This paper addresses task-allocation problems with uncertainty in situational awareness for distributed autonomous robots (DARs). The uncertainty propagation over a task-allocation process is done by using the Unscented transform that uses the Sigma-Point sampling mechanism. It has great potential to be employed for generic task-allocation schemes, in the sense that there is no need to modify an existing task-allocation method that has been developed without considering the uncertainty in the situational awareness. The proposed framework was tested in a simulated environment where the decision-maker needs to determine an optimal allocation of multiple locations assigned to multiple mobile flying robots whose locations come as random variables of known mean and covariance. The simulation result shows that the proposed stochastic task allocation approach generates an assignment with 30% less overall cost than the one without considering the uncertainty.
翻译:本文件论述分配自主机器人的情况认识不确定的任务分配问题。任务分配过程的不确定性传播是通过使用使用Sigma-Point抽样机制的未受重视的变异方法进行的。它极有可能用于通用的任务分配办法,因为没有必要在不考虑情况认识的不确定性的情况下修改已经制定的现有任务分配方法。拟议框架是在模拟环境中测试的,决策者需要确定分配给多个流动飞行机器人的多个地点的最佳分配,这些机器人的位置是已知平均值和变量的随机变数。模拟结果表明,拟议的随机任务分配办法所产生的任务分配总成本比不考虑不确定性的外差少30%。