论文标题
与高斯分布式能量不确定性的异质车辆路由和组合
Heterogeneous Vehicle Routing and Teaming with Gaussian Distributed Energy Uncertainty
论文作者
论文摘要
对于在不确定环境中运行复杂任务的机器人群中，决策算法都必须考虑异质性和不确定性，这一点很重要。本文为车辆路由问题提供了随机旅行能源成本以及异质车辆和任务的随机编程框架。我们将异质性表示为线性约束，通过高斯过程回归估算不确定的能源成本，将这种随机性作为机会约束或随机追索成本，然后使用分支机构和剪切算法解决随机程序，以最大程度地减少预期能量成本。通过广泛的计算实验和实用的测试案例证明了性能和实用性。
For robot swarms operating on complex missions in an uncertain environment, it is important that the decision-making algorithm considers both heterogeneity and uncertainty. This paper presents a stochastic programming framework for the vehicle routing problem with stochastic travel energy costs and heterogeneous vehicles and tasks. We represent the heterogeneity as linear constraints, estimate the uncertain energy cost through Gaussian process regression, formulate this stochasticity as chance constraints or stochastic recourse costs, and then solve the stochastic programs using branch and cut algorithms to minimize the expected energy cost. The performance and practicality are demonstrated through extensive computational experiments and a practical test case.
