Scholarlink

This chapter presents key innovations that significantly enhance the performance, efficiency, and sustainability of the AI (Artificial Intelligence) -powered bionic robot dog Wavego. We integrated long-range, low-power LoRa (Long Range) communication, enabling stable operation over 600 m. The proposed dual-controller architecture (ESP32 microcontroller for kinematics and an embedded Raspberry Pi computer for high-level AI tasks) optimizes resource utilization and system stability. To improve control, we developed both a web interface and a handheld remote equipped with a joystick and display, alongside real-time face recognition using computer vision. Experimental validation confirmed low latency (approximately 1.2 s), strong signal reception (up to –73 dBm, decibels relative to one milliwatt), and reliable operation in both indoor and outdoor environments. Furthermore, we implemented an intelligent RL (Reinforcement Learning)-based multichannel manager for dynamic selection among LoRa, Wi-Fi, BLE (Bluetooth Low Energy), LTE (Long-Term Evolution), GSM (Global System for Mobile Communications), ensuring adaptive, reliable and energy-efficient communication. Overall, the proposed approach advances sustainable and reliable robotic technologies aligned with the UN SDGs (Sustainable Development Goals).

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Advanced Remote-Control Systems in AI Bionic Dog Robots Using LoRa

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Yuriy Shkoropad,
  • Mykola Beshley,
  • Halyna Beshley,
  • Michal Gregus

摘要

This chapter presents key innovations that significantly enhance the performance, efficiency, and sustainability of the AI (Artificial Intelligence) -powered bionic robot dog Wavego. We integrated long-range, low-power LoRa (Long Range) communication, enabling stable operation over 600 m. The proposed dual-controller architecture (ESP32 microcontroller for kinematics and an embedded Raspberry Pi computer for high-level AI tasks) optimizes resource utilization and system stability. To improve control, we developed both a web interface and a handheld remote equipped with a joystick and display, alongside real-time face recognition using computer vision. Experimental validation confirmed low latency (approximately 1.2 s), strong signal reception (up to –73 dBm, decibels relative to one milliwatt), and reliable operation in both indoor and outdoor environments. Furthermore, we implemented an intelligent RL (Reinforcement Learning)-based multichannel manager for dynamic selection among LoRa, Wi-Fi, BLE (Bluetooth Low Energy), LTE (Long-Term Evolution), GSM (Global System for Mobile Communications), ensuring adaptive, reliable and energy-efficient communication. Overall, the proposed approach advances sustainable and reliable robotic technologies aligned with the UN SDGs (Sustainable Development Goals).