Hierarchical planning for a surface mounting machine placement

For a surface mounting machine (SMM) in printed circuit board (PCB) assembly line, there are four problems, e.g. CAD data conversion, nozzle selection, feeder assignment and placement sequence determination. A hierarchical planning for them to maximize the throughput rate of an SMM is presented here. To minimize set-up time, a CAD data conversion system was first applied that could automatically generate the data for machine placement from CAD design data files. Then an effective nozzle selection approach was implemented to minimize the time of nozzle changing. And then, to minimize picking time, an algorithm for feeder assignment was used to make picking multiple components simultaneously as much as possible. Finally, in order to shorten pick-and-place time, a heuristic algorithm was used to determine optimal component placement sequence according to the decided feeder positions. Experiments were conducted on a four head SMM. The experimental results were used to analyse the assembly line performance.

Hybrid hierarchical trajectory planning for a fixed-wing UCAV performing air-to-surface multi-target attack

This paper considers the problem of generating a flight trajectory for a single fixed-wing unmanned combat aerial vehicle （UCAV） performing an air-to-surface multi-target attack （A/SMTA） mission using satellite-guided bombs. First, this problem is formulated as a variant of the traveling salesman problem （TSP）, called the dynamic-constrained TSP with neighborhoods （DCT- SPN）. Then, a hierarchical hybrid approach, which partitions the planning algorithm into a roadmap planning layer and an optimal control layer, is proposed to solve the DCTSPN. In the roadmap planning layer, a novel algorithm based on an updatable proba- bilistic roadmap （PRM） is presented, which operates by randomly sampling a finite set of vehicle states from continuous state space in order to reduce the complicated trajectory planning problem to planning on a finite directed graph. In the optimal control layer, a collision-free state-to-state trajectory planner based on the Gauss pseudospectral method is developed, which can generate both dynamically feasible and optimal flight trajectories. The entire process of solving a DCTSPN consists of two phases. First, in the offline preprocessing phase, the algorithm constructs a PRM, and then converts the original problem into a standard asymmet- ric TSP （ATSP）. Second, in the online querying phase, the costs of directed edges in PRM are updated first, and a fast heuristic searching algorithm is then used to solve the ATSP. Numerical experiments indicate that the algorithm proposed in this paper can generate both feasible and near-optimal solutions quickly for online purposes.

Application and Realization of Hierarchical Network Planning Based on Web

Making use of Microsoft Visual Studio. NET platform, hierarchical network planning is realized in working procedure time-optimization of the construction by TBM, and hierarchical network graph of the construction by TBM is drawn based on browser. Then the theory of system realization is discussed, six components of system that can be reused are explained emphatically. The realization of hierarchical network panning in Internet provides available guarantee for controlling rate of progress in large-scale or middle-sized projects. Key words Web - network graph - hierarchical network planning CLC number TU721 Biography: WU Shi-jing (1963-), male, Professor, research direction: equipments management engineering, mechatronics, state monitoring and malfunction diagnostics of machine and electronics equipments.

Learning and fatigue inspired method for optimized HTN planning

Learning is widely used in intelligent planning to shorten the planning process or improve the plan quality. This paper aims at introducing learning and fatigue into the classical hierarchical task network （HTN） planning process so as to create better high- quality plans quickly. The process of HTN planning is mapped during a depth-first search process in a problem-solving agent, and the models of learning in HTN planning is conducted similar to the learning depth-first search （LDFS）. Based on the models, a learning method integrating HTN planning and LDFS is presented, and a fatigue mechanism is introduced to balance exploration and exploitation in learning. Finally, experiments in two classical do- mains are carried out in order to validate the effectiveness of the proposed learning and fatigue inspired method.

Intrusion response goals and corresponding strategies

The effects of strategy on the network security defense and the related research on intrusion response strategy are briefly presented, with the focus on the status and function of intrusion re- sponse strategy in the intrusion response decision-making. Some specific response strategies for specific response goals are presented as well. The relevant knowledge of the planning, and a classification of response tasks are proposed. The intrusion response planning methods and models based on hierarchical task network （HTN） are described in detail. On this basis, the model of combining the response measure decision-making with the response time decision-making is expounded. The proposed model can integrate response strategy into response decision-making mechanism. In addition, the results of the intrusion response experiments are provided to verify the ability of using different response strategies to achieve different response goals. At last, the application needs of response strategy in network security are analyzed, and the approaches of the response strategy applied in in- trusion response system are summarized.

Planning to Plan-Integrating Control Flow

In many planning situations, computation itself becomes a resource to be planned and scheduled. We model such computational resources as conventional resources which are used by control-flow actions, e.g., to direct the planning process. Control-flow actions and conventional actions are planned/scheduled in an integrated way and can interact with each other. Control-flow actions are then executed by the planning engine itself. The approach is illustrated by examples, e.g., for hierarchical planning, in which tasks that are temporally still far away impose only rough constraints on the current schedule, and control-flow tasks ensure that these tasks are refined as they approach the current time. Using the same mechanism, anytime algorithms can change appropriate search methods or parameters over time, and problems like scheduling critical time-outs for garbage collection can be made part of the planning itself.

Path Tracking Control for Autonomous Truck with Dual Modular Chassis

This study focuses on enhancing the agility and path tracking capabilities of autonomous trucks equipped with dual 4WIS-4WID modular chassis.To address the challenges associated with these versatile vehicles,a comprehensive approach is presented.Firstly,a communication framework is devised,utilizing a hierarchical combination of two fieldbus systems.This framework facilitates adaptive marshalling,allowing effective communication and coordination among the various modular components of the autonomous truck.Secondly,a reference path generation strategy is proposed.This strategy relates the motion paths of the truck's body to its modular chassis.Reference paths for the modular chassis are derived based on the center of mass,effectively resolving the issue of differing motion paths.To tackle the path tracking problem for dual modular chassis,a cooperative path tracking controller is developed.This controller is designed using the kinematic model of the autonomous truck,enabling adaptive control through online adjustments of controller parameters based on measured input–output data.Simulation and real vehicle testing validate the proposed path tracking controller.In the dual modular chassis path tracking simulation,the maximum lateral position error and the maximum yaw angle error of truck body at different speeds are 0.082 m and 0.007 rad,respectively.In the real vehicle test,the maximum lateral position error is 0.194 m,and the maximum yaw angle error is 0.071 rad.These results demonstrate the practicality and effectiveness of the controller in real-world applications.