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This paper undertakes an examination of elevator car dispatching methods in response to hall calls. Firstly, our study focuses on the establishment of a representation of an elevator group control system as a finite-state machine to understand the dynamics of elevator group control. Secondly, two primary heuristics are explored, with the first advocating directional continuity unless the highest or lowest floor has been reached, while the second permits direction change upon completing the final call, regardless of floor extremes. Identified inefficiencies in these heuristic solutions lead us to explore enhanced alternatives. Consequently, we delve into genetic algorithm (GA) and simulated annealing (SA) methodologies. Our focus initially centers on devising solution representations and determining fitness evaluations for both approaches. We employ a simulation-based optimization approach to identify the optimal parameter values for both simulated annealing and genetic algorithms. A subsequent comparative analysis is conducted to ascertain the most effective approach among these diverse solutions. A comparative analysis reveals that the GA-based approach significantly outperforms both existing heuristics and the SA-based method in minimizing average passenger waiting time at the cost of longer computational time.

Artificial Intelligence (hereinafter referred to as AI) systems have recently found great application and use in various industries, such as data processing, data analysis, and the operation control of marine robotic complexes, etc. In view of the ever-increasing degree of complexity of the missions assigned to marine robotic systems, it is becoming obvious that the AI technologies should be used as combined systems which can provide control of marine robotic complexes (hereinafter referred to as MRCs), their navigation in sea, logic formation of MRC behaviour in uncertain environments, path planning, and processing optimization of the received MRC payload data. All the areas mentioned above are within the field of MRC development, and currently do not have a general solution. This article discusses the development process of an intelligent system for path planning of a group of marine robotic complexes. The architecture of the intelligent system is based on a cascade approach, which includes the consistent use of functional modules designed as various “organs of perception” of the system. A detailed description of the development of each module and mathematical modelling of the presented algorithms are provided in this paper, and the main results of the conducted full-scale experiments are demonstrated.

Glider-type autonomous underwater vehicles are today one of the most promising areas of marine robotics. This is confirmed by the frequent and remarkable results of various research missions and projects. The cumulative group application of underwater and no less innovative wave gliders can significantly reduce the time of obtaining oceanographic data. Together with wave gliders, one group of such robotic objects can significantly increase the efficiency, time and volume of obtaining oceanographic data. There is big interest in increasing the functionality of such a group. This article presents one of the possible alternatives to increase the functionality of a group of underwater and waveguide hang gliders. We present the process of upgrading the existing design, control algorithms and software of the SHADOW underwater glider, which was developed by the teams of the St. Petersburg State Marine Technical University (SMTU) and Okeanos JSC in order to jointly study the monitoring of underwater potentially dangerous objects with the St. Petersburg State Fire Service EMERCOM of Russia. A structural-functional approach to the group application of underwater and waveguides is also proposed, which is capable of providing oceanographic, meteorological and environmental monitoring data online, based on the developed multilayer system for planning the trajectories of group movement of objects. The results of full-scale sea trials and the developed algorithms are demonstrated.

In this work fuzzy logic based controller for Elevator Group Control System (EGCS) is proposed. The most complex problem is to control the multi-elevator in a multi-storey building and there are different approaches to solve the problem. The approach used based on the Simulink model and the fuzzy logic controller which is proposed to choose the most appropriate elevator for a hall call generated by passenger/user. The simulation was carried out by giving the input which was used to calculate the input for fuzzy logic controller. The data obtained from the simulation model and the input source provided to the fuzzy based controller priority of each elevator car can be calculated. The maximum priority elevator car is responded for its hall call operation. The input was based on hall call assignment method. This work can assist in analyzing the performance of the Elevators.

The conventional elevator arrives at the floor in response to the user request and where is to dispatch an elevator car. There is one big problem that when system dispatches the elevator to the button responding floor, but corridor passenger is no there. In this paper, we present a novel elevator group control algorithm based on binocular-cameras corridor passenger detection and tracking, aiming to overcome the difficulty in the conventional elevator group control system. In the above proposed system, the corridor passengers are detected using Haar-like features based on binocular-cameras, and Unscented Kalman Filter (UKF) is introduced to improve robustness and accuracy of corridor passenger motion tracking. A new elevator group control strategy based on corridor passenger detection and tracking is proposed to improve the performance and transport efficiency of the elevator service. Compared with the traditional elevator group control system, the proposed system has potential advantages in minimizing passengers’ waiting time and saving electronic energy. The final experimental results show the validity of our method under simulation and realistic condition.

Energy-saving elevator dispatching has been recognized as a challenging issue in building transportation, and we develop a novel energy-saving dispatching strategy for regenerative group-elevator system. Group-elevator dispatching is a typical combinatorial optimization problem, and three keys of the dispatching optimization are optimization method, objective, and model. The three keys of energy-saving-oriented elevator dispatching are studied in this paper. First, robust optimization method is introduced to handle dispatching optimization under uncertain elevator traffic flows; uncertain flows influence energy-saving dispatching seriously. Second, dispatching energy-objective function for regenerative group-elevator system is derived both schedule energy for four traffic patterns (up-peak, down-peak, up/down-mixed, and night) and return energy for two peak patterns (up-peak and down-peak) are considered. Third, four robust optimization-dispatching models for four traffic patterns are built, and optimization objectives of four models are minimizing the energy-objective function. Moreover, because we cannot solve robust optimization models with uncertain parameters directly, model counterpart transformation is studied. Finally, we solve the four transformed models by Linear Interactive and General Optimizer software and obtain robust optimization-dispatching solutions. In practice, four energy-saving-dispatching robust optimization models are switched according to real-time traffic patterns, and elevators are dispatched based on the dispatching solutions. We reduce elevator system-energy consumption effectively and keep average waiting time of the passengers acceptable under multi-traffic patterns. Simulation results demonstrate the validity of our strategy. Practical application: Group-elevator system spends much unnecessary energy because of the uncertainty of elevator passenger-traffic flows. This paper develops an energy-saving elevator-dispatching optimization strategy, which is immune to the uncertainty of four typical traffic flows. In practice, we update the group-elevator controller by our algorithm to realize energy-saving dispatching of regenerative group-elevator system under multi-traffic patterns.

Elevators are the essential transportation tools in high buildings so that Elevator Group Control System (EGCS) is developed to dynamically layout the schedule of elevators in a group. In this study, a fuzzy rules based intelligent elevator group control system has been proposed in which the structure of rules including the related parameters are generated optimally based on the traffic data so as to maximize service quality. In literature, the fuzzy related approaches have been applied in EGCS but the fuzzy rules were all pre-defined. However, how to create the most suitable fuzzy rule set in EGCS for dispatching elevators more efficiently and economically are never discussed in literature. The aim of the proposed approach is to minimize the average waiting time at peak hours as well as to minimize the power energy at off-peak hours by using the proposed fuzzy rule based ECGS. Moreover, there are many decision variables are considered in the GCGS to provide the most appropriate elevator assignment whenever any hall call is given. These variables include the number of elevators, traffic flow, direction, passenger preferences (for instance, department stores, hospitals, hotels, and office buildings), congestion and VIP priority floor, etc. In this study, a fuzzy rule based elevator-dispatching approach has been proposed for the EGCS in which the fuzzy rules and related parameters are derived optimally by using genetic algorithm based on the historical elevator transportation data. The experimental results show that the performance of the proposed approach is superior to these of traditional approaches in literatures.

The conventional elevator group control system is based on the button as a response and where to dispatch an elevator car. There is one big problem that when system dispatches the elevator to that button responding floor, but no passenger there and causing a waste of time and energy. In this paper, we present a novel intelligent elevator group control algorithm based on corridor passenger detection and tracking. In the above proposed system, after the corridor passengers are detected using binocular-cameras, Unscented Kalman Filter (UKF) is introduced to improve robustness and accuracy of corridor passenger motion tracking. At same time, a novel intelligent elevator group control strategy based on corridor passenger detection and tracking is proposed to improve the performance and transport efficiency of the elevator. Compared with the traditional elevator group control system, the proposed system has potential advantages in minimizing passengers’ waiting time and saving electronic energy. The final experimental results show the validity of our method under simulation condition.

This paper considers path following control for a robotic platform. The vehicle used for the experiments is a specially designed robotic platform for performing autonomous weed control. The platform is four-wheel steered and four-wheel driven. A diesel engine powers the wheels via a hydraulic transmission. The robot uses a Real Time Kinematic Differential Global Positioning System to determine both position and orientation relative to the path. The deviation of the robot to the desired path is supplied to two high level controllers minimizing the orthogonal distance and orientation to the path. Wheel angle setpoints are determined from inversion of the kinematic model. At low level each wheel angle is controlled by a proportional controller combined with a Smith predictor. Results show the controller performance following different paths shapes including a step, a ramp, and a typical headland path. A refined tuning method calculates controller settings that let the robot drive as much as possible along the same path to its setpoint, but also limit the gains at higher speeds to prevent the closed loop system to become unstable due to the time delay in the system. Mean, minimum and maximum orthogonal distance errors while following a straight path on a paving at a speed of 0.5 m/s are 0.0, −2.4 and 3.0 cm respectively and the standard deviation is 1.2 cm. The control method for four wheel steered vehicles presented in this paper has the unique feature that it enables control of a user definable position relative to the robot frame and can deal with limitations on the wheel angles. The method is very well practical applicable for a manufacturer: all parameters needed are known by the manufacturer or can be determined easily, user settings have an easy interpretation and the only complex part can be supplied as a generic software module.

What the group control of the elevator traffic system deal with is a complex, random, multi-objective, nonlinear, uncertain decision-making problem. Good effect of controlling elevators can’t be achieved with traditional control methods. Elevator group control system based on fuzzy single chip computer is introduced in this paper. Fuzzy inference can be carried out based on control rules that are programmed based experts’ experience. Then elevators are allocated in the light of inference result in order to achieve ideal control effect.