Path planning is a fundamental component in robotics and game artificial intelligence that considerably influences the motion efficiency of robots and unmanned aerial vehicles,as well as the realism and immersion of v...Path planning is a fundamental component in robotics and game artificial intelligence that considerably influences the motion efficiency of robots and unmanned aerial vehicles,as well as the realism and immersion of virtual environments.However,traditional algorithms are often limited to single-objective optimization and lack real-time adaptability to dynamic environments.This study addresses these limitations through a proposed realtime dynamic multiobjective(RDMO)path-planning algorithm based on an enhanced A^(*) framework.The proposed algorithm employs a queue-based structure and composite multiheuristic functions to dynamically manage game tasks and compute optimal paths under changing-map-connectivity conditions in real time.Simulation experiments are conducted using real-world road network data and benchmarked against mainstream hybrid approaches based on genetic algorithms(GAs)and simulated annealing(SA).The results show that the computational speed of the RDMO algorithm is 88 and 73 times faster than that of the GA-and SA-based solutions,respectively,while the total planned path length is reduced by 58%and 33%,respectively.In addition,the RDMO algorithm also shows excellent responsiveness to dynamic changes in map connectivity and can achieve real-time replanning with a minimal computational overhead.The research results prove that the RDMO algorithm provides a robust and efficient solution for multiobjective path planning in games and robotics applications and has a great application potential in improving system performance and user experience in related fields in the future.展开更多
Demand Responsive Transit (DRT) responds to the dynamic users’ requests without any fixed routes and timetablesand determines the stop and the start according to the demands. This study explores the optimization of d...Demand Responsive Transit (DRT) responds to the dynamic users’ requests without any fixed routes and timetablesand determines the stop and the start according to the demands. This study explores the optimization of dynamicvehicle scheduling and real-time route planning in urban public transportation systems, with a focus on busservices. It addresses the limitations of current shared mobility routing algorithms, which are primarily designedfor simpler, single origin/destination scenarios, and do not meet the complex demands of bus transit systems. Theresearch introduces an route planning algorithm designed to dynamically accommodate passenger travel needsand enable real-time route modifications. Unlike traditional methods, this algorithm leverages a queue-based,multi-objective heuristic A∗ approach, offering a solution to the inflexibility and limited coverage of suburbanbus routes. Also, this study conducts a comparative analysis of the proposed algorithm with solutions based onGenetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO), focusing on calculation time, routelength, passenger waiting time, boarding time, and detour rate. The findings demonstrate that the proposedalgorithmsignificantly enhances route planning speed, achieving an 80–100-fold increase in efficiency over existingmodels, thereby supporting the real-time demands of Demand-Responsive Transportation (DRT) systems. Thestudy concludes that this algorithm not only optimizes route planning in bus transit but also presents a scalablesolution for improving urban mobility.展开更多
In this paper, the feasibility and objectives coordination of real-time optimization (RTO) are systemically investigated under soft constraints. The reason for requiring soft constraints adjustment and objective relax...In this paper, the feasibility and objectives coordination of real-time optimization (RTO) are systemically investigated under soft constraints. The reason for requiring soft constraints adjustment and objective relaxation simultaneously is that the result is not satisfactory when the feasible region is apart from the desired working point or the optimization problem is infeasible. The mixed logic method is introduced to describe the priority of the constraints and objectives, thereby the soft constraints adjustment and objectives coordination are solved together in RTO. A case study on the Shell heavy oil fractionators benchmark problem illustrating the method is finally presented.展开更多
This paper proposes a dynamic-decision-based realtime dispatch method to coordinate the economic objective with multiple types of security dispatch objectives while reducing constraint violations in the process of adj...This paper proposes a dynamic-decision-based realtime dispatch method to coordinate the economic objective with multiple types of security dispatch objectives while reducing constraint violations in the process of adjusting the system operation point to the optimum.In each decision moment,the following tasks are executed in turn:①locally linearizing the system model at the current operation point with the online model identification by using measurements;②narrowing down the gaps between unsatisfied security requirements and their security thresholds in order of priority;③minimizing the generation cost;④minimizing the security indicators within their security thresholds.Compared with the existing real-time dispatch strategies,the proposed method can adjust the deviations caused by unpredictable power flow fluctuations,avoid dispatch bias caused by model parameter errors,and reduce the constraint violations in the dispatch decision process.The effectiveness of the proposed method is verified with the IEEE 39-bus system.展开更多
The design of an annular combustion chamber in a gas turbine engine is thebackbone of this paper.It is specifically designed for a low bypass turbofan engine in a jettrainer aircraft.The combustion chamber is position...The design of an annular combustion chamber in a gas turbine engine is thebackbone of this paper.It is specifically designed for a low bypass turbofan engine in a jettrainer aircraft.The combustion chamber is positioned in between the compressor and turbine.lt has to be designed based on the constant pressure,enthalpy addition process.The presentmethodology deals with the computation of the initial design parameters from benchmarking ofreal-time industry standards and arriving at optimized values.It is then studied for feasibilityand finalized.Then the various dimensions of the combustor are calculated based on differentempirical formulas.The air mass flow is then distributed across the zones of the combustor.The cooling requirement is met using the cooling holes.Finally the variations of parameters atdifferent points are calculated.The whole combustion chamber is modeled using Siemens NX8.0,a modeling software and presented.The model is then analyzed using various parametersat various stages and levels to determine the optimized design.The aerodynamic flowcharacteristics is simulated numerically by means of ANSYS 14.5 software suite.The air-fuelmixture,combustion-turbulence,thermal and cooling analysis is carried out.The analysis isperformed at various scenarios and compared.The results are then presented in image outputsand graphs.展开更多
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(NRF-2023R1A2C1005950).
文摘Path planning is a fundamental component in robotics and game artificial intelligence that considerably influences the motion efficiency of robots and unmanned aerial vehicles,as well as the realism and immersion of virtual environments.However,traditional algorithms are often limited to single-objective optimization and lack real-time adaptability to dynamic environments.This study addresses these limitations through a proposed realtime dynamic multiobjective(RDMO)path-planning algorithm based on an enhanced A^(*) framework.The proposed algorithm employs a queue-based structure and composite multiheuristic functions to dynamically manage game tasks and compute optimal paths under changing-map-connectivity conditions in real time.Simulation experiments are conducted using real-world road network data and benchmarked against mainstream hybrid approaches based on genetic algorithms(GAs)and simulated annealing(SA).The results show that the computational speed of the RDMO algorithm is 88 and 73 times faster than that of the GA-and SA-based solutions,respectively,while the total planned path length is reduced by 58%and 33%,respectively.In addition,the RDMO algorithm also shows excellent responsiveness to dynamic changes in map connectivity and can achieve real-time replanning with a minimal computational overhead.The research results prove that the RDMO algorithm provides a robust and efficient solution for multiobjective path planning in games and robotics applications and has a great application potential in improving system performance and user experience in related fields in the future.
文摘Demand Responsive Transit (DRT) responds to the dynamic users’ requests without any fixed routes and timetablesand determines the stop and the start according to the demands. This study explores the optimization of dynamicvehicle scheduling and real-time route planning in urban public transportation systems, with a focus on busservices. It addresses the limitations of current shared mobility routing algorithms, which are primarily designedfor simpler, single origin/destination scenarios, and do not meet the complex demands of bus transit systems. Theresearch introduces an route planning algorithm designed to dynamically accommodate passenger travel needsand enable real-time route modifications. Unlike traditional methods, this algorithm leverages a queue-based,multi-objective heuristic A∗ approach, offering a solution to the inflexibility and limited coverage of suburbanbus routes. Also, this study conducts a comparative analysis of the proposed algorithm with solutions based onGenetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO), focusing on calculation time, routelength, passenger waiting time, boarding time, and detour rate. The findings demonstrate that the proposedalgorithmsignificantly enhances route planning speed, achieving an 80–100-fold increase in efficiency over existingmodels, thereby supporting the real-time demands of Demand-Responsive Transportation (DRT) systems. Thestudy concludes that this algorithm not only optimizes route planning in bus transit but also presents a scalablesolution for improving urban mobility.
基金Supported by the National Natural Science Foundation of China (No. 60474051) the Key Technology and Development Program of Shanghai Science and Technology Department (No. 04DZ11008) partly by the Specialized Research Fund for the Doctoral Program of Higher Education of China (No. 20020248028).
文摘In this paper, the feasibility and objectives coordination of real-time optimization (RTO) are systemically investigated under soft constraints. The reason for requiring soft constraints adjustment and objective relaxation simultaneously is that the result is not satisfactory when the feasible region is apart from the desired working point or the optimization problem is infeasible. The mixed logic method is introduced to describe the priority of the constraints and objectives, thereby the soft constraints adjustment and objectives coordination are solved together in RTO. A case study on the Shell heavy oil fractionators benchmark problem illustrating the method is finally presented.
基金This work was supported by the National Natural Science Foundation of China(No.51761145106)the Guangdong Provincial Natural Science Foundation of China(No.2018B030306041)+1 种基金the Fundamental Research Funds for the Central Universities(No.2019SJ01)the China Scholarship Council(No.201806155019).
文摘This paper proposes a dynamic-decision-based realtime dispatch method to coordinate the economic objective with multiple types of security dispatch objectives while reducing constraint violations in the process of adjusting the system operation point to the optimum.In each decision moment,the following tasks are executed in turn:①locally linearizing the system model at the current operation point with the online model identification by using measurements;②narrowing down the gaps between unsatisfied security requirements and their security thresholds in order of priority;③minimizing the generation cost;④minimizing the security indicators within their security thresholds.Compared with the existing real-time dispatch strategies,the proposed method can adjust the deviations caused by unpredictable power flow fluctuations,avoid dispatch bias caused by model parameter errors,and reduce the constraint violations in the dispatch decision process.The effectiveness of the proposed method is verified with the IEEE 39-bus system.
文摘The design of an annular combustion chamber in a gas turbine engine is thebackbone of this paper.It is specifically designed for a low bypass turbofan engine in a jettrainer aircraft.The combustion chamber is positioned in between the compressor and turbine.lt has to be designed based on the constant pressure,enthalpy addition process.The presentmethodology deals with the computation of the initial design parameters from benchmarking ofreal-time industry standards and arriving at optimized values.It is then studied for feasibilityand finalized.Then the various dimensions of the combustor are calculated based on differentempirical formulas.The air mass flow is then distributed across the zones of the combustor.The cooling requirement is met using the cooling holes.Finally the variations of parameters atdifferent points are calculated.The whole combustion chamber is modeled using Siemens NX8.0,a modeling software and presented.The model is then analyzed using various parametersat various stages and levels to determine the optimized design.The aerodynamic flowcharacteristics is simulated numerically by means of ANSYS 14.5 software suite.The air-fuelmixture,combustion-turbulence,thermal and cooling analysis is carried out.The analysis isperformed at various scenarios and compared.The results are then presented in image outputsand graphs.
