RESUMO
Unmanned Aerial Vehicles are useful tools for many applications. However, autonomous path planning for Unmanned Aerial Vehicles in unfamiliar environments is a challenging problem when facing a series of problems such as poor consistency, high influence by the native controller of the Unmanned Aerial Vehicles. In this paper, we investigate reinforcement learning-based autonomous local path planning methods for Unmanned Aerial Vehicles with high autonomous decision-making capability and locally high portability. We propose an autonomous local path planning algorithm based on the TD3 strategy to solve the problem of local obstacle avoidance and path planning in unfamiliar environments using autonomous decision-making of Unmanned Aerial Vehicles. The simulation results on Gazebo show that our method can effectively realize the autonomous local path planning task for Unmanned Aerial Vehicles, the success rate of path planning with our method can reach 93% under the interference of no obstacles, and 92% in the environment with obstacles. Finally, our method can be used for autonomous path planning of Unmanned Aerial Vehicles in unfamiliar environments.
RESUMO
Objective To investigate the predictive value of acute physiology and chronic health evaluation Ⅱ(APACHE Ⅱ) for nursing workload in emergency intensive care unit (EICU).Methods A retrospective cohort study was conducted. Critical patients except for trauma admitted to EICU of West China Hospital of Sichuan University from June 2012 to July 2016 were enrolled. APACHE Ⅱ score and therapeutic intervention scoring system (TISS-28) score were calculated in each case. The relationship between APACHE Ⅱ score and TISS-28 score was analyzed by correlation analysis and curve estimation.Results A total of 1148 patients were enrolled, including 689 male (60.0%), and age of (56±19) years old, APACHE Ⅱ score was 19 (13, 25), and TISS score was 19 (13, 25). The Spearman correlation coefficient between APACHE Ⅱ score and TISS-28 score was 0.849 (P < 0.001), and that among the linear model quadratic model, composite model, growth model, logarithm model, cubic model and exponential modelR2 were 0.669, 0.710, 0.792, 0.764, 0.765, 0.765, respectively (allP < 0.01), and the most suitable description for relationship between APACHE Ⅱ score and TISS-28 score curve was cubic model. The nursing workload was significantly increased when APACHE Ⅱ score ≥30, and it was decreased when APACHE Ⅱ score < 30.Conclusion APACHE Ⅱ was a useful score for predicting the nursing workload in EICU.
