论文标题
Markov随机场优化的空中机器人群
Swarming of Aerial Robots with Markov Random Field Optimization
论文作者
论文摘要
与替代品相比，群是高度强大的系统，具有独特的好处。在这项工作中，我们提出了一种由生物启发和人工电位的机器人群控制方法，其中群体形成动力学是根据马尔可夫随机场（MRF）优化建模的。我们将内部代理的本地互动和外部环境影响整合到MRF中。在轨迹的不同阶段进行优化的形成构型可以看作是编队“形状”，这进一步使我们能够整合机器人的动态受限运动控制。我们表明，这种方法可用于生成动态可行的轨迹，以在复杂的环境中浏览空中机器人团队。
Swarms are highly robust systems that offer unique benefits compared to their alternatives. In this work, we propose a bio-inspired and artificial potential field-driven robot swarm control method, where the swarm formation dynamics are modeled on the basis of Markov Random Field (MRF) optimization. We integrate the internal agent-wise local interactions and external environmental influences into the MRF. The optimized formation configurations at different stages of the trajectory can be viewed as formation "shapes" which further allows us to integrate dynamics-constrained motion control of the robots. We show that this approach can be used to generate dynamically feasible trajectories to navigate teams of aerial robots in complex environments.
