Decentralized task allocation for multiple UAVs with task execution uncertainties

This work builds on a robust decentralized task allocation algorithm to address the multiple unmanned aerial vehicle (UAV) surveillance problem under task duration uncertainties. Considering the existing robust task allocation algorithm is computationally intensive and also has no optimality guarantees, this paper proposes a new robust task assignment formulation that reduces the calculation of robust scores and provides a certain theoretical guarantee of optimality. In the proposed method, the Markov model is introduced to describe the impact of uncertain parameters on task rewards and the expected score function is reformulated as the utility function of the states in the Markov model. Through providing the high-precision expected marginal gain of tasks, the task assignment gains a better accumulative score than the state of arts robust algorithms do. Besides, this algorithm is proven to be convergent and could reach a prior optimality guarantee of at least 50%. Numerical Simulations demonstrate the performance improvement of the proposed method compared with basic CBBA, robust extension to CBBA and cost-benefit greedy algorithm