Task Allocation in Groups of Mobile Robots under Uncertainty using an Adaptive Ant Colony Algorithm

Abstract

This paper addresses the task allocation problem in groups of mobile robots operating under parametric and stochastic uncertainty caused by sensor noise, environmental dynamics, and incomplete information about tasks. A task distribution method is proposed based on a modified ant colony optimization (ACO) algorithm incorporating an adaptive mechanism responsive to environmental changes. A mathematical model of multi-objective optimization is developed, accounting for task completion time, energy consumption, and solution robustness. An enhanced algorithm is presented, capable of adjusting its parameters in response to the emergence of new tasks and robot failures. The objective function integrates total execution time, cumulative energy cost, and robustness to external disturbances. A key feature of the proposed modification is the linear adaptation of the weighting coefficients associated with the pheromone and heuristic components in the probabilistic decision rule, implemented through progressive adjustment of the α and β parameters. Numerical experiments confirm the superiority of the adaptive algorithm over the baseline version. It is shown that dynamic parameter tuning significantly accelerates solution reconfiguration in response to environmental changes. The results demonstrate the viability of the proposed approach and its effectiveness in constructing intelligent control systems for mobile robot groups operating in complex and dynamic environments.