Path Planning for Autonomous Vehicle Based on a Two-Layered Planning Model in Complex Environment

The autonomous vehicle consists of perception, decision-making, and control system. The study of path planning method has always been a core and difficult problem, especially in complex environment, due to the effect of dynamic environment, the safety, smoothness, and real-time requirement, and the nonholonomic constraints of vehicle. To address the problem of travelling in complex environments which consists of lots of obstacles, a two-layered path planning model is presented in this paper. This method includes a high-level model that produces a rough path and a low-level model that provides precise navigation. In the high-level model, the improved Bidirectional Rapidly-exploring Random Tree (Bi-RRT) based on the steering constraint is used to generate an obstacle-free path while satisfying the nonholonomic constraints of vehicle. In low-level model, a Vector Field Histogram- (VFH-) guided polynomial planning algorithm in Frenet coordinates is introduced. Based on the result of VFH, the aim point chosen from improved Bi-RRT path is moved to the most suitable location on the basis of evaluation function. By applying quintic polynomial in Frenet coordinates, a real-time local path that is safe and smooth is generated based on the improved Bi-RRT path. To verify the effectiveness of the proposed planning model, the real autonomous vehicle has been placed in several driving scenarios with different amounts of obstacles. The two-layered real-time planning model produces flexible, smooth, and safe paths that enable the vehicle to travel in complex environment. Document type: Articl

Emergency tracheal intubation in 202 patients with COVID-19 in Wuhan, China:lessons learnt and international expert recommendations

Tracheal intubation in coronavirus disease 2019 (COVID-19) patients creates a risk to physiologically compromised patients and to attending healthcare providers. Clinical information on airway management and expert recommendations in these patients are urgently needed. By analysing a two-centre retrospective observational case series from Wuhan, China, a panel of international airway management experts discussed the results and formulated consensus recommendations for the management of tracheal intubation in COVID-19 patients. Of 202 COVID-19 patients undergoing emergency tracheal intubation, most were males (n=136; 67.3%) and aged 65 yr or more (n=128; 63.4%). Most patients (n=152; 75.2%) were hypoxaemic (Sao2 <90%) before intubation. Personal protective equipment was worn by all intubating healthcare workers. Rapid sequence induction (RSI) or modified RSI was used with an intubation success rate of 89.1% on the first attempt and 100% overall. Hypoxaemia (Sao2 <90%) was common during intubation (n=148; 73.3%). Hypotension (arterial pressure <90/60 mm Hg) occurred in 36 (17.8%) patients during and 45 (22.3%) after intubation with cardiac arrest in four (2.0%). Pneumothorax occurred in 12 (5.9%) patients and death within 24 h in 21 (10.4%). Up to 14 days post-procedure, there was no evidence of cross infection in the anaesthesiologists who intubated the COVID-19 patients. Based on clinical information and expert recommendation, we propose detailed planning, strategy, and methods for tracheal intubation in COVID-19 patients

The Role of Government Support in Daily Business Operations and Information Technology Innovation of Medical Tourism Enterprises under COVID-19

COVID-19 has posed unprecedented challenges to medical tourism due to the global nature of its pandemic effect. Government support plays an essential role in the recovery and further development of the medical tourism industry. With the support of government, medical tourism enterprises, on the one hand, will maintain their daily business operations and wait for the mitigation of the pandemic. On the other hand, they will carry out information technology innovation in medical tourism and develop new businesses to create profits. Therefore, we construct a multitask principal-agent model for government and enterprises in the medical tourism industry, and discuss the influence of several critical exogenous variables on the decision strategies of the government and enterprises from the perspective of resilience. Numerical simulation is used to verify the effectiveness of the relevant conclusions. This paper can provide some decision-making basis and reference to realize the digital transformation and sustainable development of medical tourism industry under the COVID-19 crisis

Optimal Energy Consumption Path Planning for Quadrotor UAV Transmission Tower Inspection Based on Simulated Annealing Algorithm

In order to improve the efficiency of UAVs in transmission tower inspections, the UAV transmission tower inspection energy consumption model is proposed for the existing research in which there is no accurate energy consumption calculation method in transmission tower inspection, and the optimal energy consumption path for UAV transmission tower inspection is designed by combining with simulated annealing algorithm. Firstly, a real experimental environment is built for experimental data collection and analysis, and the energy consumption model for transmission tower inspection is constructed and the influencing factors are discussed and analyzed, and the energy consumption coefficients under different situations are obtained. Second, according to the constructed transmission tower inspection energy consumption model combined with the path planning algorithm, experimental simulation is conducted to plan the optimal energy consumption inspection path, and finally, the above results are verified by carrying out actual measurement experiments. The simulation results show that under different constant loads, the optimal energy consumption path in this paper can save 36.53% and 27.32% compared with the conventional path; compared with the shortest path, it can save 11.16% and 0.45%. The optimal energy consumption path of UAV transmission tower inspection based on the simulated annealing algorithm proposed in this paper effectively improves the efficiency of UAV transmission tower inspection

Optimal Energy Consumption Path Planning for Quadrotor UAV Transmission Tower Inspection Based on Simulated Annealing Algorithm

In order to improve the efficiency of UAVs in transmission tower inspections, the UAV transmission tower inspection energy consumption model is proposed for the existing research in which there is no accurate energy consumption calculation method in transmission tower inspection, and the optimal energy consumption path for UAV transmission tower inspection is designed by combining with simulated annealing algorithm. Firstly, a real experimental environment is built for experimental data collection and analysis, and the energy consumption model for transmission tower inspection is constructed and the influencing factors are discussed and analyzed, and the energy consumption coefficients under different situations are obtained. Second, according to the constructed transmission tower inspection energy consumption model combined with the path planning algorithm, experimental simulation is conducted to plan the optimal energy consumption inspection path, and finally, the above results are verified by carrying out actual measurement experiments. The simulation results show that under different constant loads, the optimal energy consumption path in this paper can save 36.53% and 27.32% compared with the conventional path; compared with the shortest path, it can save 11.16% and 0.45%. The optimal energy consumption path of UAV transmission tower inspection based on the simulated annealing algorithm proposed in this paper effectively improves the efficiency of UAV transmission tower inspection

A Novel Stability Analysis of Uncertain Switched Systems with Time-Varying Delays

This paper deals with the stability of switched systems with time-varying delay. The time-varying system parameters are assumed to be norm-bounded. Based on a novel switched time-varying Lyapunov functional method, some new LMI-based sufficient conditions have been obtained to ensure the exponential stability for the uncertain switched delays systems. Finally, the proposed method is applied to a numerical example and the simulative results are also given

Decentralized state estimation for hybrid AC/DC microgrids

This paper presents a decentralized state estimation (SE) for the newly emerging hybrid ac/dc microgrid. The microgrid consists of an ac and a dc network which are connected via the interlinking ac/dc converter. The proposed SE is able to estimate the state variables of both networks in a decentralized way that the estimation is separately conducted while only limited information is exchanged during the process. The dual decomposition approach is adopted as the decentralized technique in this paper. Simulation results suggest that the proposed decentralized SE performs comparably to the centralized SE for the hybrid ac/dc microgrids in terms of accuracy, convergence, and robustness