论文标题
使用最小深度信息和颤振反馈的半自治假体控制
Semi-autonomous Prosthesis Control Using Minimal Depth Information and Vibrotactile Feedback
论文作者
论文摘要
基于计算机视觉的半自治假体控制器可改善性能，同时减少认知工作。但是，由于处理点云的计算需求，依靠全深度数据的控制器面临嵌入式假体控制器的部署。为了解决这个问题，本研究提出了一种从最小深度数据中重建各种每日对象的形状的方法。这是使用四个并发激光扫描仪线代替全点云来实现的。这些线代表对象横截面的部分轮廓，使其尺寸和方向可以使用简单的几何形状重建。使用具有四个激光扫描仪的深度传感器实现了对照原型。颤振反馈还旨在帮助用户正确将传感器瞄准目标对象。十名健壮的志愿者使用了配备了新型控制器的假肢来掌握十个不同形状，大小和方向的物体。为了进行比较，他们还测试了使用全深信息的现有基准控制器。结果表明，新型控制器处理了所有对象，尽管训练的性能得到了改善，但略低于基准。这标志着迈出基于紧凑的视力系统的重要一步，用于嵌入深度感应假体抓地。
Semi-autonomous prosthesis controllers based on computer vision improve performance while reducing cognitive effort. However, controllers relying on full-depth data face challenges in being deployed as embedded prosthesis controllers due to the computational demands of processing point clouds. To address this, the present study proposes a method to reconstruct the shape of various daily objects from minimal depth data. This is achieved using four concurrent laser scanner lines instead of a full point cloud. These lines represent the partial contours of an object's cross-section, enabling its dimensions and orientation to be reconstructed using simple geometry. A control prototype was implemented using a depth sensor with four laser scanners. Vibrotactile feedback was also designed to help users to correctly aim the sensor at target objects. Ten able-bodied volunteers used a prosthesis equipped with the novel controller to grasp ten objects of varying shapes, sizes, and orientations. For comparison, they also tested an existing benchmark controller that used full-depth information. The results showed that the novel controller handled all objects and, while performance improved with training, it remained slightly below that of the benchmark. This marks an important step towards a compact vision-based system for embedded depth sensing in prosthesis grasping.
