The novel coronavirus disease 2019 (COVID-19) is an acute infectious disease caused by infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Currently, the World Health Organization has confirmed that COVID-19 is a global infectious disease pandemic. This is the third acute infectious disease caused by coronavirus infection in this century, after sudden acute respirator syndrome and Middle East respiratory syndrome. The damage mechanism of SARS-CoV-2 is still unclear. It is possible that protein S binds to angiotensin-converting enzyme 2 receptors and invades alveolar epithelial cells, causing direct toxic effects and an excessive immune response. This stimulates a systemic inflammatory response, thus forming a cytokine storm, which leads to lung tissue injury. In severe cases, the disease can lead to acute respiratory distress syndrome, septic shock, metabolic acidosis, coagulation dysfunction, and multiple organ dysfunction syndromes. Patients with severe COVID-19 have a relatively high mortality rate. Currently, there are no specific antiviral drugs for the treatment of COVID-19. Most patients need to be admitted to the intensive care unit for intensive monitoring and supportive organ function treatments. This article reviews the epidemiology, pathogenesis, clinical manifestations, diagnosis, and treatment methods of severe COVID-19 and puts forward some tentative ideas, aiming to provide some guidance for the diagnosis and treatment of severe COVID-19.

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Dynamic Movement Primitives (DMP) are widely applied in movement representation due to their ability to encode tasks using generalization properties. However, the coupled multiple DMP generalization cannot be directly solved based on the original DMP formula. Prior works provide satisfactory performance for the coupled DMP generalization in rigid object manipulation, but their extension to deformable objects may degrade due to the intrinsic uncertainty of the deformable model structure and parameters. This paper introduces an adaptive term to replace the fixed term to couple multiple DMP generalizations and model the deformable object using the classic mass-spring-damper model. Based on the modeling, the manipulation of a deformable object can be treated as a second-order system, which provides additional implementation flexibility and robustness in deformable object transportation. To validate the proposed method, we perform extensive simulations for cooperatively transporting a rope and a deformable thin film, imitating the manipulation with a semi-ellipse trajectory and M-shape trajectory. We further implement our method on a dual-arm robot platform for rope manipulation with depth visual feedback. Both simulation and experiment results demonstrate satisfactory DMP generalization, collision avoidance, and configuration preservation.

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