scholarly journals Modeling of Task Planning for Multirobot System Using Reputation Mechanism

2014 ◽  
Vol 2014 ◽  
pp. 1-12
Author(s):  
Zhiguo Shi ◽  
Jun Tu ◽  
Yuankai Li ◽  
Junming Wei
Keyword(s):  
Task Allocation ◽  
Calibration Function ◽  
Task Planning ◽  
Biped Robots ◽  
Task Decomposition ◽  
Reputation Mechanism ◽  
Multirobot System ◽  
Time Calibration ◽  
Experimental Apparatus ◽  
Simulation And Experiment

Modeling of task planning for multirobot system is developed from two parts: task decomposition and task allocation. In the part of task decomposition, the conditions and processes of decomposition are elaborated. In the part of task allocation, the collaboration strategy, the framework of reputation mechanism, and three types of reputations are defined in detail, which include robot individual reputation, robot group reputation, and robot direct reputation. A time calibration function and a group calibration function are designed to improve the effectiveness of the proposed method and proved that they have the characteristics of time attenuation, historical experience related, and newly joined robot reward. Tasks attempt to be assigned to the robot with higher overall reputation, which can help to increase the success rate of the mandate implementation, thereby reducing the time of task recovery and redistribution. Player/Stage is used as the simulation platform, and three biped-robots are established as the experimental apparatus. The experimental results of task planning are compared with the other allocation methods. Simulation and experiment results illustrate the effectiveness of the proposed method for multi-robot collaboration system.

An Inteligent Tasks Planning System for Industrial Mobile Robots

2012 ◽  
Vol 162 ◽  
pp. 308-315 ◽  
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.

Research of Allocation for Uncertain Task Based on Genetic Algorithm

2014 ◽  
Vol 902 ◽  
pp. 324-329
Author(s):  
Yi Hui He ◽  
Ming Cong Pan ◽  
Wei Xu ◽  
Yong Bin Zou
Keyword(s):  
Genetic Algorithm ◽  
Task Allocation ◽  
Probabilistic Reasoning ◽  
Decomposition Tree ◽  
Uncertain Environments ◽  
Calculation Algorithm ◽  
Task Decomposition ◽  
Allocation Method ◽  
Reasoning Algorithm ◽  
Fitness Calculation

The traditional task allocation method is applied in the certain environments that the relationships between tasks and the execution abilities of agents are determined. This paper analyses the uncertain factors caused by uncertain environments. These factors exist in the process of task decomposition and execution, such as the uncertainty of tasks, their relationships and agents execution. Then an uncertain task allocation method based on genetic algorithm is presented. According to the structure of the decomposition tree, this paper puts forward a probabilistic reasoning algorithm for calculating fitness. To the end, an example is given to illustrate the effectiveness of fitness calculation algorithm.

Non-Linear Description for the Calcined Product of Ca-Based Absorbent

2011 ◽  
Vol 356-360 ◽  
pp. 1609-1612
Author(s):  
Ri Guang Wei ◽  
Jian Mei ◽  
Hong Wei Chen ◽  
Jian Qiang Gao ◽  
Chun Bo Wang
Keyword(s):  
Pore Size ◽  
Pore Volume ◽  
Pore Radius ◽  
Model Simulation ◽  
Exponential Model ◽  
Distribution Model ◽  
Volume Distribution ◽  
Distribution Models ◽  
Experimental Apparatus ◽  
Simulation And Experiment

This paper was based on pore volume distribution function which can be measured by experimental apparatus, proposed two methods of establishing microstructure distribution model for the calcined product of Ca-based absorbent, got the distribution models of pore volume, specific surface and pore length for the absorbent, and carried out comparison studies for the pore size distribution characteristics which was got both by model simulation and experiment, then compared the two models to observe the applicability of them. The result has showed that these two models both can describe the pore size and its distribution of Ca-based absorbent completely, the equivalent aperture of the logarithmic model was as same as definition, it was more appropriate when the pore radius is relatively small, and when the pore radius was relatively large, exponential model was better.

GRSTAPS: Graphically Recursive Simultaneous Task Allocation, Planning, and Scheduling

2021 ◽  
pp. 027836492110520
Author(s):  
Andrew Messing ◽  
Glen Neville ◽  
Sonia Chernova ◽  
Seth Hutchinson ◽  
Harish Ravichandar
Keyword(s):  
Motion Planning ◽  
Task Allocation ◽  
Integrated Treatment ◽  
Task Planning ◽  
Levels Of Abstraction ◽  
Solution Quality ◽  
Planning And Scheduling ◽  
Allocation Planning ◽  
And Task ◽  
Multi Robot

Effective deployment of multi-robot teams requires solving several interdependent problems at varying levels of abstraction. Specifically, heterogeneous multi-robot systems must answer four important questions: what (task planning), how (motion planning), who (task allocation), and when (scheduling). Although there are rich bodies of work dedicated to various combinations of these questions, a fully integrated treatment of all four questions lies beyond the scope of the current literature, which lacks even a formal description of the complete problem. In this article, we address this absence, first by formalizing this class of multi-robot problems under the banner Simultaneous Task Allocation and Planning with Spatiotemporal Constraints (STAP-STC), and then by proposing a solution that we call Graphically Recursive Simultaneous Task Allocation, Planning, and Scheduling (GRSTAPS). GRSTAPS interleaves task planning, task allocation, scheduling, and motion planning, performing a multi-layer search while effectively sharing information among system modules. In addition to providing a unified solution to STAP-STC problems, GRSTAPS includes individual innovations both in task planning and task allocation. At the task planning level, our interleaved approach allows the planner to abstract away which agents will perform a task using an approach that we refer to as agent-agnostic planning. At the task allocation level, we contribute a search-based algorithm that can simultaneously satisfy planning constraints and task requirements while optimizing the associated schedule. We demonstrate the efficacy of GRSTAPS using detailed ablative and comparative experiments in a simulated emergency-response domain. Results of these experiments conclusively demonstrate that GRSTAPS outperforms both ablative baselines and state-of-the-art temporal planners in terms of computation time, solution quality, and problem coverage.

Task allocation strategies for cooperative task planning of multi-autonomous satellite constellation

2019 ◽  
Vol 63 (2) ◽  
pp. 1073-1084 ◽  
Author(s):  
Feng Yao ◽  
Jiting Li ◽  
Yuning Chen ◽  
Xiaogeng Chu ◽  
Bang Zhao
Keyword(s):  
Task Allocation ◽  
Task Planning ◽  
Satellite Constellation ◽  
Allocation Strategies
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