Table 1.
Comparison of previous research and the current study on multi-robot task allocation.
Fig 1.
Scalable and energy-efficient task allocation in industry 4.0.
Fig 2.
Task completion rate vs. task complexity for different algorithms.
Description: This figure compares the task completion rate of different algorithms as task complexity increases. It demonstrates that the proposed auction algorithm and IBPSO maintain higher completion rates compared to baseline methods, especially as complexity rises.
Table 2.
Detailed performance metrics of the auction algorithm across various task scenarios.
Fig 3.
Coalition formation time vs. task success rate.
Description: The figure presents the relationship between coalition formation time and task success rate. The IBPSO algorithm forms coalitions faster while achieving high success rates, making it more efficient in handling complex tasks.
Fig 4.
Energy consumption comparison for simple and complex tasks.
Description: This figure shows energy consumption for both simple and complex tasks across various algorithms. The IBPSO algorithm exhibits lower energy consumption, highlighting its efficiency in managing both task types.
Table 3.
Coalition formation efficiency and energy optimization in IBPSO algorithm.
Table 4.
Comparative analysis of energy consumption and task accuracy in high-complexity scenarios.
Fig 5.
Task allocation time across different network conditions.
Description: This figure illustrates how task allocation time changes under different network conditions (low, moderate, and high latency). The auction algorithm is shown to perform faster than IBPSO under all network conditions, particularly in low-latency environments.
Table 5.
Impact of network conditions on task allocation efficiency and system stability.
Fig 6.
Real-World application performance metrics.
Description: This figure shows the performance of the proposed algorithms in real-world scenarios, highlighting high task success rates, improved energy efficiency, reduced operational costs, and increased production output across various manufacturing applications.
Table 6.
Real-world application scenarios and performance metrics.
Fig 7.
Impact of task complexity on operational costs and environmental impact.
Description: The figure depicts how increasing task complexity affects operational costs and environmental impact. Higher complexity tasks increase both costs and environmental impact, but the proposed algorithms reduce these effects compared to traditional methods.