Distributed Task Allocation Algorithm for Scenarios with Sensor Noise and Communication Constraints

Distributed task allocation architecture utilizes the interaction and processing of multiple unmanned aerial vehicles (UAVs)’s local information to achieve efficient and consistent allocation results, which prevents the danger of single point failure and enhances scalability. However, UAV sensor noise and constrained communication between UAVs may lead to inconsistent cognition of target and UAV positions, which poses challenges on the design of robust distributed task allocation algorithms. This paper presents the distributed task allocation model of multiple UAVs considering cognition error, characterized by the variance of target locations and UAV locations affected by sensor noise and data transmission success probability. A robust distributed multi-UAV task allocation algorithm is proposed, which introduces the cost error limit matrix related to cognition error to optimize the target allocation update process and improve the accuracy of the allocation results. Simulation results demonstrate that the proposed algorithm can achieve consistent task allocation results for multiple UAVs in scenarios with cognition error and constrained communication.