A layered control architecture for mobile robot navigation

A Thesis submitted to the University Research Degree Committee in fulfillment ofthe requirements for the degree of DOCTOR OF PHILOSOPHY in RoboticsThis thesis addresses the problem of how to control an autonomous mobile robot navigation in indoor environments, in the face of sensor noise, imprecise information, uncertainty and limited response time. The thesis argues that the effective control of autonomous mobile robots can be achieved by organising low level and higher level control activities into a layered architecture. The low level reactive control allows the robot to respond to contingencies quickly. The higher level control allows the robot to make longer term decisions and arranges appropriate sequences for a task execution. The thesis describes the design and implementation of a two layer control architecture, a task template based sequencing layer and a fuzzy behaviour based low level control layer. The sequencing layer works at the pace of the higher level of abstraction, interprets a task plan, mediates and monitors the controlling activities. While the low level performs fast computation in response to dynamic changes in the real world and carries out robust control under uncertainty. The organisation and fusion of fuzzy behaviours are described extensively for the construction of a low level control system. A learning methodology is also developed to systematically learn fuzzy behaviours and the behaviour selection network and therefore solve the difficulties in configuring the low level control layer. A two layer control system has been implemented and used to control a simulated mobile robot performing two tasks in simulated indoor environments. The effectiveness of the layered control and learning methodology is demonstrated through the traces of controlling activities at the two different levels. The results also show a general design methodology that the high level should be used to guide the robot's actions while the low level takes care of detailed control in the face of sensor noise and environment uncertainty in real time

Application Effect of Limited Fluid Resuscitation in Emergency Patients with Multiple Trauma Complicated with Shock

This article explores the methods and effects of limited fluid resuscitation in the treatment of hemorrhagic shock caused by multiple trauma, which is common in clinic. 80 patients with multiple trauma complicated with shock were randomly selected from the emergency department of our hospital and divided into the observation group and the control group, with 40 members in each group. Patients in the observation group were treated with limited fluid resuscitation, while those in the control group were treated with aggressive fluid resuscitation. By comparing the therapeutic effects of the two groups, it is concluded that the therapeutic effect of the observation group is significantly better than that of the control group. Therefore, adopting limited fluid resuscitation in the clinical treatment of patients with multiple trauma complicated with shock can realize faster recovery, as well as protect patients’ coagulation function, effectively reducing complications and mortality. Moreover, it can also reduce the injury of trauma perfusion to the body, ensuring the recovery of patients

Matematični model in analiza toplotnih lastnosti sten letalske kabine pod tlakom

In the present paper, we investigate the heat transfer through the multilayer wall of aircraft cabins, as this process influences the comfortable conditions created for most passengers and crew members. The numerical modelling results and calculation of a multilayer wall with a hat-stringer in aircraft design are presented. The thermal characteristics evaluation and their relationship with the design parameters were made. The effect of the air layer size on the overall thermal resistance of the multilayer wall, taking into account the geometrical dimensions and properties of the surfaces, was studied. The relative temperature field in the insulation layer, which crosses the hat-stringer elements of the fuselage frame, is calculated. It is shown that the insufficient thickness of the layer of thermal insulation material in the zone of the hat-stringer at low temperatures leads to a significant deterioration in the multilayer wall characteristics, which can worsen the microclimatic conditions inside the aircraft cabin

Optimizing BiLSTM Network Attack Prediction Based on Improved Gray Wolf Algorithm

Aiming at the problems of low accuracy of network attack prediction and long response time of attack detection, bidirectional long short-term memory (BiLSTM) was used to predict network attacks. However, BiLSTM has the problems of difficulty in parameter setting and low accuracy of the prediction model. This paper first proposes the Improved Grey Wolf algorithm (IGWO) to optimize the BiLSTM (IGWO-BiLSTM). First, IGWO uses Dimension Learning Hunting (DLH) strategy to construct the wolf neighborhood. In the established wolf neighborhood, the BiLSTM parameters are iteratively optimized to obtain a prediction model with fast convergence speed and small reconstruction error. Secondly, the dataset is preprocessed, and the IP packet statistical signature (IPDCF) is defined according to the characteristics of denial of service (DOS) and distributed denial of service (DDOS) attacks. IPDCF was used to establish the time series model and network traffic time series data were input into IGWO-BiLSTM to get the prediction results. Finally, the DOS and DDOS network packets were input into the trained prediction model to obtain the prediction results of attack data. By comparing the predicted values of IGWO-BiLSTM normal network packets and attack packets, a reasonable threshold is set to provide the basis for the subsequent attack prediction. Experiments show that the IGWO-BiLSTM can reach 99.05% of the fitting degree and accurately distinguish network attacks from normal network demand increases