scholarly journals A Novel Behavior-based Navigation Architecture of Mobile Robot in Unknown Environments

2016 ◽  
Vol 32 (3) ◽  
Author(s):  
Đào Thị Làn ◽  
Nguyen Thi Thanh Van ◽  
Phung Manh Duong ◽  
Dang Anh Viet ◽  
Tran Quang Vinh
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robot ◽  
Optimal Solution ◽  
Pareto Optimal Solution ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Complex Environments ◽  
Navigation Problem ◽  
Multi Objective ◽  
Behavior Based

Abstract: This study proposes behavior-based navigation architecture, named BBFM, for mobile robot in unknown environment with obstacles. The architecture is carried out in three steps: (i) analyzing the navigation problem to determine parameters of the architecture; (ii) designing the objective functions to relate input data with the desired output; and (iii) fusing the output of each objective function to generate the optimal control signal. We use fuzzy logic to design the objective functions and multi-objective optimization to find the Pareto optimal solution for the fusion. A number of simulations, comparisons, and experiments were conducted. The results show that the proposed architecture outperforms some popular behavior- based architectures in navigating the mobile robot in complex environments. Keywords: Behavior-based navigation, fuzzy logic, multi-objective optimization, mobile robot.

Multi-Objective Optimization Algorithm Design and Implementation of Mobile Base Station Site Selection

2010 ◽  
Vol 29-32 ◽  
pp. 2496-2502
Author(s):  
Min Wang ◽  
Jun Tang
Keyword(s):  
Site Selection ◽  
Engineering Application ◽  
Optimal Solution ◽  
Base Station ◽  
Pareto Optimal Solution ◽  
Antibody Concentration ◽  
Pareto Optimal ◽  
Memory Cells ◽  
Multi Objective Optimization ◽  
Multi Objective

The number of base station location impact the network quality of service. A new method is proposed based on immune genetic algorithm for site selection. The mathematical model of multi-objective optimization problem for base station selection and the realization of the process were given. The use of antibody concentration selection ensures the diversity of the antibody and avoiding the premature convergence, and the use of memory cells to store Pareto optimal solution of each generation. A exclusion algorithm of neighboring memory cells on the updating and deleting to ensure that the Pareto optimal solution set of the distribution. The experiments results show that the algorithm can effectively find a number of possible base station and provide a solution for the practical engineering application.

scholarly journals Multi-Task Scheduling Based on Classification in Mobile Edge Computing

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 938 ◽  
Author(s):  
Xiao Zheng ◽  
Yuanfang Chen ◽  
Muhammad Alam ◽  
Jun Guo
Keyword(s):  
Task Scheduling ◽  
Upper Bound ◽  
Optimization Algorithms ◽  
Optimal Solution ◽  
Pareto Optimal Solution ◽  
Edge Computing ◽  
Pareto Optimal ◽  
Mobile Edge Computing ◽  
Multi Objective Optimization ◽  
Multi Objective

In this paper, a dynamic multi-task scheduling prototype is proposed to improve the limited resource utilization in the vehicular networks (VNET) assisted by mobile edge computing (MEC). To ensure quality of service (QoS) and meet the growing data demands, multi-task scheduling strategies should be specially constructed by considering vehicle mobility and hardware service constraints. We investigate the rational scheduling of multiple computing tasks to minimize the VNET loss. To avoid conflicts between tasks when the vehicle moves, we regard multi-task scheduling (MTS) as a multi-objective optimization (MOO) problem, and the whole goal is to find the Pareto optimal solution. Therefore, we develop some gradient-based multi-objective optimization algorithms. Those optimization algorithms are unable to deal with large-scale task scheduling because they become unscalable as the task number and gradient dimensions increase. We therefore further investigate an upper bound of the loss of multi-objective and prove that it can be optimized in an effective way. Moreover, we also reach the conclusion that, with practical assumptions, we can produce a Pareto optimal solution by upper bound optimization. Compared with the existing methods, the experimental results show that the accuracy is significantly improved.

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