A flexible evolutionary algorithm for task allocation in multi-robot team
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 11056 LNAI, Page: 89-99
2018
- 7Citations
- 3Usage
- 4Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations7
- Citation Indexes7
- Usage3
- Abstract Views3
- Captures4
- Readers4
Conference Paper Description
The paper presents an Evolutionary Algorithm (EA) based framework capable of handling a variety of complex Multi-Robot Task Allocation (MRTA) problems. Equipped with a flexible chromosome structure, customized variation operators, and a penalty function, the EA demonstrates the capability to switch between single-robot and multi-robot cases of MRTA and entertains team heterogeneity. The framework is validated and compared against a Genetic Algorithm based representation and a heuristic-based solution. The experimental results show that the presented EA provides better overall results to the task allocation problem with faster convergence and lesser chances of sub-optimal results.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85053155507&origin=inward; http://dx.doi.org/10.1007/978-3-319-98446-9_9; http://link.springer.com/10.1007/978-3-319-98446-9_9; http://link.springer.com/content/pdf/10.1007/978-3-319-98446-9_9; https://ir.iba.edu.pk/faculty-research-series/89; https://ir.iba.edu.pk/cgi/viewcontent.cgi?article=1088&context=faculty-research-series; https://doi.org/10.1007%2F978-3-319-98446-9_9; https://dx.doi.org/10.1007/978-3-319-98446-9_9; https://link.springer.com/chapter/10.1007/978-3-319-98446-9_9
Springer Nature America, Inc
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know