Verification of a UAV/UGV heterogeneous swarm based on coalitional game theory

Unlike unmanned platforms comprising a single type or individual, heterogeneous unmanned swarm platforms excel in performing more intricate tasks and exhibit heightened adaptability to challenging battlefield environments. When coordinating tasks on a heterogeneous unmanned swarm platform, task assignment is a crucial aspect that requires considering various constraints and objectives related to the heterogeneous unmanned platforms and the tasks. Traditional task assignment methods exhibit low efficiency and handle large-scale complex tasks with difficulty. The alliance game divides a group according to the attributes and preferences of the individual by forming an alliance composed of several participants to maximize the interests of the individual and the group. This paper focuses on investigating optimal task assignment strategies within unmanned heterogeneous swarm platforms, employing an enhanced coalitional game algorithm. First, by considering the initial position, speed, resources, and individual reputation of heterogeneous platforms in the task, a task allocation algorithm is proposed on the basis of a coalitional game with a spatial adaptive play (SAP) mechanism. The SAP algorithm is an adaptive learning method employed in spatial games, capable of self-adjustment based on the specific characteristics of task allocation problems. Notably, this algorithm tends to randomly select and update target individuals during each iteration process with equal probability. However, a challenge arises when attempting random agent selection in a distributed environment. To address this issue, a periodic adaptive selection mechanism is introduced to facilitate periodic updating. Furthermore, to enhance the convergence speed of SAP, neighbor information and historical information are considered during task updates. Second, in alignment with the task scenario, the necessary software and hardware platform for task execution is established. Finally, the proposed algorithm and the unmanned heterogeneous swarm platform are verified experimentally on the basis of the simulated battlefield environment. The mission objectives of each unit are reassigned on the basis of the environmental situation and resource integration of each combat unit. At the same time, when the battlefield situation changes, such as with the emergence of new targets or the elimination of the current target, the platform will allocate new combat units to form new alliances with the existing units. This allocation ensures the optimal use of resources and guarantees the completion of missions. The verification results demonstrate that the platform possesses comprehensive situational awareness, enabling it to respond promptly to battlefield changes, make informed judgments, optimize task allocation, and effectively coordinate combat units to achieve mission objectives.