Development of a Robot Assembly Task Planning System

This paper reports on an investigation into the proposed usability of virtual reality for a manufacturing application such as the assembly of a number of component parts into a final product. Before the assembly task itself is considered, the investigation explores the use of VR for the training of human assembly operators and compares the findings to conventionally adopted techniques for parts assembly. The investigation highlighted several limitations of using VR technology. Most significant was the lack of haptic feedback provided by current input devices for virtual environments. To address this, an instrumented object (IO) was employed that enabled the user to pick up and manipulate the IO as the representation of a component from a product to be assembled. The reported findings indicate that object manipulation times are superior when IOs are employed as the interaction device, and that IO devices could therefore be adopted in VEs to provide haptic feedback for diverse applications and, in particular, for assembly task planning.

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Design and simulation in SAR satellites’ task planning system using genetic algorithm with entropy operator

Aircraft Engineering and Aerospace Technology ◽

10.1108/aeat-11-2020-0247 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Wan Liu ◽

Zeyu Li ◽

Li Chen ◽

Dexin Zhang ◽

Xiaowei Shao

Keyword(s):

Genetic Algorithm ◽

Satellite Observation ◽

Operating Costs ◽

Planning System ◽

Task Planning ◽

Content Type ◽

Emergency Rescue ◽

Planning Algorithm ◽

Planning Problems ◽

Fitness Calculation

Purpose This paper aims to innovatively propose to improve the efficiency of satellite observation and avoid the waste of satellite resources, a genetic algorithm with entropy operator (GAE) of synthetic aperture radar (SAR) satellites’ task planning algorithm. Design/methodology/approach The GAE abbreviated as GAE introduces the entropy value of each orbit task into the fitness calculation of the genetic algorithm, which makes the orbit with higher entropy value more likely to be selected and participate in the remaining process of the genetic algorithm. Findings The simulation result shows that in a condition of the same calculate ability, 85% of the orbital revisit time is unchanged or decreased and 30% is significantly reduced by using the GAE compared with traditional task planning genetic algorithm, which indicates that the GAE can improve the efficiency of satellites’ task planning. Originality/value The GAE is an optimization of the traditional genetic algorithm. It combines entropy in thermodynamics with task planning problems. The algorithm considers the whole lifecycle of task planning and gets the desired results. It can greatly improve the efficiency of task planning in observation satellites and shorten the entire task execution time. Then, using the GAE to complete SAR satellites’ task planning is of great significance in reducing satellite operating costs and emergency rescue, which brings certain economic and social benefits.

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An Inteligent Tasks Planning System for Industrial Mobile Robots

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.162.308 ◽

2012 ◽

Vol 162 ◽

pp. 308-315 ◽

Cited By ~ 1

Author(s):

Alina Ninett Panfir ◽

Alexandra Covaci ◽

Gheorghe Leonte Mogan

Keyword(s):

Mobile Robots ◽

General Structure ◽

Planning System ◽

Complex Task ◽

Task Planning ◽

Multirobot System ◽

To Come ◽

Case Based

In this paper we present a general structure of an automatic task planner for a multirobot system. Our focus in this paper is to develop an intelligent complex task planning system that uses both model and case - based approach, while trying to come up with actions that support end goals. We provide an overall description of the proposed system and its integration in an implemented architecture.

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