Immigrants Based Adaptive Genetic Algorithms for Task Allocation in Multi-Robot Systems

2017 ◽  
Vol 16 (04) ◽  
pp. 1750025 ◽  
Author(s):  
Pranab K Muhuri ◽  
Amit Rauniyar
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Task Allocation ◽  
Performance Metrics ◽  
Genetic Operators ◽  
Comparative Performance ◽  
Adaptive Genetic Algorithms ◽  
Robot Systems ◽  
Key Issues ◽  
Multi Robot

Optimal task allocation among the suitably formed robot groups is one of the key issues to be investigated for the smooth operations of multi-robot systems. Considering the complete execution of available tasks, the problem of assigning available resources (robot features) to the tasks is computationally complex, which may further increase if the number of tasks increases. Popularly this problem is known as multi-robot coalition formation (MRCF) problem. Genetic algorithms (GAs) have been found to be quite efficient in solving such complex computational problems. There are several GA-based approaches to solve MRCF problems but none of them have considered the dynamic GA variants. This paper considers immigrants-based GAs viz. random immigrants genetic algorithm (RIGA) and elitism based immigrants genetic algorithm (EIGA) for optimal task allocation in MRCF problem. Further, it reports a novel use of these algorithms making them adaptive with certain modifications in their traditional attributes by adaptively choosing the parameters of genetic operators and terms them as adaptive RIGA (aRIGA) and adaptive EIGA (aEIGA). Extensive simulation experiments are conducted for a comparative performance evaluation with respect to standard genetic algorithm (SGA) using three popular performance metrics. A statistical analysis with the analysis of variance has also been performed. It is demonstrated that RIGA and EIGA produce better solutions than SGA for both fixed and adaptive genetic operators. Among them, EIGA and aEIGA outperform RIGA and aRIGA, respectively.

Task Allocation of Multi-Robot Systems Based on a Novel Explosive Immune Evolutionary Algorithm

2012 ◽  
Vol 246-247 ◽  
pp. 331-335 ◽  
Author(s):  
Ming Xin Yuan ◽  
Ya Feng Jiang ◽  
Yi Shen ◽  
Zhao Li Ye ◽  
Qi Wang
Keyword(s):  
Genetic Algorithm ◽  
Task Allocation ◽  
Population Diversity ◽  
Immune Genetic Algorithm ◽  
Population Number ◽  
Robot Systems ◽  
Search Speed ◽  
Search Precision ◽  
Evolutionary Mode ◽  
Multi Robot

To solve the task allocation of multi-robot systems, a novel explosive evolution - based immune genetic algorithm (EIGA) is presented. On the basis of the immune genetic algorithm (IGA), the population number of EIGA is increased quickly through explosive evolutionary mode, and then the better individuals are selected through the comparison of allelic genes, which can improve the population quality with the premise of ensuring the population diversity, and enhance the search speed and search precision of EIGA. Compared with the IGA and genetic algorithm (GA), the simulation results indicate that the proposed EIGA is characterized by quick convergence speed, high optimization precision and good stability, and the tasks are allocated rationally and scientifi-cally which realizes the task cooperation of multi-robot systems well.

scholarly journals Resilient Task Allocation in Heterogeneous Multi-Robot Systems

2021 ◽  
Vol 6 (2) ◽  
pp. 1327-1334
Author(s):  
Siddharth Mayya ◽  
Diego S. D'antonio ◽  
David Saldana ◽  
Vijay Kumar
Keyword(s):  
Task Allocation ◽  
Robot Systems ◽  
Multi Robot

Minimization of total trim loss occurring in pipe cutting in shipbuilding with genetic algorithm

2021 ◽  
pp. 147509022199169
Author(s):  
Abdullah Türk ◽  
Dursun Saral ◽  
Murat Özkök ◽  
Ercan Köse
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Search Space ◽  
Cutting Process ◽  
Genetic Operators ◽  
Critical Stage ◽  
Different Types ◽  
Trim Loss ◽  
Pipe Systems

Outfitting is a critical stage in the shipbuilding process. Within the outfitting, the construction of pipe systems is a phase that has a significant effect on time and cost. While cutting the pipes required for the pipe systems in shipyards, the cutting process is usually performed randomly. This can result in large amounts of trim losses. In this paper, we present an approach to minimize these losses. With the proposed method it is aimed to base the pipe cutting process on a specific systematic. To solve this problem, Genetic Algorithms (GA), which gives successful results in solving many problems in the literature, have been used. Different types of genetic operators have been used to investigate the search space of the problem well. The results obtained have proven the effectiveness of the proposed approach.

A Novel Approach With Bayesian Networks to Multi-Robot Task Allocation in Dynamic Environments

2021 ◽  
Author(s):  
Ching-Wei Chuang ◽  
Harry H. Cheng
Keyword(s):  
Bayesian Networks ◽  
Task Allocation ◽  
Low Cost ◽  
Dynamic Environments ◽  
Modern World ◽  
Robot System ◽  
Novel Approach ◽  
Robot Systems ◽  
Robot Task ◽  
Multi Robot

Abstract In the modern world, building an autonomous multi-robot system is essential to coordinate and control robots to help humans because using several low-cost robots becomes more robust and efficient than using one expensive, powerful robot to execute tasks to achieve the overall goal of a mission. One research area, multi-robot task allocation (MRTA), becomes substantial in a multi-robot system. Assigning suitable tasks to suitable robots is crucial in coordination, which may directly influence the result of a mission. In the past few decades, although numerous researchers have addressed various algorithms or approaches to solve MRTA problems in different multi-robot systems, it is still difficult to overcome certain challenges, such as dynamic environments, changeable task information, miscellaneous robot abilities, the dynamic condition of a robot, or uncertainties from sensors or actuators. In this paper, we propose a novel approach to handle MRTA problems with Bayesian Networks (BNs) under these challenging circumstances. Our experiments exhibit that the proposed approach may effectively solve real problems in a search-and-rescue mission in centralized, decentralized, and distributed multi-robot systems with real, low-cost robots in dynamic environments. In the future, we will demonstrate that our approach is trainable and can be utilized in a large-scale, complicated environment. Researchers might be able to apply our approach to other applications to explore its extensibility.

scholarly journals Analysis of Dynamic Task Allocation in Multi-Robot Systems

2006 ◽  
Author(s):  
Kristina Lerman ◽  
Chris Jones ◽  
Aram Galstyan ◽  
Maja J. Mataric
Keyword(s):  
Task Allocation ◽  
Dynamic Task ◽  
Dynamic Task Allocation ◽  
Robot Systems ◽  
Multi Robot
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