Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, 518060, China.
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China.
Adv Sci (Weinh). 2024 Nov;11(41):e2402705. doi: 10.1002/advs.202402705. Epub 2024 Sep 9.
Humans recognize and manipulate objects relying on the multidimensional force features captured by the tactile sense of skin during the manipulation. Since the current sensors integrated in robots cannot support the robots to sense the multiple interaction states between manipulator and objects, achieving human-like perception and analytical capabilities remains a major challenge for service robots. Prompted by the tactile perception involved in robots performing complex tasks, a multimodal tactile sensory system is presented to provide in situ simultaneous sensing for robots when approaching, touching, and manipulating objects. The system comprises a capacitive sensor owning the high sensitivity of 1.11E-2 pF mm, a triboelectricity nanogenerator with the fast response speed of 30 ms, and a pressure sensor array capable of 3D force detection. By Combining transfer learning models, which fuses multimodal tactile information to achieve high-precision (up to 95%) recognition of the multi-featured targets such as random hardness and texture information under random sampling conditions, including random grasp force and velocity. This sensory system is expected to enhance the intelligent recognition and behavior-planning capabilities of autonomous robots when performing complex tasks in undefined surrounding environments.
人类在操作过程中依靠皮肤触觉感知到的多维力特征来识别和操纵物体。由于当前集成在机器人中的传感器无法支持机器人感知操纵器和物体之间的多种交互状态,因此实现类人的感知和分析能力仍然是服务机器人面临的主要挑战。受机器人执行复杂任务时涉及的触觉感知的启发,提出了一种多模态触觉传感系统,为机器人在接近、触摸和操纵物体时提供原位同步感知。该系统包括具有 1.11E-2 pF mm 高灵敏度的电容传感器、具有 30 ms 快速响应速度的摩擦电纳米发电机和能够进行 3D 力检测的压力传感器阵列。通过结合迁移学习模型,融合多模态触觉信息,实现了在随机采样条件下(包括随机抓取力和速度)对随机硬度和纹理信息等多特征目标的高精度(高达 95%)识别。该传感系统有望增强自主机器人在不确定周围环境中执行复杂任务时的智能识别和行为规划能力。