4分14秒！同济具身智造团队创造纪录，揭秘双臂机器人的"进化"之路

Core Viewpoint - The article highlights the significant achievement of Tongji University's embodied intelligence team in the 2025 Zhangjiang Embodied Intelligence Developer Conference and International Humanoid Robot Skills Competition, where they set a record of 4 minutes and 14 seconds in the box loading and unloading task, showcasing the practical application of embodied intelligence technology in industrial settings [1][14]. Group 1: Technical Achievements - The team achieved a positioning accuracy of ±0.2mm using Intel RealSense D435 depth camera combined with visual SLAM algorithms, demonstrating the feasibility of robots replacing humans in high-repetition, harsh industrial environments [2]. - The robot's design includes a 7-degree-of-freedom humanoid arm that mimics human arm joint structures, allowing for complex posture adjustments in confined spaces [2]. - The team improved task deployment efficiency by 60% through an AI-driven autonomous learning system [2]. Group 2: System Design and Collaboration - The dual-arm robot system expands the capabilities of industrial robots, with an 800mm lift column design that allows a working range of 200-2000mm, addressing the need for space utilization in factories [4]. - The left arm is responsible for identifying and placing parts, while the right arm transports them to the target box, with future research focusing on communication mechanisms for parallel scheduling to enhance overall efficiency [4][6]. - The perception system integrates a global vision system and multiple depth cameras, enabling dynamic obstacle avoidance and safe operation in unstructured environments [6]. Group 3: Real-World Application and Challenges - The team conducted real-world tests in collaboration with factories, overcoming challenges such as operating in confined spaces and dynamic lighting conditions [7][8]. - They utilized precise 3D environmental modeling to identify potential obstacles and set virtual safety boundaries, ensuring safe and reliable task execution in crowded environments [8]. - To address lighting variability, the team implemented hardware solutions like integrated compensatory light sources and software improvements for robust visual perception [8]. Group 4: Future Outlook - The team aims to achieve breakthroughs in three technical areas over the next 3-5 years: enhancing multi-modal perception, establishing high-fidelity industrial digital twin environments, and developing safe human-robot collaboration frameworks [12][13]. - The core challenges in industrial manufacturing include the constraints of dynamic, unstructured environments and the need for robust multi-modal perception and real-time decision-making [13]. - Opportunities lie in creating value loops that move from traditional automation to global autonomous responses, applicable in flexible assembly quality inspection and dynamic production line optimization [13].