This paper proposes a novel hybrid algorithm called Fractional-order Particle Swarm optimization Gravitational Search Algorithm(FPSOGSA)and applies it to the trajectory planning of the hypersonic lifting reentry fligh...This paper proposes a novel hybrid algorithm called Fractional-order Particle Swarm optimization Gravitational Search Algorithm(FPSOGSA)and applies it to the trajectory planning of the hypersonic lifting reentry flight vehicles.The proposed method is used to calculate the control profiles to achieve the two objectives,namely a smoother trajectory and enforcement of the path constraints with terminal accuracy.The smoothness of the trajectory is achieved by scheduling the bank angle with the aid of a modified scheme known as a Quasi-Equilibrium Glide(QEG)scheme.The aerodynamic load factor and the dynamic pressure path constraints are enforced by further planning of the bank angle with the help of a constraint enforcement scheme.The maximum heating rate path constraint is enforced through the angle of attack parameterization.The Common Aero Vehicle(CAV)flight vehicle is used for the simulation purpose to test and compare the proposed method with that of the standard Particle Swarm Optimization(PSO)method and the standard Gravitational Search Algorithm(GSA).The simulation results confirm the efficiency of the proposed FPSOGSA method over the standard PSO and the GSA methods by showing its better convergence and computation efficiency.展开更多
Driven by the flourish of location-based services, trajectory search has received significant attentions in recent years. Different from existing studies that focus on searching trajectories with spatio-temporal infor...Driven by the flourish of location-based services, trajectory search has received significant attentions in recent years. Different from existing studies that focus on searching trajectories with spatio-temporal information and text de-scriptions, we study a novel problem of searching trajectories with spatial distance, activities, and rating scores. Given a query q with a threshold of distance, a set of activities, a start point S and a destination E, trip oriented search on activity trajectory （TOSAT） returns k trajectories that can cover the activities with the highest rating scores within the threshold of distance. In addition, we extend the query with an order, i.e., order-sensitive trip oriented search on activity trajectory （OTOSAT）, which takes both the order of activities in a query q and the order of trajectories into consideration. It is very challenging to answer TOSAT and OTOSAT e？ciently due to the structural complexity of trajectory data with rating infor-mation. In order to tackle the problem e？ciently, we develop a hybrid index AC-tree to organize trajectories. Moreover, the optimized variant RAC＋-tree and novel algorithms are introduced with the goal of achieving higher performance. Extensive experiments based on real trajectory datasets demonstrate that the proposed index structures and algorithms are capable of achieving high e？ciency and scalability.展开更多
In recent years, a few researches focus on the similarity measure of semantic trajectories in road networks, since semantic trajectories in road networks have smaller volumes, higher qualities and can better reflect u...In recent years, a few researches focus on the similarity measure of semantic trajectories in road networks, since semantic trajectories in road networks have smaller volumes, higher qualities and can better reflect user behaviors. However, these works do not further discuss how to efficiently search similar trajectories. Thus, to implement an efficient similarity search, we design an index called SIET based on the structures of road networks. Then, we propose a novel algorithm called SSN-BF to search similar trajectories efficiently by using best-first strategy. At last, we take the experimental evaluations on real dataset and prove the efficiency of our algorithm.展开更多
This paper proposes a new methodology to optimize trajectory of the path for multi-robots using improved gravitational search algorithm(IGSA) in clutter environment. Classical GSA has been improved in this paper based...This paper proposes a new methodology to optimize trajectory of the path for multi-robots using improved gravitational search algorithm(IGSA) in clutter environment. Classical GSA has been improved in this paper based on the communication and memory characteristics of particle swarm optimization(PSO). IGSA technique is incorporated into the multi-robot system in a dynamic framework, which will provide robust performance, self-deterministic cooperation, and coping with an inhospitable environment. The robots in the team make independent decisions, coordinate, and cooperate with each other to accomplish a common goal using the developed IGSA. A path planning scheme has been developed using IGSA to optimally obtain the succeeding positions of the robots from the existing position in the proposed environment. Finally, the analytical and experimental results of the multi-robot path planning were compared with those obtained by IGSA, GSA and differential evolution(DE) in a similar environment. The simulation and the Khepera environment result show outperforms of IGSA as compared to GSA and DE with respect to the average total trajectory path deviation, average uncovered trajectory target distance and energy optimization in terms of rotation.展开更多
Performing repeatable duties automatically was the dreams of human being for centuries. Although full autonomy has long been dreamed of by visionaries, many researches have been performed for surface vehicles automati...Performing repeatable duties automatically was the dreams of human being for centuries. Although full autonomy has long been dreamed of by visionaries, many researches have been performed for surface vehicles automation since the last century to get close to this dream stepwise. To increase daily working hours and accuracy and reduce cost, operations such as hydrography are susceptible for autonomy. Beside platform topology, installed sensors and energy resources, the core elements of any autonomous surface vehicle are navigation, guidance and control systems. To perform bathymetry operation in autonomy manner, a reliable and robust navigation algorithm is designed and embedded in an autonomous surface vehicle titled Morvarid. Morvarid is a plug-in hybrid solar powered catamaran boat. The developed algorithm is a combination of extended Kalman filter, search ball and potential field approaches. Many experimental field tests are carried out after simulation in Simulink environment. Test results illustrated the algorithm and improved the path followed by reducing SD and RMSE and there is a good correlation between simulation run and experimental tests.展开更多
文摘This paper proposes a novel hybrid algorithm called Fractional-order Particle Swarm optimization Gravitational Search Algorithm(FPSOGSA)and applies it to the trajectory planning of the hypersonic lifting reentry flight vehicles.The proposed method is used to calculate the control profiles to achieve the two objectives,namely a smoother trajectory and enforcement of the path constraints with terminal accuracy.The smoothness of the trajectory is achieved by scheduling the bank angle with the aid of a modified scheme known as a Quasi-Equilibrium Glide(QEG)scheme.The aerodynamic load factor and the dynamic pressure path constraints are enforced by further planning of the bank angle with the help of a constraint enforcement scheme.The maximum heating rate path constraint is enforced through the angle of attack parameterization.The Common Aero Vehicle(CAV)flight vehicle is used for the simulation purpose to test and compare the proposed method with that of the standard Particle Swarm Optimization(PSO)method and the standard Gravitational Search Algorithm(GSA).The simulation results confirm the efficiency of the proposed FPSOGSA method over the standard PSO and the GSA methods by showing its better convergence and computation efficiency.
基金This work was supported by the National Natural Science Foundation of China under Grant Nos. 61073061, 61303019, 61003044, 61232006, 61472263, 61402312, and 61402313, the Doctoral Fund of Ministry of Education of China under Grant No. 20133201120012, and Jiangsu Provincial Department of Education under Grant No. 12KJB520017.
文摘Driven by the flourish of location-based services, trajectory search has received significant attentions in recent years. Different from existing studies that focus on searching trajectories with spatio-temporal information and text de-scriptions, we study a novel problem of searching trajectories with spatial distance, activities, and rating scores. Given a query q with a threshold of distance, a set of activities, a start point S and a destination E, trip oriented search on activity trajectory （TOSAT） returns k trajectories that can cover the activities with the highest rating scores within the threshold of distance. In addition, we extend the query with an order, i.e., order-sensitive trip oriented search on activity trajectory （OTOSAT）, which takes both the order of activities in a query q and the order of trajectories into consideration. It is very challenging to answer TOSAT and OTOSAT e？ciently due to the structural complexity of trajectory data with rating infor-mation. In order to tackle the problem e？ciently, we develop a hybrid index AC-tree to organize trajectories. Moreover, the optimized variant RAC＋-tree and novel algorithms are introduced with the goal of achieving higher performance. Extensive experiments based on real trajectory datasets demonstrate that the proposed index structures and algorithms are capable of achieving high e？ciency and scalability.
基金Supported by the National Key Research and Development Program of the Ministry of Science and Technology of China(2016YFB1000700)
文摘In recent years, a few researches focus on the similarity measure of semantic trajectories in road networks, since semantic trajectories in road networks have smaller volumes, higher qualities and can better reflect user behaviors. However, these works do not further discuss how to efficiently search similar trajectories. Thus, to implement an efficient similarity search, we design an index called SIET based on the structures of road networks. Then, we propose a novel algorithm called SSN-BF to search similar trajectories efficiently by using best-first strategy. At last, we take the experimental evaluations on real dataset and prove the efficiency of our algorithm.
文摘This paper proposes a new methodology to optimize trajectory of the path for multi-robots using improved gravitational search algorithm(IGSA) in clutter environment. Classical GSA has been improved in this paper based on the communication and memory characteristics of particle swarm optimization(PSO). IGSA technique is incorporated into the multi-robot system in a dynamic framework, which will provide robust performance, self-deterministic cooperation, and coping with an inhospitable environment. The robots in the team make independent decisions, coordinate, and cooperate with each other to accomplish a common goal using the developed IGSA. A path planning scheme has been developed using IGSA to optimally obtain the succeeding positions of the robots from the existing position in the proposed environment. Finally, the analytical and experimental results of the multi-robot path planning were compared with those obtained by IGSA, GSA and differential evolution(DE) in a similar environment. The simulation and the Khepera environment result show outperforms of IGSA as compared to GSA and DE with respect to the average total trajectory path deviation, average uncovered trajectory target distance and energy optimization in terms of rotation.
基金financially supported by the Ports and Maritime Organization for funding the Morvarid Project(Grant No.20S/7509.2015)
文摘Performing repeatable duties automatically was the dreams of human being for centuries. Although full autonomy has long been dreamed of by visionaries, many researches have been performed for surface vehicles automation since the last century to get close to this dream stepwise. To increase daily working hours and accuracy and reduce cost, operations such as hydrography are susceptible for autonomy. Beside platform topology, installed sensors and energy resources, the core elements of any autonomous surface vehicle are navigation, guidance and control systems. To perform bathymetry operation in autonomy manner, a reliable and robust navigation algorithm is designed and embedded in an autonomous surface vehicle titled Morvarid. Morvarid is a plug-in hybrid solar powered catamaran boat. The developed algorithm is a combination of extended Kalman filter, search ball and potential field approaches. Many experimental field tests are carried out after simulation in Simulink environment. Test results illustrated the algorithm and improved the path followed by reducing SD and RMSE and there is a good correlation between simulation run and experimental tests.
