ﻻ يوجد ملخص باللغة العربية
Scene recognition model based on the DNN and game theory with its applications in human-robot interaction is proposed in this paper. The use of deep learning methods in the field of scene recognition is still in its infancy, but has become an important trend in the future. As the innovative idea of the paper, we propose the following novelties. (1) In this paper, the image registration problem is transformed into a problem of minimum energy in Markov Random Field to finalize the image pre-processing task. Game theory is used to find the optimal. (2) We select neighboring homogeneous sample features and the neighboring heterogeneous sample features for the extracted sample features to build a triple and modify the traditional neural network to propose the novel DNN for scene understanding. (3) The robot control is well combined to guide the robot vision for multiple tasks. The experiment is then conducted to validate the overall performance.
Robot capabilities are maturing across domains, from self-driving cars, to bipeds and drones. As a result, robots will soon no longer be confined to safety-controlled industrial settings; instead, they will directly interact with the general public.
Industrial standards define safety requirements for Human-Robot Collaboration (HRC) in industrial manufacturing. The standards particularly require real-time monitoring and securing of the minimum protective distance between a robot and an operator.
Given two consecutive RGB-D images, we propose a model that estimates a dense 3D motion field, also known as scene flow. We take advantage of the fact that in robot manipulation scenarios, scenes often consist of a set of rigidly moving objects. Our
When cooperating with a human, a robot should not only care about its environment and task but also develop an understanding of the partners reasoning. To support its human partner in complex tasks, the robot can share information that it knows. Howe
Today, physical Human-Robot Interaction (pHRI) is a very popular topic in the field of ground manipulation. At the same time, Aerial Physical Interaction (APhI) is also developing very fast. Nevertheless, pHRI with aerial vehicles has not been addres