Edwin Hou

A genetic algorithm for multiprocessor scheduling

The problem of multiprocessor scheduling can be stated as finding a schedule for a general task graph to be executed on a multiprocessor system so that the schedule length can be minimized. This scheduling problem is known to be NP-hard, and methods based on heuristic search have been proposed to obtain optimal and suboptimal solutions. Genetic algorithms have recently received much attention as a class of robust stochastic search algorithms for various optimization problems. In this paper, an efficient method based on genetic algorithms is developed to solve the multiprocessor scheduling problem. The representation of the search node is based on the order of the tasks being executed in each individual processor. The genetic operator proposed is based on the precedence relations between the tasks in the task graph. Simulation results comparing the proposed genetic algorithm, the list scheduling algorithm, and the optimal schedule using random task graphs, and a robot inverse dynamics computational task graph are presented. >

Efficient multiprocessor scheduling based on genetic algorithms

An efficient method, based on genetic algorithms, for solving the multiprocessor scheduling problem is proposed. The representation of the search node is based on the schedule of the tasks in each individual processor. The genetic operator is based on the precedence relations between the tasks in the task graph. The genetic algorithm is applied to the problem of scheduling robot inverse dynamics computations.<<ETX>>

Mobile manipulator path planning by a genetic algorithm

This article addresses the path-planning problem for a mobile manipulator system that is used to perform a sequence of tasks specified by locations and minimum oriented force capabilities. The problem is to find an optimal sequence of base positions and manipulator configurations for performing a sequence of tasks given a series of task specifications. The formulation of the problem is nonlinear. The feasible regions for the problem are nonconvex and unconnected. Genetic algorithms applied to such problems appear to be very promising while traditional optimization methods cause difficulties. Computer simulations are carried out on a three-degrees-of-freedom manipulator mounted on a two-degrees-of-freedom mobile base to search for the near optimal path-planning solution for performing the sequence of tasks.

A new method to optimize the satellite broadcasting schedules using the mean field annealing of a Hopfield neural network

Reports a new method for optimizing satellite broadcasting schedules based on the Hopfield neural model in combination with the mean field annealing theory. A clamping technique is used with an associative matrix, thus reducing the dimensions of the solution space. A formula for estimating the critical temperature for the mean field annealing procedure is derived, hence enabling the updating of the mean field theory equations to be more economical. Several factors on the numerical implementation of the mean field equations using a straightforward iteration method that may cause divergence are discussed; methods to avoid this kind of divergence are also proposed. Excellent results are consistently found for problems of various sizes.

Mobile robot path planning based on hierarchical hexagonal decomposition and artificial potential fields

In this article, a new algorithm based on an artificial potential field and hierarchical cell decomposition technique is developed to solve the find-path problem for a mobile robot. The complete map of the workspace including obstacle locations is assumed to be known a priori. The basic cell structure used for decomposition is a hexagon. The artificial potential field is based on an attractive force from the goal position and repelling forces from the obstacles. Computer simulations of the algorithm for various obstacle scenarios are also presented.

Networking for critical conditions

To enhance the preparedness of federal and state agencies to effectively manage federal or state recovery efforts in response to a broad spectrum of emergencies, we propose a hybrid adaptive network that will adopt currently available off-the-shelf wireless network devices and integrate them quickly into a scalable, reliable, and secure network with a minimum of human intervention for configuration and management. This model will serve as the framework for various rescue missions for securing and distributing critical resources. We investigate different technologies and network strategies and integrate them into the proposed network model to provide seamless support to heterogeneous environments including wireline nodes, ad hoc and sensor network nodes, and network devices based on different standards. In this article we present the network architecture and identify the key technical aspects of its management, security, QoS, and implementation.

Optimal Input Shaper Design For High-Speed Robotic Workcells

This paper deals with the feedforward control of a high-speed robotic workcell used by the NIST-ATP Precision Optoelectronics Assembly Consortium as a coarse stage to achieve micrometer-level placement accuracy. To maximize the speed of response under different load conditions, robust feedforward algorithms are considered. An optimal shaper is synthesized to trade off performance and robustness according to assembly specifications of the workcell. The optimal shaper along with standard shaper designs such as zero vibration, zero vibration and derivative, and extra insensitive are applied to conduct cycle time testing on the robotic workcell. The performance of each shaper is evaluated with respect to residual vibration, robustness, and speed. Specifically, the workcell performance for various unknown loading conditions is observed. It is shown that the optimal shaper produces the best overall results.

Task scheduling for flexible manufacturing systems based on genetic algorithms

The authors present a genetic algorithm approach to solving the task scheduling problem in flexible manufacturing systems (FMSs) An FMS is modeled as a collection of m workstations and p automated guided vehicles (AGVs). The FMS completes a task by performing a series of operations through the workstations, and the parts are transported between the workstations by the AGVs. The problem of task scheduling in an FMS can be stated as finding a schedule for the p AGVs among the m workstations such that n tasks can be completed in the shortest time. The genetic algorithm developed uses a reproduction operator and five mutation operators to perform the task scheduling. Computer simulations of the proposed genetic algorithm are also presented.<<ETX>>

Mobile Manipulator Path Planning By A Genetic algorithm

A bstract-The paper addresses the path planning problem for a mobile manipulator system which is used to perform a sequence of tasks specified by locations and minimum oriented force capabilities. The problem is to find an optimal sequence of base posi t ions a n d m a n i p u l a t o r conf igura t ions f o r performing a sequence of tasks given a series of task specifications. The formulation of the problem is nonlinear. The feasible regions for the problem a re nonconvex and unconnected. Genetic algorithms applied to such problems a p p e a r t o be very promising while t radi t ional optimization methods cause difficulties. Computer simulations a re carried out on a three-degrees-of-freedom manipula tor mounted on a two-degrees-of-freedom mobile base to search for the near optimal path planning solution for performing the sequence of tasks.

Cuckoo Search and Particle Filter-Based Inversing Approach to Estimating Defects via Magnetic Flux Leakage Signals

Accurate and timely prediction of defect dimensions from magnetic flux leakage signals requires one to solve an inverse problem efficiently. This paper proposes a new inversing approach to such a problem. It combines cuckoo search (CS) and particle filter (PF) to estimate the defect profile from measured signals and adopts a radial-basis function neural network as a forward model as well as the observation equation in PF. As one of the latest nature-inspired heuristic optimization algorithms, CS can solve high-dimensional optimization problems. As an effective estimator for a nonlinear filtering problem, PF is applied to the proposed inversing approach in order to improve the latters robustness to the noise. The resulting algorithm enjoys the advantages of both CS and PF where CS produces the optimized state sequence for PF while PF processes the state sequence and estimates the desired profile. The simulation and experimental results have demonstrated that the proposed approach is significantly better than the inversing approach based on CS alone in a noisy environment.