Autonomous tumor palpation and resection path planning using tactile array sensor and deep reinforcement learning for surgical robot

Feng Ju, Haoran Ye, Dongming Bai, Yingxuan Zhang, Chengjun Zhu, Yanfei Cao, Wenchao Yue
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Abstract

Surgical robots have been widely used in tumor resection, but they still have shortcomings such as the lack of tactile perception, which may lead to inaccurate intraoperative tumor identification and resection. A piezoresistive tactile array sensor is proposed in this paper, which features small size (10 mm X 10 mm) as well as the high-efficiency array detection mode. The sensing principle is simply applying a constant voltage to each tactile element and measuring the current to generate a tactile image. Its effectiveness and performance are verified by finite element simulations. In addition, a deep reinforcement learning-based autonomous detection algorithm is developed to further improve the detection efficiency and facilitate the planning of the resection path, which provides an effective guarantee for accurate tumor resection in future autonomous robotic surgeries.
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基于触觉阵列传感器和深度强化学习的手术机器人肿瘤自主触诊和切除路径规划
手术机器人在肿瘤切除中得到了广泛的应用,但仍存在缺乏触觉感知等缺点,可能导致术中肿瘤的识别和切除不准确。本文提出了一种压阻式触觉阵列传感器,该传感器具有体积小(10mm × 10mm)和高效阵列检测方式的特点。传感原理是简单地对每个触觉元件施加恒定电压并测量电流以产生触觉图像。通过有限元仿真验证了该方法的有效性和性能。此外,开发了一种基于深度强化学习的自主检测算法,进一步提高了检测效率,方便了切除路径的规划,为未来自主机器人手术中肿瘤的准确切除提供了有效保障。
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