Adaptive Market-Based Dynamic Task Allocation Under Environmental Uncertainty

This paper presents a novel consensus-based adaptive genetic-optimized auction (CAGA) algorithm to solve the dynamic task allocation (DTA) problem for a fleet of autonomous vehicles. The algorithm employs an auction routine for task assignment and a genetic algorithm (GA) to optimize task prices subject to the price update rule. The proposed algorithm is devised to achieve superior solutions in real-world applications. Hence, uncertainty theory was adopted to model uncertainties in task positions to create a realistic environment. In addition, Monte Carlo (MC) simulations are performed to effectively determine the degree of uncertainty. Several test scenarios have been carried out using other market-based methods, and the results illustrate the effectiveness of the algorithm.