Sharif Digital Repository

As a means to relieve traffic congestion, toll pricing has recently received significant attention by transportation planners. Inappropriate use of transportation networks is one of the major causes of network congestion. Toll pricing is a method of traffic management in which traffic flow is guided to proper time and path in order to reduce the total delay in the network. This article investigates a method for solving the minimum toll revenue problem in real and large-scale transportation networks. The objective of this problem is to find link tolls that simultaneously cause users to efficiently use the transportation network and to minimize the total toll revenues to be collected. Although... 

Using mobile robots in disaster areas can reduce risks and the search time in urban search and rescue operations. Optimal path-planning for mobile robotics can play a key role in the reduction of the search time for rescuing victims. In order to minimize the search time, the shortest path to the target should be determined. In this paper, a new integrated Reinforcement Learning—based method is proposed to search and find a hidden target in an unknown environment in the minimum time. The proposed algorithm is developed in two main phases. Depending on whether or not the mobile robot receives the signal from the hidden target, phases I or II of the proposed algorithm can be carried out. Then,... 

Using mobile robots in disaster areas can reduce risks and the search time in urban search and rescue operations. Optimal path-planning for mobile robotics can play a key role in the reduction of the search time for rescuing victims. In order to minimize the search time, the shortest path to the target should be determined. In this paper, a new integrated Reinforcement Learning—based method is proposed to search and find a hidden target in an unknown environment in the minimum time. The proposed algorithm is developed in two main phases. Depending on whether or not the mobile robot receives the signal from the hidden target, phases I or II of the proposed algorithm can be carried out. Then,... 

A new algorithm named random particle optimization algorithm (RPOA) for local path planning problem of mobile robots in dynamic and unknown environments is proposed. The new algorithm inspired from bacterial foraging technique is based on particles which are randomly distributed around a robot. These particles search the optimal path toward the target position while avoiding the moving obstacles by getting help from the robot's sensors. The criterion of optimal path selection relies on the particles distance to target and Gaussian cost function assign to detected obstacles. Then, a high level decision making strategy will decide to select best mobile robot path among the proceeded particles,... 

Although there are many works in which path planning of robots is studied, but path planning of the bevel-tip needles with highly flexible body is different and difficult due to unique properties of soft tissues. Real soft tissues are nonhomogeneously elastic and uncertainly deformable and hence, during needle motions the planned path changes unknowingly. In this paper, a novel adaptive path planning of bevel-tip needles inside the uncertain brain tissue is presented. The proposed approach is based on minimization of a Lyapanov energy function used as the cost function which consists of 6 partial costs: path length, number of changes in bevel direction, tissue deformation, horizontal and... 

In this paper, the path planning problem of special hyper-redundant manipulator with lockable joints is solved using particle swarm optimization. There is a locking mechanism in each link of this tendon-actuated manipulator. At any time, all links of the manipulator must be locked except one. Then by pulling the cables, the configuration of the corresponding link will change and the manipulator will tilt to its new position. Therefore, by unlocking the links in sequence and pulling the cables, any desirable configuration of manipulator can be reached. In path planning problem, the desired path of the end-effector is given and the optimum sequence of switching (discrete) and the optimum... 

In this paper, we consider the restricted version of the well-known 2D line simplification problem under area measures and for restricted version. We first propose a unified definition for both of sum-area and difference-area measures that can be used on a general path of n vertices. The optimal simplification runs in O(n 3) under both of these measures. Under sum-area measure and for a realistic input path, we propose an approximation algorithm of O n2 time complexity to find a simplification of the input path, where is the absolute error of this algorithm compared to the optimal solution. Furthermore, for difference-area measure, we present an algorithm that finds the optimal... 

Routing methods and algorithms in "Wireless Sensor Networks" attract researchers' attention a lot recently. In order to find the optimal path in WSNs depending on their type and application, various algorithms are proposed. In this paper, an optimal path finding algorithm based on "Game Theory" is discussed and improved. This algorithm finds a path witch is optimizing network lifetime, load distribution, and path reliability simultaneously  

This paper presents evolutionary methods for optimization in dynamic mobile robot path planning. In dynamic mobile path planning, the goal is to find an optimal feasible path from starting point to target point with various obstacles, as well as smoothness and safety in the proposed path. Pattern search (PS) algorithm, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used to find an optimal path for mobile robots to reach to target point with obstacle avoidance. For showing the success of the proposed method, first they are applied to two different paths with a dynamic environment in obstacles. The first results show that the PSO algorithms are converged and minimizethe... 

In this paper, a "Game Theory" approach for finding an optimal path in a "Wireless Sensor Network" is discussed. WSN is one of the most interesting research fields in the communication networks, and is the center of attention in recent years. In our model, a pricing and payment technique is presented to obtain an optimal path in a WSN by considering reliability, energy and traffic load. The proposed algorithm is able to find a path which improves network lifetime, load distribution and path reliability  

In this paper, a new method for path planning is proposed using a genetic algorithm (GA). Our method has two key advantages over existing GA methods. The first is a novel environment representation which allows a more efficient method for obstacles dilation in comparison to current cell based approaches that have a tradeoff between speed and accuracy. The second is the strategy we use to generate the initial population in order to speed up the convergence rate which is completely novel. Simulation results show that our method can find a near optimal path faster than computational geometry approaches and with more accuracy in smaller number of generations than GA methods. © 2009 IEEE  

The failure of mechanical components is a common phenomenon in satellites. This failure can happen in the satellite attitude control system, which causes that the control system of the satellite becomes underactuated. There have been many attempts to control the orientation of underactuated satellites. However, in most studies, the inertia matrix of the satellite is assumed to be diagonal with respect to the body coordinate system, and no limitations on the amount of torque applied by the reaction wheels have been considered. In this paper, at first, it is attempted to control the satellite using the motion planning method. The satellite control inputs are assumed to be cubic spline with... 

We propose a cost estimation model based on a fuzzy rule backpropagation network, configuring the rules to estimate the cost under uncertainty. A multiple linear regression analysis is applied to analyze the rules and identify the effective rules for cost estimation. Then, using a dynamic programming approach, we determine the optimal path of the manufacturing network. Finally, an application of this model is illustrated through a numerical example showing the effectiveness of the proposed model for solving the cost estimation problem under uncertainty  

We propose an approach for finding an optimal path in a flexible jobshop manufacturing system considering two criteria of time and cost. With rise in demands, advancement in technology and increase in production capacity, the need for more shops persists. Therefore, a flexible jobshop system has more than one shop with the same duty. The difference among shops with the same duty is in their machines with various specifications. A network is configured in which the nodes are considered to be the shops with arcs representing the paths among the shops. An automated guided vehicle functions as a material handling device through the manufacturing network. To account for uncertainty, we consider... 

We propose an approach for finding an optimal path in a flexible jobshop manufacturing system considering two criteria of time and cost. With rise in demands, advancement in technology, and increase in production capacity, the need for more shops persists. Therefore, a flexible jobshop system has more than one shop with the same duty. The difference among shops with the same duty is in their machines with various specifications. A network is configured in which the nodes are considered to be the shops with arcs representing the paths among the shops. An Automated Guided Vehicle (AGV) functions as a material handling device through the manufacturing network. To account for uncertainty, we... 

Engineers have applied mathematical models to find the optimal railway path in order to minimise the total cost subject to the railway limitations. There are two main issues for this problem. First, this approach results in a complex formulation for real-life applications, mainly because there are a huge number of variables and constraints. Second, to compute the total cost, different types of data are required, such as topography, right-of-way unit cost, forbidden zones and geology. Because various administrations are often responsible for preparing these data with their own standards, there is much inconsistency in the data. This paper deals with the first issue by proposing a... 

We propose a cost estimation model based on a fuzzy rule back-propagation network (BPN), configuring the rules to estimate the cost under uncertainty. A multiple linear regression analysis is applied to analyze the rules and identify the effective rules for cost estimation. Then, using a dynamic programming approach we determine the optimal path in the manufacturing network. Finally, an application of this model is illustrated through a numerical example showing the effectiveness of the proposed model for solving the cost estimation problem under uncertainty  

Purpose - The purpose of this paper is to devise a new approach to synthesize closed-loop feedback guidance law for online thrust- insensitive optimal trajectory generation utilizing neural networks. Design/methodology/approach - The proposed methodology utilizes an open- loop variational formulation that initially determines optimal launch/ ascent trajectories for various scenarios of known uncertainties in the thrust profile of typical solid propellant engines. These open-loop optimized trajectories will then provide the knowledge base needed for the subsequent training of a neural network. The trained network could eventually produce thrust-insensitive closed-loop optimal guidance laws...