Global planning at Harris Semiconductor

2002 ◽  
Author(s):  
S.V. Murty ◽  
J.W. Bienvenu
Keyword(s):  
Global Planning

An E2GPGP-GASA-Based Multi-Agent Job Shop Scheduling System

2012 ◽  
Vol 505 ◽  
pp. 65-74
Author(s):  
Lin Lin Lu ◽  
Xin Ma ◽  
Ya Xuan Wang
Keyword(s):  
Large Scale ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Simulation Software ◽  
Prototype System ◽  
Shop Scheduling ◽  
Model Combining ◽  
Proposed Model ◽  
Multi Agent ◽  
Global Planning

In this paper, a job shop scheduling model combining MAS (Multi-Agent System) with GASA (Simulated Annealing-Genetic Algorithm) is presented. The proposed model is based on the E2GPGP (extended extended generalized partial global planning) mechanism and utilizes the advantages of static intelligence algorithms with dynamic MAS. A scheduling process from ‘initialized macro-scheduling’ to ‘repeated micro-scheduling’ is designed for large-scale complex problems to enable to implement an effective and widely applicable prototype system for the job shop scheduling problem (JSSP). Under a set of theoretic strategies in the GPGP which is summarized in detail, E2GPGP is also proposed further. The GPGP-cooperation-mechanism is simulated by using simulation software DECAF for the JSSP. The results show that the proposed model based on the E2GPGP-GASA not only improves the effectiveness, but also reduces the resource cost.

scholarly journals The territorial redefinition of the Vineyard Landscape in the sherry wine region (Spain)

2018 ◽  
Vol 22 (2) ◽  
pp. 95-101 ◽  
Author(s):  
Concepcion Foronda-Robles
Keyword(s):  
Local Economy ◽  
Architectural Heritage ◽  
Socioeconomic Impacts ◽  
Wine Tourism ◽  
The Public ◽  
Sherry Wine ◽  
Wine Sector ◽  
Global Planning ◽  
Historical Discourses ◽  
Wine Region

Abstract The wine sector is a sector that lives and breathes its history and identity; and where developmental alternatives are sought in order to be able to compete in the market. Vineyard areas are sold as rural paradises, where leisure, gastronomy, the landscape, and open-air activities all provide quality tourist experiences. The case of the Sherry Wine Region (Spain) illustrates local restructuring processes, changes in local-global planning, and the socioeconomic impacts of the globalization of food. The symbiosis between the specific, the global, and the historical discourses gives rise to reflections on this region’s territorial redefinition; and highlights its architectural heritage, its landscape, and the gastronomic experiences on offer. Diversification is regenerating the local economy, and wine, and wine tourism, are both the focus of a new territorial policy strategy designed to face the challenges of globalization, and common bonds for partnerships between the public and the private sectors.

Recent Advances in Robot Trajectory Planning in a Dynamic Environment

2021 ◽  
Vol 14 ◽  
Author(s):  
Hongxin Zhang ◽  
Rongzijun Shu ◽  
Guangsen Li
Keyword(s):  
Trajectory Planning ◽  
Dynamic Environment ◽  
Local Planning ◽  
Robot Trajectory ◽  
Recent Advances ◽  
Dynamic Obstacle Avoidance ◽  
Dynamic Obstacle ◽  
Task Collaboration ◽  
Global Planning ◽  
Robot Trajectory Planning

Background: Trajectory planning is important to research in robotics. As the application environment changes rapidly, robot trajectory planning in a static environment can no longer meet actual needs. Therefore, a lot of research has turned to robot trajectory planning in a dynamic environment. Objective: This paper aims at providing references for researchers from related fields by reviewing recent advances in robot trajectory planning in a dynamic environment. Methods: This paper reviews the latest patents and current representative articles related to robot trajectory planning in a dynamic environment and introduces some key methods of references from the aspects of algorithm, innovation and principle. Results: In this paper, we classified the researches related to robot trajectory planning in a dynamic environment in the last 10 years, introduced and analyzed the advantages of different algorithms in these patents and articles, and the future developments and potential problems in this field are discussed. Conclusion: Trajectory planning in a dynamic environment can help robots to accomplish tasks in a complex environment, improving robots’ intelligence, work efficiency and adaptability to the environment. Current research focuses on dynamic obstacle avoidance, parameter optimization, real-time planning, and efficient work, which can be used to solve robot trajectory planning in a dynamic environment. In terms of the combination of multiple algorithms, multi-sensor information fusion, the combination of local planning and global planning, and multi-robot and multi-task collaboration, more improvements and innovations are needed. It should create more patents on robot trajectory planning in a dynamic environment.

scholarly journals High-Level Path Planning for an Autonomous Sailboat Robot Using Q-Learning

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1550 ◽  
Author(s):  
Andouglas Gonçalves da Silva Silva Junior ◽  
Davi Henrique dos Santos ◽  
Alvaro Pinto Fernandes de Negreiros ◽  
João Moreno Vilas Boas de Souza Silva ◽  
Luiz Marcos Garcia Gonçalves
Keyword(s):  
Path Planning ◽  
Dead Zone ◽  
Weather Conditions ◽  
Target Point ◽  
Q Learning ◽  
Ground Station ◽  
Global Route ◽  
Global Planning ◽  
High Level ◽  
Path Planner

Path planning for sailboat robots is a challenging task particularly due to the kinematics and dynamics modelling of such kinds of wind propelled boats. The problem is divided into two layers. The first one is global were a general trajectory composed of waypoints is planned, which can be done automatically based on some variables such as weather conditions or defined by hand using some human–robot interface (a ground-station). In the second local layer, at execution time, the global route should be followed by making the sailboat proceed between each pair of consecutive waypoints. Our proposal in this paper is an algorithm for the global, path generation layer, which has been developed for the N-Boat (The Sailboat Robot project), in order to compute feasible sailing routes between a start and a target point while avoiding dangerous situations such as obstacles and borders. A reinforcement learning approach (Q-Learning) is used based on a reward matrix and a set of actions that changes according to wind directions to account for the dead zone, which is the region against the wind where the sailboat can not gain velocity. Our algorithm generates straight and zigzag paths accounting for wind direction. The path generated also guarantees the sailboat safety and robustness, enabling it to sail for long periods of time, depending only on the start and target points defined for this global planning. The result is the development of a complete path planner algorithm that, together with the local planner solved in previous work, can be used to allow the final developments of an N-Boat making it a fully autonomous sailboat.

Mobile Robot Motion Planning to Avoid Obstacle Using Modified Ant Colony Optimization

2015 ◽  
Vol 776 ◽  
pp. 396-402 ◽  
Author(s):  
Nukman Habib ◽  
Adi Soeprijanto ◽  
Djoko Purwanto ◽  
Mauridhi Hery Purnomo
Keyword(s):  
Mobile Robot ◽  
Motion Planning ◽  
Ant Colony Optimization ◽  
Optimal Path ◽  
Initial Position ◽  
Ant Colony ◽  
Robot Motion ◽  
Complex Environments ◽  
Point To Point ◽  
Global Planning

The ability of mobile robot to move about the environment from initial position to the goal position, without colliding the obstacles is needed. This paper presents about motion planning of mobile robot (MR) in obstacles-filled workspace using the modified Ant Colony Optimization (M-ACO) algorithm combined with the point to point (PTP) motion in achieving the static goal. Initially, MR try to plan the path to reach a goal, but since there are obstacles on the path will be passed through so nodes must be placed around the obstacles. Then MR do PTP motion through this nodes chosen by M-ACO, in order to form optimal path from the choice nodes until the last node that is free from obstacles. The proposed approach shows that MR can not only avoid collision with obstacle but also make a global planning path. The simulation result have shown that the proposed algorithm is suitable for MR motion planning in the complex environments with less running time.

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