论文标题
分布式数据驱动的多代理协作腿部运动的预测控制
Distributed Data-Driven Predictive Control for Multi-Agent Collaborative Legged Locomotion
论文作者
论文摘要
这项工作的目的是定义一个计划者，该计划者可以为复杂的多机构系统启用强大的腿部运动，该系统由几个自动限制的四倍体组成。为此，我们采用了一种基于行为系统理论的方法来模拟由自动限制引起的复杂和高维结构。然后将所得模型与分布式控制技术同时使用，以便在保留代理之间的耦合时在跨代理之间共享计算负担。最后，该分布式模型是在预测控制器的背景下构建的，从而导致了轨迹计划的稳定方法。该方法在模拟中进行了多达五个代理的模拟测试，并在三个A1 A1四足动物的机器人上进一步验证了各种不确定性，包括有效载荷，粗糙的地形和推动干扰。
The aim of this work is to define a planner that enables robust legged locomotion for complex multi-agent systems consisting of several holonomically constrained quadrupeds. To this end, we employ a methodology based on behavioral systems theory to model the sophisticated and high-dimensional structure induced by the holonomic constraints. The resulting model is then used in tandem with distributed control techniques such that the computational burden is shared across agents while the coupling between agents is preserved. Finally, this distributed model is framed in the context of a predictive controller, resulting in a robustly stable method for trajectory planning. This methodology is tested in simulation with up to five agents and is further experimentally validated on three A1 quadrupedal robots subject to various uncertainties, including payloads, rough terrain, and push disturbances.
