近期，实验室博士生田昊宇作为第一作者，实验室教师姚蔚然作为通信作者的论文“Task-Extended Utility Tensor Method for Decentralized Multi-Vehicle Mission Planning”已被国际权威期刊“IEEE Transactions on Automation Science and Engineering”录用。

该论文提出了一种基于市场机制的任务扩展效用张量算法（TEUTA）。在多车辆系统的任务规划问题中，考虑了任务调度对车辆轨迹的影响，并以张量的形式表示了不同先前任务点下的车辆任务执行效用。此外，在迭代策略的基础上，提出了一种任务扩展效用张量迭代算法（TEUTIA），以提高算法的计算复杂度。为实现这两种算法，设计了任务执行效用估计模型和算法框架。仿真和实验结果表明，与非张量方法相比，TEUTA和TEUTIA可以获得更高的任务执行性能，并且TEUTIA具有更好的计算效率。

Abstract

In multi-vehicle systems, the coupling problem between the task allocation and path planning and the variability of task execution solutions creates challenges for utility estimation and affects the effectiveness of distributed mission planning. To characterize the effect of task sequences on the task utilities and implement a task-extended distributed allocation, we propose a task-extended utility tensor algorithm (TEUTA) based on market mechanism. In the mission planning problem of multi-vehicle system, we consider the impact of the task schedule on the vehicle trajectory, and indicate the vehicle task execution utilities under different preceding task points in the form of tensors. Further, a task-extended utility tensor iterative algorithm (TEUTIA) is presented based on an iterative strategy to improve the algorithm in terms of computational complexity. A task execution utility estimation model and an algorithm framework are designed for the implementation of the two proposed algorithms. The simulation and experimental results show that compared with the non-tensor method, TEUTA and TEUTIA can achieve higher task execution performance, and TEUTIA has better computational efficiency.