Autonomous surgery has attracted increasing attention for revolutionizing robotic patient care, yet remains a distant and challenging goal. In this paper, we propose an image-based framework for high-precision autonomous suturing operation. We first build an algebraic geometric algorithm to achieve accurate needle pose estimation, then design the corresponding keypoint-based calibration network for joint-offset compensation, and further plan and control suture trajectory. Our solution ranked first among all competitors in the AccelNet Surgical Robotics Challenge. The source code is opened here to accelerate future autonomous surgery research.
翻译:自主手术吸引了人们对机器人病人护理革命的日益关注,但仍然是一个遥远而具有挑战性的目标。在本文中,我们提出了高精度自主脉冲操作的基于图像的框架。我们首先建立一个代数几何算法,以实现准确的针头构成估计,然后设计相应的基于关键点的混合补偿校准网络,并进一步计划和控制缝线轨迹。我们的解决方案在AccelNet外科机器人挑战中名列前茅。源代码在这里打开,以加速未来的自主手术研究。