公开(公告)号：US20240289973A1

公开(公告)日：2024-08-29

申请号：US18570521

申请日：2022-06-14

申请人： Arizona Board of Regents on Behalf of Arizona State University

发明人： Xiao Liu ,  Geoffrey Clark ,  Heni Ben Amor

IPC分类号： G06T7/55 ,  A61F2/66 ,  A61F2/70 ,  A61H1/02 ,  A61H3/00 ,  G06T7/11

CPC分类号： G06T7/55 ,  A61F2/6607 ,  A61H1/0266 ,  A61H3/00 ,  G06T7/11 ,  A61F2002/704 ,  G06T2207/10024 ,  G06T2207/20016 ,  G06T2207/20084

摘要： An environment-aware prediction and control framework, which incorporates learned environment and terrain features into a predictive model for human-robot symbiotic walking, is disclosed herein. First, a compact deep neural network is introduced for accurate and efficient prediction of pixel-level depth maps from RGB inputs. In turn, this methodology reduces the size, weight, and cost of the necessary hardware, while adding key features such as close-range sensing, filtering, and temporal consistency. In combination with human kinematics data and demonstrated walking gaits, the extracted visual features of the environment are used to learn a probabilistic model coupling perceptions to optimal actions. The resulting data-driven controllers. Bayesian Interaction Primitives, can be used to infer in real-time optimal control actions for a lower-limb prosthesis. The inferred actions naturally take the current state of the environment and the user into account during walking.