论文标题
数据驱动的风险敏感模型模型预测控制多机器人系统中的安全导航
Data-Driven Risk-sensitive Model Predictive Control for Safe Navigation in Multi-Robot Systems
论文作者
论文摘要
安全导航是多机器人系统中的一个基本挑战，因为围绕机器人的未来轨迹的不确定性相互障碍。在这项工作中，我们提出了一种原则性的数据驱动方法，其中每个机器人反复解决一个有限的地平线优化问题，但要避免碰撞限制，后者被表达为分布稳健的有条件的条件价值危险风险（CVAR），对代理商和多面性障碍物几何学之间的距离。具体而言，对于与执行过程中观察到的预测误差样本构成的经验分布相近的所有分布，CVAR约束必须保持。该方法的一般性使我们能够在分布式和去中心化设置中通常施加的假设下出现的预测错误鲁棒性。我们通过利用凸面和Minmax二元性结果来得出这类约束的有限尺寸近似值，用于瓦斯斯坦分布在强大的优化问题上。在凉亭平台中实现的多人导航设置中说明了所提出的方法的有效性。
Safe navigation is a fundamental challenge in multi-robot systems due to the uncertainty surrounding the future trajectory of the robots that act as obstacles for each other. In this work, we propose a principled data-driven approach where each robot repeatedly solves a finite horizon optimization problem subject to collision avoidance constraints with latter being formulated as distributionally robust conditional value-at-risk (CVaR) of the distance between the agent and a polyhedral obstacle geometry. Specifically, the CVaR constraints are required to hold for all distributions that are close to the empirical distribution constructed from observed samples of prediction error collected during execution. The generality of the approach allows us to robustify against prediction errors that arise under commonly imposed assumptions in both distributed and decentralized settings. We derive tractable finite-dimensional approximations of this class of constraints by leveraging convex and minmax duality results for Wasserstein distributionally robust optimization problems. The effectiveness of the proposed approach is illustrated in a multi-drone navigation setting implemented in Gazebo platform.
