A Novel Hybrid Auction Algorithm for Multi-UAVs Dynamic Task Assignment

Xiaojun Duan,Huiying Liu,Fan Zhang,Qing Cai,Xiaotian Han,Hong Tang

doi:10.1109/access.2019.2959327

Xiaojun Duan, Huiying Liu + Show 4 more

Open Access

https://doi.org/10.1109/access.2019.2959327

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Abstract

The auction algorithm is a widely used method for task assignments. However, most existing auction algorithms yield poor performance when applied to multi-UAVs dynamic task assignment. To end this, we propose a novel hybrid “Two-Stage” auction algorithm based on the hierarchical decision mechanism and an improved objective function, which simultaneously realizes heterogeneous multi-UAVs dynamic task assignment with limited resources of each UAV and avoidance obstacle path planning. In the first stage, according to the novel proposed hierarchical decision mechanism, we select a task that is urgently needed to be performed in the task group by using the decision function and three attribute values of tasks. After the first stage, it will result in a reasonable auction sequence, instead of random auction sequence as in previous algorithms. In the second stage, by considering the coverage factor and adaptive-limitation penalty term, a novel objective function is proposed and directs related UAVs for auction. In addition, we combine the structural advantages of the centralized and distributed auction algorithm, which greatly promotes its performance in dynamic task assignment. The experimental results demonstrate that the proposed method outperforms many state-of-the-art models in efficiency and robustness.

Highlights

For highly autonomous multi-UAVs systems, dynamic task assignment is a crucial problem that needs to be addressed efficiently
We propose a novel hybrid ‘‘Two-Stage’’ auction algorithm based on the hierarchical decision mechanism and centralized-distributed auction structure
According to the above analysis, we know that CONVENTIONAL AUCTION ALGORITHM (CAA), consensus-based bundle algorithm (CBBA) and DATP all use random auction sequence, which may produce poor performances in complex dynamic environment

Summary

INTRODUCTION

For highly autonomous multi-UAVs systems, dynamic task assignment is a crucial problem that needs to be addressed efficiently. Similar with the CBBA model, the auction sequence of DATP model is still generated randomly, which limits its performance in complex and changeable environment Both DTAP and CBBA limit the number of tasks that each agent can perform with a fixed constant, the flexibility of dynamic assignment results will be seriously affected. According to the above analysis, we know that CAA, CBBA and DATP all use random auction sequence, which may produce poor performances in complex dynamic environment To solve this problem in these models, we divide the multi-UAVs dynamic task assignment problem into two stages, where the first stage judges which tasks are prioritized, and the second stage executes auction process to find the suitable agent and plans the path with the consideration of avoiding obstacles. Related UAVs bid for the task based on the novel objective function and build their own local task queue and path

DYNAMIC TASK ASSIGNMENT MODEL

FIRST STAGE: UPPER DECISION Upper decision function

SECOND STAGE

Findings

DYNAMIC TASK ASSIGNMENT FOR NEW FOUND TARGETS