛應用於各個關節，以完成單一關節的局部封閉回路型控制，並確保精確的運動執行。此外，由於靈巧手部的獨特特性，手指關節中整合了高度整合、結構緊湊的MCU，以實現所有自由度的全面覆蓋。

感知系統：MCU 在感知、判斷和執行的封閉回路型中處理分散式即時處理任務，從而實現諸如檢測和收集感測器資料、預處理和融合多模態訊號等功能。

電源管理系統：MCU主要用於監控電池狀態、安排能源消耗並提供安全保護。

通訊系統：MCU 實現了大腦、小腦、關節和周邊效應器之間命令的循環和同步互連。

編碼器模組 - 將機械運動轉換為高精度、低延遲的電訊號，支援封閉回路型控制。

慣性測量單元 (IMU) 模組－這是機器人辨識自身姿態、運動、加速度和角速度的核心組件。微控制器 (MCU) 讀取資料並執行演算法，最終輸出機器人的姿態資訊。

人形機器人複雜手部技術路線差異化中聯合MCU的專業化趨勢。

作為機器人的關鍵關節模組，靈巧手預計主要沿著以下五個趨勢發展：

基於尖端技術的演進趨勢，MCU層面出現了五大趨勢：小型化、高效能即時處理、I3C匯流排通訊、使用NPU的邊緣AI以及功能安全認證。

趨勢一：小型化和高整合度：隨著精密機械手的靈活性不斷提升，其內部空間變得極為有限，這促使微控制器（MCU）進一步小型化和高整合度發展。在封裝方面，業界正從BGA封裝轉向WLCSP封裝（4x4mm，甚至更小）。 GigaDevice的GD32G553實現了4x4mm的WLCSP封裝，並致力於以單晶片實現多馬達控制，使得「單晶片一體化解決方案」成為理想的發展方向。 NXP的i.MX RT1180則可直接以單晶片驅動多達六個無刷無芯馬達。

趨勢二：增強的高效能即時處理能力－所需的主頻正從目前的 200MHz 提升至 600MHz，甚至 1GHz。多核心架構將被廣泛採用，並成為主流標準配置。 NXP i.MX RT1180 採用雙核心架構（240MHz M33 + 800MHz M7），整合的硬體加速器，例如 FPU、DSP、TMU（三角函數單元）和 FAC（濾波演算法加速器），至關重要。

趨勢 3 - 新型匯流排通訊架構 I3C - I3C匯流排拓撲連接多個伺服節點和觸覺感測器，同時透過 EtherCAT、CAN 和 RS485 為機器人系統的匯流排提供外部連接，使複雜的機械手系統具有更高的整合度、更高的性能和更廣泛的應用場景。

趨勢 4 - 邊緣 AI 運算委託給 MCU - 智慧手正在演變為一個獨立的智慧子系統，具有專用的網域控制器，支援邊緣 AI 執行手勢姿態辨識、物體偵測和滑倒預測等任務，而無需依賴雲端的運算能力。

趨勢 5 - MCU 專業化和功能安全認證 - 專業化 MCU 正在成為擴大市場規模的趨勢，有效降低系統的整體物料清單成本。

「巧手」系統MCU中採用的I3C分散式匯流排架構有利於「巧手」系統的高度整合。

與傳統的 I2C 相比，I3C 匯流排為敏捷手持系統提供了顯著的優勢，包括高速通訊、簡化的佈線和硬體設計、動態設備管理以及即時回應。

恩智浦半導體(NXP)透過採用基於I3C的本地匯流排拓撲結構，革新了其精密機械手的內部通訊模式。在這個架構中，i.MX RT1180用作手掌主控MCU，MCX A132用作手指關節控制MCU，透過I3C匯流排連接多個伺服節點和觸覺感測器，同時也透過EtherCAT、CAN和RS485介面與機器人系統的匯流排進行外部連接。

NXP i.MX RT1180 是一款手掌系統的主控 MCU，採用高效能雙核心架構（240MHz M33 + 800MHz M7）。它整合了兩個 I3C 介面，可連接多個伺服節點和感測器，支援多種工業通訊協定介面，例如 EtherCAT 和 CAN-FD，並提供豐富的 PWM、ADC 和編碼器介面。單晶片即可直接驅動多達六個無刷無芯馬達。

NXP MCX A132 微控制器整合於手指關節的伺服節點和觸覺感測器中。這款微控制器採用小型封裝，適合整合到手指模組中，整合了一個用於與主控制器高速通訊的 I3C 介面，並內建 16 位元類比數位轉換器 (ADC)，可從觸覺感測器高品質地擷取類比訊號。此外，它還支援 IEC 61508 SIL2 功能安全自檢庫，以滿足未來人形機器人的功能安全要求。

一款靈巧的手持式MCU，整合了邊緣AI，能夠執行末端執行器模型演算法。

透過將EAI的運算能力轉移到機器人的末端執行器（靈巧手），實現了觸覺感知、力控制和決策的局部封閉回路型。作為EAI一部分的多模態感知任務在靈巧手內部的裝置上以推理形式執行，而不是將資料傳送到雲端。

意法半導體（STMicroelectronics）推出基於STM32N6和STM32MP257的手勢姿態辨識與控制系統，以滿足市場需求並推動自身技術發展。該系統由三個主要組件構成：基於STM32N6的感知單元，用於手勢姿態辨識和數據採集；基於STM32MP257的PLC單元，用於數據轉換和處理；以及基於STM32G431的運動控制單元，用於控制靈巧的手部運動和手勢追蹤。

STM32N6 內建的 NPU 運行先進的手勢姿態辨識模型。結合高靈敏度彩色影像感測器「VD66GY」，它可以精確捕捉並識別 21 個手勢關鍵點。配備 STM32MP257 的 PLC 演示板處理這些數據，並控制 15 個伺服馬達來模擬靈巧的手部動作，從而實現即時、精準的手勢響應。意法半導體 (STMicroelectronics) 展現了其卓越的整合能力，將微控制器技術與先進感測器結合，實現了即時手勢姿態辨識和控制。

STM32N6 微控制器整合了 ST 強大的 Neural-ART 加速器 NPU，擁有高達 600 GOPS 的處理能力和 3 TOPS/W 的超低功耗。它採用 800MHz Cortex-M55 內核，並增加了 150 個 DSP 向量擴展 (MVE) 指令集，為視覺資料處理提供了卓越的計算性能，確保高效、準確的手勢姿態辨識。

人形機器人MCU市場趨勢—全端晶片解決方案+專用EAI MCU

隨著EAI市場的擴張，MCU廠商不再試圖以單一MCU來滿足機器人的需求。相反，他們正在整合「MCU+類比+儲存+感測+通訊」的多條產品線，以涵蓋感知、控制、驅動、通訊和安全等整個流程，同時也提供參考設計和生態系統支援。

在晶片整合機器人硬體領域，兆易半導體建構了涵蓋控制、儲存和類比電路的綜合產品矩陣，為人形機器人提供全端晶片支援。其高性能GD32 MCU用於即時關節運動控制和系統調度。快閃記憶體確保決策過程所需的高速可靠資料存儲，而類比晶片則涵蓋感測器訊號調諧、馬達驅動和電源管理等關鍵流程，從而強化了從感知到決策和執行的整個機器人系統鏈。

總體而言，GigaDevice 的 GD32 微控制器與 GD30DR 系列驅動晶片配合使用，並結合 LDO、DC-DC 轉換器、PWM 和保護電路，可以創建可靠的機器人馬達控制解決方案，與關節驅動和伺服系統完全相容。

根據每個EAI系統的特性客製化專用EAI MCU。

業界正從通用型MCU轉向高度整合、即時、功能安全的MCU，這些MCU針對每個機器人組件（關節/自適應手/感測節點）進行客製化，並由互連的全端晶片提供支援。這種轉變有助於實現高整合度和功能安全認證，有效解決關節和自適應手在空間、同步和大規模生產方面面臨的挑戰，同時降低物料清單成本和開發門檻。未來，更多廠商將優先研發專用EAI MCU。

HPMicro 開發了 HPM53M1，這是一款專為機器人關節的 CAN 通訊和運動控制而設計的微控制器 (MCU)。 HPM53M1 繼承了 HPMicro 在強大運算能力、高即時性能和高可靠性方面的卓越優勢，並且是業界首款整合式高壓預驅動器和多通道運算放大器的 MCU，擁有世界一流的整合式預驅動器性能。

HPM53M1 採用高效能 RISC-V 內核，運行頻率為 480MHz，支援雙精度浮點運算和強大的 DSP 擴展，能夠輕鬆處理複雜的控制演算法。其記憶體架構包括 1MB 快閃記憶體和 288KB SRAM，以及 16KB 快取（I/D 快取）和高達 256KB 的零等待指令/本機資料記憶體 (ILM/DLM)。它還配備了高效能周邊設備，包括一個 2 通道 16 位元 ADC（2MSPS）、兩個可配置為 PGA 的 OPMAP 和一個 2 通道 12 位元 DAC（1MSPS）。能夠以超高速即時擷取電流、電壓、溫度和位置感測訊號，從而加快關節動態響應速度並提高控制精度。

目錄

第1章：已實現具身智慧（EAI）的定義及硬體簡介

  • 企業應用整合（EAI）的基本概念和術語
  • EAI硬體架構
  • EAI執行器（關節）

第2章：EAI微控制器單元（MCU）市場及趨勢

  • MCU概念和架構
  • EAI中MCU的數量和分佈
  • EAI MCU供應商分析
  • 中國EAI MCU市場預測
  • EAI MCU供應商分析
  • EAI中MCU的演進趨勢
  • EAI MCU 的演進趨勢

第3章：微控制器單元（MCU）在企業應用整合（EAI）的應用

  • EAI中MCU應用概述
  • EAI中MCU應用概述
  • 小腦核心控制系統MCU產品概述
  • 關節控制系統MCU產品概述
  • 靈巧手用MCU產品概述
  • MCU在核心控制系統的應用
  • 微控制器在核心控制系統的應用：小腦系統的演化趨勢
  • 微控制器在核心控制系統的應用：微控制器在小腦系統的應用趨勢
  • MCU在核心控制系統中的應用：新產品參數比較
  • 核心控制系統MCU應用解決方案
  • MCU在協作控制系統中的應用
  • 機器人關節模組的結構
  • 關節控制系統MCU
  • 協作控制系統的演化趨勢
  • MCU在協作控制系統中的應用趨勢
  • 關節控制系統MCU：新產品參數對比
  • 關節控制系統MCU解決方案
  • MCU在實現靈巧手部功能的應用
  • 巧手MCU
  • 靈巧手的進化趨勢
  • MCU在靈巧手中的應用趨勢
  • 靈巧型手持MCU：新產品參數對比
  • 一款功能全面的手持式MCU解決方案
  • MCU在手臂和腿部關節的應用
  • 用於機械臂/腿部關節的MCU解決方案
  • 微控制器在感知系統的應用
  • 感知系統（MCU）
  • 感知系統解決方案
  • MCU在電源管理系統的應用
  • 透過MCU對BMS（電池管理系統）進行監控與管理。
  • BMS MCU監控與管理解決方案

第4章：微控制器下游市場需求分析

  • 下游MCU市場細分：靈巧之手
  • 靈巧雙手概述
  • 巧手和技術路徑
  • 對於手部靈巧的人來說，各種技術方法的優缺點
  • 手持式MCU市場趨勢預測
  • 主要手動工具供應商分析
  • 供應商提供的最新技術，打造靈巧雙手
  • 下游MCU市場細分：小腦控制
  • 小腦控制系統技術路徑概述
  • 小腦控制系統的技術路徑
  • 小腦控制系統的MCU市場預測

第5章：EAI OEM對MCU的應用

  • UBTECH
  • AgiBot
  • Unitree Robotics
  • Booster Robotics
  • ROBOTERA
  • EngineAI
  • Noetix Robotics
  • Fourier Intelligence
  • DEEP Robotics
  • Xpeng IRON
  • Xiaomi
  • Tesla
  • Figure AI

第6章：EAI MCU供應商的解決方案

  • NXP
  • Texas Instruments（TI）
  • STMicroelectronics
  • Renesas Electronics
  • Infineon
  • HPMicro
  • GigaDevice
  • Huawei HiSilicon
  • Nsing Technologies
  • Geehy Semiconductor
  • Allwinner Technology
  • Artery Technology
  • Huada Semiconductor
  • SemiDrive
  • Rockchip
  • Unisoc
  • MindMotion
簡介目錄
Product Code: ZQY002

Research on Humanoid Robot MCUs: Evolution from General-Purpose Control to High-Value Dedicated Chip Solutions Integrated with Edge AI Functions

MCU (Microcontroller Unit) refers to a compact integrated circuit that integrates a central processing unit (CPU), memory (RAM, ROM/Flash), input/output interfaces (I/O), timers/counters, analog-to-digital converters (ADC) and other peripherals onto a single chip. It is also known as a single-chip microcomputer.

Currently, most MCUs adopted by embodied artificial intelligence (EAI) are general-purpose MCUs. However, as performance requirements and scenario applications for EAI increase, dedicated MCUs have been launched to ensure stable operation of EAI across various scenarios. In the future, such dedicated MCUs will encroach upon the market for general-purpose MCUs and be widely applied in EAI manufacturing.

MCUs are not deployed in a "single-chip centralized control" mode on robots. Instead, they are embedded in a distributed manner across all system layers to undertake perception, control, communication, execution and other tasks. They play vital roles in the core control system, joint controller system, perception system, power management system and communication system of EAI.

Core Control System: As the "nerve center" and "cerebellum", MCUs are embedded in full-body joints, perception and communication modules in the form of distributed nodes, undertaking full-link functions ranging from real-time motion control to system safety.

Joint Controller System: MCUs are widely deployed in various joints as execution units to complete local closed-loop control of individual joints and guarantee accurate movement execution. In addition, due to the unique characteristics of dexterous hands, MCUs of highly integrated small-size specifications are embedded in finger joints to achieve full coverage of degrees of freedom.

Perception System: MCUs undertake distributed real-time processing tasks within the perception, decision and execution closed loop to enable such functions as sensor data sensing and collection, multi-modal signal preprocessing and fusion.

Power Management System: MCUs are mainly used to realize battery status monitoring, energy consumption scheduling and safety protection.

Communication System: MCUs allow for instruction circulation and synchronous interconnection among the "cerebrum-cerebellum-joint-end effector".

Encoder Module: Responsible for converting mechanical motion into high-precision, low-latency electrical signal feedback to support closed-loop control.

IMU (Inertial Measurement Unit) Module: The core component for robots to perceive their own posture, movement, acceleration and angular velocity. MCUs output motion posture by reading data and running algorithms.

Specialized Evolution Trend of Joint MCUs Amid Technical Route Differentiation of Humanoid Robot Dexterous Hands

As primary joint modules of robots, dexterous hands will mainly follow five development trends in the future:

According to the evolution trends of dexterous hands, five core trends have formed at the MCU level: small-size packaging, high-performance real-time processing, I3C bus communication, NPU edge AI, and functional safety certification.

Trend 1 - Miniaturization & High Integration: As dexterous hands tend to have higher degrees of freedom, internal hand space becomes extremely constrained, driving MCUs toward smaller sizes and higher integration. In packaging, the industry is shifting from BGA to WLCSP (4X4mm, even smaller). GigaDevice GD32G553 has realized 4X4mm WLCSP; single-chip multi-motor control is targeted, with the "single-chip integrated all-in-one solution" as the ideal direction. NXP i.MX RT1180 can directly drive up to 6 brushless coreless motors via a single chip.

Trend 2 - Enhanced High-Performance Real-Time Processing Capability: Required main frequency evolves from the current 200MHz to 600MHz or even 1GHz; multi-core architectures will find wide application and become mainstream standard configurations. NXP i.MX RT1180 adopts a dual-core architecture (240MHz M33 + 800MHz M7); integrated hardware accelerators including FPU, DSP, TMU (Trigonometric Math Unit) and FAC (Filter Algorithm Accelerator) become a must.

Trend 3 - New Bus Communication Architecture I3C: The I3C bus topology connects multiple servo nodes and tactile sensors, while externally linking to the robot system bus via EtherCAT, CAN and RS485, advancing dexterous hand systems toward higher integration, higher performance and wider application scenarios.

Trend 4 - Edge AI Computing Delegated to MCU: Dexterous hands are evolving into independent intelligent subsystems with dedicated domain controllers supporting edge AI for gesture recognition, object detection, slip prediction and other tasks, without relying on cloud computing power.

Trend 5 - Specialization and Functional Safety Certification of MCUs: To expand market size, specialized MCUs become a trend and can effectively reduce overall system BOM costs.

I3C Distributed Bus Architecture Adopted by Dexterous Hand MCUs Facilitates High Integration of Dexterous Hand Systems

Compared with traditional I2C, the I3C bus delivers outstanding advantages for dexterous hand systems including high-speed communication, simplified wiring and hardware design, dynamic device management and real-time response.

NXP innovates in internal communication modes for dexterous hands with an I3C-based local bus topology. This architecture uses i.MX RT1180 as the palm main control MCU and MCX A132 as finger joint control MCUs, connecting multiple servo nodes and tactile sensors via the I3C bus, while externally linking to the robot system bus through EtherCAT, CAN and RS485.

NXP i.MX RT1180 serves as the palm main control MCU, featuring a dual-core architecture (240MHz M33 + 800MHz M7) with high-performance processing capabilities. It integrates 2 I3C interfaces to connect multiple servo nodes and sensors, supports multiple industrial communication protocol interfaces such as EtherCAT and CAN-FD, and provides abundant PWM, ADC and encoder interfaces. A single chip can directly drive up to 6 brushless coreless motors.

NXP MCX A132 is deployed at finger joint servo nodes and tactile sensors. This MCU features small-size packaging suitable for embedding into finger modules, integrates 1 I3C interface for high-speed communication with the main controller, embeds a 16-bit ADC for high-quality analog signal collection from tactile sensors, and supports the IEC 61508 SIL2 functional safety self-test library to meet future functional safety requirements of humanoid robots.

Edge AI-Integrated Dexterous Hand MCUs Enable Execution of End-effector Model Algorithms

Shift EAI computing power down to robot end effectors (dexterous hands) to realize local closed loops for tactile sensing, force control and decision. Partial multi-modal perception tasks of EAI run on-device inference within dexterous hands instead of transmitting data back to the cloud.

STMicroelectronics has launched gesture recognition and control systems based on STM32N6 and STM32MP257 to match market demand and internal technical evolution. This system is composed of three core parts: Perception part based on STM32N6 for gesture recognition and data collection; PLC part based on STM32MP257 for data conversion and processing tasks; motion control part based on STM32G431 for motion control and gesture following of dexterous hands.

The NPU inside STM32N6 runs sophisticated gesture recognition models. Paired with the VD66GY high-sensitivity color image sensor, it accurately captures and recognizes 21 key points of hand gestures. The STM32MP257 PLC demonstration board processes this data and controls 15 servo motors in a dexterous hand, enabling real-time, precise gesture responses. STMicroelectronics showed its outstanding integration capability in combining microcontroller technology with advanced sensors to achieve real-time gesture recognition and control.

STM32N6 microcontroller integrates ST's powerful Neural-ART accelerator NPU with processing capacity up to 600 GOPS and ultra-low power consumption of 3 TOPS/W. It adopts a Cortex-M55 core with a main frequency of 800MHz and adds 150 DSP Vector Extension (MVE) instruction sets, delivering exceptional computing performance for visual data processing to guarantee efficient and accurate gesture recognition.

Humanoid Robot MCU Market Trend: Full-Stack Chip Solutions + Specialized EAI MCUs

As the EAI market keeps expanding, MCU vendors no longer rely on single MCU to satisfy robot requirements. Instead, they coordinate multi-product lines of "MCU + analog + storage + sensing + communication" to cover the full link of perception, control, drive, communication and safety, while providing reference designs and ecosystem support.

In the chip-enabled robot hardware segment, GigaDevice has built a complete product matrix covering control, storage and analog to deliver full-stack chip support for humanoid robots. Its high-performance GD32 MCUs are applied for real-time multi-joint motion control and system scheduling; its Flash memory delivers high-speed, high-reliability data storage guarantee for decision computing; analog chips cover key links such as sensor signal conditioning, motor drive, and power management, empowering robotic systems across the entire chain from perception and decision to execution.

Overall, GigaDevice GD32 MCUs can coordinate with GD30DR series driver chips, paired with LDO, DC-DC, PWM and protection circuits to build complete, reliable robot motor control solutions fully serving joint drive and servo systems.

Customize Specialized EAI MCUs Based on Characteristics of Each EAI System

The industry is shifting from general-purpose MCUs to highly integrated, real-time, functional safety dedicated MCUs customized by robot component (joint/dexterous hand/sensing node), supported by coordinated full-stack chips. This transformation facilitates high integration and functional safety certification, precisely resolving space, synchronization and mass production challenges of joints and dexterous hands while lowering BOM costs and development barriers. More vendors will prioritize R&D of specialized EAI MCUs in the future.

HPMicro has developed HPM53M1, a dedicated MCU exclusively built for CAN communication and motion control of robot joints. Inheriting HPMicro's consistent strengths of powerful computing power, high real-time performance and high reliability, HPM53M1 integrates high-voltage pre-drivers and multi-channel operational amplifiers for the first time, making it the MCU with the most powerful integrated pre-drive performance available globally.

HPM53M1 adopts a high-performance RISC-V core running at 480MHz, supporting double-precision floating-point calculation and robust DSP expansion capabilities to easily handle complex control algorithms. In terms of storage architecture, it is equipped with 1MB Flash memory and 288KB SRAM, supplemented by 16KB high-speed cache (I/D Cache) and up to 256KB zero-wait instruction and local data memory (ILM/DLM). It carries high-performance analog peripherals including 2-channel 16-bit ADC (2MSPS), 2 OPMAPs configurable as PGAs, and 2-channel 12-bit DAC (1MSPS). It can collect current, voltage, temperature and position sensing signals at ultra-high rates in real time, enabling faster dynamic joint response and qualitative improvements in control precision.

Table of Contents

1 Definition of Embodied Artificial Intelligence (EAI) and Hardware Introduction

  • 1.1 Basic Concepts and Terms of EAI
  • 1.2 Hardware Architecture of EAI
  • 1.3 EAI Actuators (Joints)

2 EAI Microcontroller Unit (MCU) Market and Trends

  • 2.1 Concept and Architecture of MCU
  • 2.2 Quantity and Distribution of MCUs in EAI
  • 2.3 Analysis of EAI MCU Suppliers
  • China's EAI MCU Market Forecast
  • Analysis of EAI MCU Suppliers (1)
  • Analysis of EAI MCU Suppliers (2)
  • Analysis of EAI MCU Suppliers (3)
  • 2.4 Evolution Trends of MCUs for EAI
  • Evolution Trend 1 of EAI MCUs
  • Evolution Trend 2 of EAI MCUs
  • Evolution Trend 3 of EAI MCUs

3 Application of Microcontroller Units (MCUs) for EAI

  • 3.1 Overview of MCU Applications in EAI
  • Overview of MCU Applications in EAI (1)
  • Overview of MCU Applications in EAI (2)
  • Overview of MCU Products for Cerebellum Core Control System
  • Overview of MCU Products for Joint Control System
  • Overview of MCU Products for Dexterous Hands
  • 3.2 Applications of MCUs in Core Control System
  • Applications of MCUs in Core Control System (1)
  • Applications of MCUs in Core Control System: Evolution Trend of Cerebellum System
  • Applications of MCUs in Core Control System: Application Trend of MCUs in Cerebellum System
  • Applications of MCUs in Core Control System: Parameter Comparison between New Products
  • Core Control System MCU Application Solutions
  • 3.3 Applications of MCUs in Joint Control System
  • Structure of Robot Joint Modules (1)
  • Structure of Robot Joint Modules (2)
  • Joint Control System MCUs
  • Evolution Trend of Joint Control System
  • Application Trend of MCUs in Joint Control System
  • Joint Control System MCUs: Parameter Comparison between New Products
  • Joint Control System MCU Solutions (1)
  • Joint Control System MCU Solutions (2)
  • Joint Control System MCU Solutions (3)
  • 3.4 Applications of MCUs in Dexterous Hands
  • Dexterous Hand MCUs
  • Evolution Trends of Dexterous Hands
  • Application Trend of MCUs in Dexterous Hands
  • Dexterous Hand MCUs: Parameter Comparison between New Products
  • Dexterous Hand MCU Solutions (1)
  • Dexterous Hand MCU Solutions (2)
  • Dexterous Hand MCU Solutions (3)
  • 3.5 Applications of MCUs in Arm/Leg Joints
  • Robot Arm/Leg Joint MCU Solutions (1)
  • Robot Arm/Leg Joint MCU Solutions (2)
  • 3.6 Applications of MCUs in Perception System
  • Perception System MCUs
  • Perception System Solutions (1)
  • Perception System Solutions (2)
  • 3.7 Applications of MCUs in Power Management System
  • Monitoring and Management of BMS (Battery Management System) by MCU
  • BMS MCU Monitoring and Management Solutions (1)
  • BMS MCU Monitoring and Management Solutions (2)

4 Downstream Market Demand Analysis of MCUs

  • 4.1 Downstream MCU Market Segment: Dexterous Hands
  • Overview of Dexterous Hands
  • Technical Routes of Dexterous Hands
  • Advantages and Disadvantages of Technical Routes for Dexterous Hands
  • Dexterous Hand MCU Market Trend Forecast
  • Analysis of Mainstream Dexterous Hand Suppliers (1)
  • Latest Technologies of Dexterous Hand Suppliers (1)
  • Latest Technologies of Dexterous Hand Suppliers (2)
  • 4.2 Downstream MCU Market Segment: Cerebellum Control
  • Overview of Technical Paths of Cerebellum Control System
  • Technical Paths of Cerebellum Control System (1)
  • Technical Paths of Cerebellum Control System (2)
  • Cerebellum Control System MCU Market Forecast

5 MCU Application by EAI OEMs

  • 5.1 UBTECH
  • Product Strategy
  • Overview of Robot Configurations
  • Overview of Robot Hardware and Software
  • Parameter Comparison between General-purpose Humanoid Robot Products (1)
  • Parameter Comparison between General-purpose Humanoid Robot Products (2)
  • Evolution of Dexterous Hands
  • 5th-Generation Dexterous Hands
  • MCU Solutions
  • 5.2 AgiBot
  • Profile
  • Overview of Robot Configurations (1)
  • Overview of Robot Configurations (2)
  • Overview of Robot Hardware and Software
  • Parameter Comparison between Humanoid Robot Products (1)
  • Parameter Comparison between Humanoid Robot Products (2)
  • Model Solutions (1)
  • Model Solutions (2)
  • Dexterous Hand Solutions (1)
  • Dexterous Hand Solutions (2)
  • 5.3 Unitree Robotics
  • Profile
  • Overview of Robot Configurations (1)
  • Overview of Robot Configurations (2)
  • Overview of Robot Hardware and Software
  • Parameter Comparison between Quadruped Robot Products (1)
  • Detailed Parameters of Dexterous Hands (1)
  • Detailed Parameters of Dexterous Hands (2)
  • Detailed Parameters of Dexterous Hands (3)
  • Self-Developed Dex5-1 Dexterous Hand
  • 5.4 Booster Robotics
  • Profile
  • Overview of Robot Configurations
  • Parameter Comparison between Robot Products (1)
  • Parameter Comparison between Robot Products (2)
  • Self-Developed Custom Joint Motor Booster T1
  • 5.5 ROBOTERA
  • Profile
  • Overview of Robot Configurations
  • Overview of Robot Hardware and Software
  • Detailed Parameters of Dexterous Hands (1)
  • Detailed Parameters of Dexterous Hands (2)
  • 5.6 EngineAI
  • Profile
  • Overview of Robot Configurations
  • T800 Multi-Dimensional Perception Dexterous Hand
  • Self-Developed Micro Joint Mechatronics
  • Energy and Structural Patents
  • Joint Technology Patents
  • 5.7 Noetix Robotics
  • Profile
  • Overview of Robot Configurations
  • Overview of Robot Hardware and Software
  • 5.8 Fourier Intelligence
  • Profile
  • Overview of Robot Configurations
  • Actuator Solutions
  • Dexterous Hand Solutions
  • 5.9 DEEP Robotics
  • Overview of Joint Modules
  • Joint Solutions (1)
  • Joint Solutions (2)
  • 5.10 Galbot
  • Overview of Hardware and Software
  • Detailed Parameters of Robots (1)
  • 5.11 Xpeng IRON
  • Profile of Xpeng Motors
  • Xpeng IRON Robot: Commercialization Progress and Future Plan
  • Xpeng IRON Humanoid Robot: Parameter Comparison between Products (1)
  • Xpeng IRON Robot: Dexterous Hand Solutions (1)
  • Xpeng IRON Robot: Dexterous Hand Solutions (2)
  • 5.12 Xiaomi
  • Parameters of Xiaomi CyberOne Robot (1)
  • Xiaomi Robot: Bionic Hand Solutions (1)
  • Xiaomi Robot: Bionic Hand Solutions (2)
  • 5.13 Tesla
  • Parameters of Tesla Optimus (1)
  • Parameters of Tesla Optimus (2)
  • Tesla Optimus Gen 3: Dexterous Hand Solutions (1)
  • Tesla Optimus Gen 3: Dexterous Hand Solutions (2)
  • Tesla Optimus Gen 3: Dexterous Hand Solutions (3)
  • 5.14 Figure AI
  • Profile
  • Overview of Configurations, Hardware and Software of Robots
  • Dexterous Hand Solutions
  • Humanoid Robot Factories

6 Solutions of EAI MCU Suppliers

  • 6.1 NXP
  • Motor and Motion Control for EAI
  • Microcontrollers (1)
  • Microcontrollers (2)
  • Basic Robot Solutions (1)
  • Basic Robot Solutions (2)
  • Servo Motor Drivers
  • Motor and Motion Control for EAI
  • 6.2 Texas Instruments (TI)
  • Sensor Solutions (1)
  • Sensor Solutions (2)
  • Vision System Processors
  • NPU Designed for MCUs
  • Full-Stack Development Resources
  • 6.3 STMicroelectronics
  • Full-Stack Robot Solutions
  • Detailed Parameters of MCUs for EAI
  • Actuator Solutions for Humanoid Robots
  • Motor Controller Solutions
  • Vision System and Power Supply Solutions for Humanoid Robots
  • MCU Application Solutions for Leg Joints
  • Power Management Solutions
  • Main Processor Solutions for Robot Body
  • 6.4 Renesas Electronics
  • Humanoid Robot Solutions
  • Main Control SoC Solutions for Humanoid Robots (1)
  • Main Control SoC Solutions for Humanoid Robots (2)
  • Main Control SoC Solutions for Humanoid Robots (3)
  • Motor Control Microcontrollers
  • Robot Dexterous Hand Solutions
  • DC Servo System Solutions
  • DC Servo System Solutions
  • Battery Management System for Humanoid Robots
  • LiDAR-Based SLAM Vision Mapping System
  • 6.5 Infineon
  • Full-Stack Solutions for Humanoid Robots
  • Motor Control MCU Solutions (1)
  • Motor Control MCU Solutions (2)
  • GaN (Gallium Nitride) Motor Control Solutions (1)
  • GaN (Gallium Nitride) Motor Control Solutions (2)
  • 6.6 HPMicro
  • Detailed Parameters of EAI Joint MCUs (1)
  • Detailed Parameters of EAI Motion MCUs (2)
  • Robot Control Chip Product Series
  • Joint Servo Solutions (1)
  • Joint Servo Solutions (2)
  • Robot Joint Application Solutions
  • Real-Time Communication Solutions Inside Robots
  • 6.7 GigaDevice
  • Full-Stack Chip Solutions for Robots
  • Dexterous Hand MCUs
  • Arm Joint MCUs (1)
  • Arm Joint MCUs (2)
  • Leg Joint MCUs
  • 6-Axis Robotic Arm Solutions
  • Robot Joint Solutions
  • 6-Axis Force Detection Solutions
  • 6.8 Huawei HiSilicon
  • Detailed Parameters of EAI MCUs (1)
  • Detailed Parameters of EAI MCUs (2)
  • Self-Developed RISC-V Core High-Performance Real-Time Control Dedicated MCUs
  • Vision Modules
  • Sensors
  • HarmonyOS Empowered Intelligent Robotic Arms
  • 6.9 Nsing Technologies
  • Detailed Parameters of EAI MCUs
  • Full-Stack MCU Solutions for Embodied Robots
  • Dexterous Hand MCU Solutions
  • Robot Joint MCU Solutions
  • N32 Series Robot Servo Drivers
  • Full-Stack Solutions for Quadruped Robots (1)
  • Full-Stack Solutions for Quadruped Robots (2)
  • 6.10 Geehy Semiconductor
  • Intelligent Joint Servo Solutions for Robots (1)
  • Intelligent Joint Servo Solutions for Robots (2)
  • Intelligent Joint Servo Solutions for Robots (3)
  • Absolute Encoder Solutions (1)
  • Absolute Encoder Solutions (2)
  • 6.11 Allwinner Technology
  • Detailed Parameters of MCUs for EAI
  • Cerebellum Control Solutions
  • Main Control Chips for Humanoid Robots
  • 6.12 Artery Technology
  • Dexterous Hand Joint Control (1)
  • Dexterous Hand Joint Control (2)
  • MCU Application Solutions
  • 6.13 Huada Semiconductor
  • Parameters of EAI-oriented Chips
  • MCU Solutions
  • 6.14 SemiDrive
  • EAI Products Application and Planning
  • Strategy 2.0 - From Driving Intelligence to General Intelligence
  • Detailed Parameters of EAI Cerebrum SoCs (1)
  • Detailed Parameters of EAI Cerebrum SoCs (2)
  • Detailed Parameters of EAI Cerebellum SoCs (1)
  • Detailed Parameters of EAI Cerebellum SoCs (2)
  • Detailed Parameters of High-Performance MCUs for EAI (1)
  • Detailed Parameters of High-Performance MCUs for EAI (2)
  • Joint Module Solutions
  • Dexterous Hand Solutions
  • LiDAR Solutions
  • 6.15 Rockchip
  • Profile
  • Product Parameters (1)
  • Product Parameters (2)
  • MCU Solutions (1)
  • MCU Solutions (2)
  • 6.16 Unisoc
  • Detailed Parameter of Main Control SoCs for EAI
  • Agentic AI Chip Solutions
  • 6.17 Jingwei HiRain
  • Solutions (1)
  • Solutions (2)
  • 6.18 MindMotion
  • Joint Control MCUs (1)
  • Joint Control MCUs (2)