Cooperative Task Allocation and Path Planning for Multi-UAVs in Low-Altitude Urban Intelligent Transportation Systems

In low-altitude urban intelligent transportation systems, efficient cooperative task allocation and path planning for multiple unmanned aerial vehicles (UAV) are critical for ensuring the effective execution of complex tasks. This paper proposes a distributed decision-making and autonomous planning framework to achieve cooperative task allocation and path planning for multi-UAVs in low-altitude urban traffic environment. The mission requirements of task allocation and path planning are modeled using evolutionary potential games and show that there exists a Nash equilibrium for the proposed potential function. An Improved Log-linear Learning Algorithm (ILLA) is proposed, and suitable Boltzmann parameters are derived which will enable the proposed ILLA to converge to the optimal Nash equilibrium with a probability one. Furthermore, a Constraint-Based Multi-layer Bidirectional Adaptive A-Star (CBMBA A-Star) algorithm is designed to find optimal and collision free paths for each UAV. Compared with the baseline method, simulation results demonstrate that the proposed approach improves the task reward by 11.67%, reduces the task execution time by 37.41%, and decreases run time by 61.02%, confirming its effectiveness and efficiency in the complex low-altitude urban traffic scenario.