CCTEG China Coal Research Institute
State Key Laboratory of High Efficient Mining and Clean Utilization of Coal Resources

松散型合作群机器人系统在现阶段煤矿辅助机器人作业中具有广泛应用前景。但松散型合作群机器人系统的任务分配过程未向划分过程进行反馈，导致任务划分与分配过程高效性与合理性不足。针对该问题，提出一种基于改进型鲁宾斯坦协商策略的煤矿井下群机器人高效任务分配算法。根据群机器人系统任务划分与分配过程的多方博弈特点，将鲁宾斯坦协商策略由双方博弈向多方共同博弈方向延伸，提出多方协商博弈的“出价−讨价−还价”规则。从机器人个体执行能力与任务执行情况差异的角度出发，提出基于机器人个体单位时间任务完成量的折扣因子计算方法，以及基于各分配周期任务执行情况的任务完成状态反馈参数模型，以实现任务的动态划分与分配。通过3组机器人合作执行煤矿矿区的整体监测任务，对算法性能开展实验验证，结果表明：① 算法3（采用改进型鲁宾斯坦协商策略）的任务划分与分配效率较算法1（将每组无人机数量与运行速度乘积的比例直接作为3组无人机任务划分与分配的标准）、算法2（使用多方共同协商的鲁宾斯坦协商策略，但不考虑任务完成状态反馈参数）分别提升了30.10%，18.29%。② 基于算法3的3组无人机执行任务的平均最大时间差为42 s，较算法1、算法2分别优化了77.66%，65.29%，这是由于算法3通过引入任务完成状态反馈参数，及时对任务参与方的任务执行过程进行评估，将任务的分配和执行过程向任务的划分阶段进行反馈，使任务的划分与分配更加准确。

The loose cooperative group robot system has broad application prospects in the current coal mine auxiliary robot operation. However, the task assignment process of the loose cooperative group robot system did not provide feedback to the division process, resulting in insufficient efficiency and rationality of the task division and assignment process. To address this issue, an efficient task assignment algorithm for coal mine underground group robots based on an improved Rubinstein negotiation strategy is proposed. Based on the multi-party game features of task division and assignment in group robot systems, the Rubinstein negotiation strategy is extended from a bipartite game to a multi-party joint game. A "bid-bargain-counteroffer" rule for multi-party negotiation games is proposed. From the perspective of the difference between the execution capability and task execution status of individual robots, a discount factor calculation method based on the task completion quantity per unit time of robot individuals is proposed. A task completion status feedback parameter model based on the task execution status of each assignment cycle is also proposed to achieve dynamic task division and assignment. By collaborating with three groups of robots to perform overall monitoring tasks in coal mining areas, experimental verification is conducted on the performance of the algorithm. The results show the following points. ① Algorithm 3 uses an improved Rubinstein negotiation strategy. Algorithm 1 directly uses the ratio of the number of unmanned aerial vehicles in each group multiplied by their running speed as the standard for task division and assignment in three groups of unmanned aerial vehicles. Algorithm 2 uses the Rubinstein negotiation strategy of multi-party negotiation without considering the feedback parameters of task completion status. Algorithm 3 has a higher efficiency in task division and assignment than Algorithm 1 and Algorithm 2 by 30.10% and 18.29% respectively. ② The average maximum time difference for the three groups of unmanned aerial vehicles based on Algorithm 3 to execute tasks is 42 seconds. It is 77.66% and 65.29% optimized compared to Algorithm 1 and Algorithm 2, respectively. This is because Algorithm 3 introduces task completion status feedback parameters to timely evaluate the task execution process of the task participants. Algorithm 3 provides feedback on the task assignment and execution process to the task division stages, making the task division and assignment more accurate.

国家重点研发计划资助项目（2021YFB3201905）。

吴文臻. 煤矿井下群机器人高效任务分配算法[J]. 工矿自动化，2023，49(8)：60-69.

WU Wenzhen. Efficient task assignment algorithm for coal mine underground group robots[J]. Journal of Mine Automation，2023，49(8)：60-69.