Nguyen Hoang Viet

Obstacle Avoidance Path Planning for Mobile Robot Based on Multi Colony Ant Algorithm

The task of planning trajectories for a mobile robot has received considerable attention in the research literature. The problem involves computing a collision-free path between a start point and a target point in environment of known obstacles. In this paper, we study an obstacle avoidance path planning problem using multi ant colony system, in which several colonies of ants cooperate in finding good solution by exchanging good information. In the simulation, we experimentally investigate the behaviour of multi colony ant algorithm with different kinds of information among the colonies. At last we will compare the behaviour of different number of colonies with a multi start single colony ant algorithm to show the good improvement.

Obstacle Avoidance Path Planning for Mobile Robot Based on Ant-Q Reinforcement Learning Algorithm

Path planning is an important task in mobile robot control. When the robot must move rapidly from any arbitrary start positions to any target positions in environment, a proper path must avoid both static obstacles and moving obstacles of arbitrary shape. In this paper, an obstacle avoidance path planning approach for mobile robots is proposed by using Ant-Q algorithm. Ant-Q is an algorithm in the family of ant colony based methods that are distributed algorithms for combinatorial optimization problems based on the metaphor of ant colonies. In the simulation, we experimentally investigate the sensitivity of the Ant-Q algorithm to its three methods of delayed reinforcement updating and we compare it with the results obtained by other heuristic approaches based on genetic algorithm or traditional ant colony system. At last, we will show very good results obtained by applying Ant-Q to bigger problem: Ant-Q find very good path at higher convergence rate.

Probabilistic Ranking Support Vector Machine

Recently, Support Vector Machines (SVMs) have been applied very effectively in learning ranking functions (or preference functions).They intend to learn ranking functions with the principles of the large margin and the kernel trick . However, the output of a ranking function is a score function which is not a calibrated posterior probability to enable post-processing. One approach to deal with this problem is to apply a generalized linear model with a link function and solve it by calculating the maximum likelihood estimate. But, if the link function is nonlinear, maximizing the likelihood will face with difficulties. Instead, we propose a new approach which train an SVM for a ranking function, then map the SVM outputs into a probabilistic sigmoid function whose parameters are trained by using cross-validation. This method will be tested on three data-mining datasets and compared to the results obtained by standard SVMs.

Ant Colony based Algorithm for Stable Marriage Problem

This paper introduces ant colony system (ACS), a distributed algorithm that is applied to the Stable Marriage Problem (SM). The stable marriage problem is an extensively-studied combinatorial problem with many practical applications. It is well known that at least one stable matching exists for every stable marriage instance. However, the classical Gale-Shapley [2] algorithm produces a marriage that greatly favors the men at the expense of the women, or vice versa. In our proposed ACS, a set of cooperating agents called ants cooperate to find stable matchings such as stable matching with man-optimal, woman-optimal, egalitarian stable matching, sex-fair stable matching. So this ACS is a novel method to solve Stable Marriage Problem. Our simulation results show the effectiveness of the proposed ACS.

Efficient Distributed Sensor Dispatch in Mobile Sensor Network

In this paper, a hybrid sensor network consisting of static and mobile sensors is considered, where static sensors are used to detect events, and mobiles sensors can move to event locations to conduct more advanced analysis. This dispatching problem can be converted to maximum weighted maximum matching problem with the objective of minimizing the total energy consumption to move sensor or maximizing the average remaining energy of sensor after movement. We present a distributed algorithm for sensor dispatch using message passing rule. Simulation results show that the algorithm can solve sensor dispatch problem in optimal distributed way.

VRIFA: a nonlinear SVM visualization tool using nomogram and localized radial basis function (LRBF) kernels

Prediction problems are prevalent in medical domains. For example, computer-aided diagnosis or prognosis is a key component in a CDSS (Clinical Decision Support System). SVMs, especially SVMs with nonlinear kernels such RBF kernels, have shown superior accuracy in prediction problems. However, they are not favorably used by physicians for medical prediction problems because nonlinear SVMs are difficult to visualize, thus it is hard to provide intuitive interpretation of prediction results to physicians. Nomogram was proposed to visualize SVM classification models. However, it cannot visualize nonlinear SVM models. Localized RBF (LRBF) kernel was proposed which shows comparable accuracy as the RBF kernel while the LRBF kernel is easier to interpret since it can be linearly decomposed. This paper presents a new tool named VRIFA, which integrates the nomogram and LRBF kernel to provide users with an interactive visualization of nonlinear SVM models. VRIFA graphically exposes the internal structure of nonlinear SVM models showing the effect of each feature, the magnitude of the effect, and the change at the prediction output. VRIFA also performs nomogram-based feature selection while training a model in order to remove noise or redundant features and improve the prediction accuracy. The tool has been used by biomedical researchers for computer-aided diagnosis and risk factor analysis for diseases. VRIFA is accessible at http://dm.postech.ac.kr/vrifa .

Policy Gradient SMDP for Resource Allocation and Routing in Integrated Services Networks

This paper presents a policy gradient semi-Markov decision process (SMDP) algorithm for call admission control and routing functions in an integrated network. These systems must handle several classes of calls of different value and with different resource requirements. The problem of maximizing the average reward (or cost) of admitted calls per unit time is naturally formulated as a SMDP problem, but is too complex to allow for an exact solution. Thus, a policy gradient algorithm is proposed to find the optimal call admission control and routing policy among a parameterized randomized policy space. To implement that gradient algorithm, we approximate the gradient of the average reward. Then, we present a simulation-based algorithm to estimate the approximate average gradient of the average reward (GSMDP), using only single sample path of the underlying Markov chain for the SMDP of call admission control and routing problem. The experimental simulations will compare its performance with other methods show the robustness of our algorithm.

Q-Learning based Univector Field Navigation Method for Mobile Robots

In this paper, the Q-Learning based univector field method is proposed for mobile robot to accomplish the obstacle avoidance and the robot orientation at the target position. Univector field method guarantees the desired posture of the robot at the target position. But it does not navigate the robot to avoid obstacles. To solve this problem, modified univector field is used and trained by Q-learning. When the robot following the field to get the desired posture collides with obstacles, univector fields at collision positions are modified according to the reinforcement of Q-learning algorithm. With this proposed navigation method, robot navigation task in a dynamically changing environment becomes easier by using double action Q-learning [8] to train univector field instead of ordinary Q-learning. Computer simulations and experimental results are carried out for an obstacle avoidance mobile robot to demonstrate the effectiveness of the proposed scheme.