Abstract:
This study addresses the problem of task allocation among groups of mobile robots under conditions of parametric and stochastic uncertainty arising from sensor errors, environmental non-stationarity, and limited information about controlled objects. The primary objective is to adapt previously developed heuristic algorithms to real-world conditions, where sensor inaccuracies and incomplete knowledge of the environment are present. Three baseline approaches are considered: the ant colony algorithm, the Hopfield neural network, and the genetic algorithm. Each method is enhanced with specific modifications to account for input uncertainty: dynamic pheromone trail updates, adaptive adjustment of neuron weight coefficients, and interval-based estimation of environmental parameters. The paper presents a formal problem statement, mathematical models, and the design principles of the proposed task allocation algorithms. Numerical simulations were conducted to compare the performance of the modified algorithms against their baseline counterparts under varying levels of operational uncertainty. Results show that the proposed adaptive mechanisms improve task allocation efficiency by up to 20% compared to the original methods. Based on these findings, recommendations are formulated for selecting the optimal algorithm depending on specific operating conditions and control objectives. The study concludes that the proposed approaches are effective for the design of intelligent adaptive group control systems for mobile robots. Furthermore, these solutions can be extended to a broader class of problems, including dynamic resource reassignment and the organization of cooperative behavior among technical agents.
Keywords:task allocation, heuristic algorithms, mobile robot teams, ant colony algorithm, Hopfield neural network, genetic algorithm.
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