Distributed Data-Driven Predictive Control for Multi-Agent Collaborative Legged Locomotion
Distributed Data-Driven Predictive Control for Multi-Agent Collaborative Legged Locomotion
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DOI：
10.1109/icra48891.2023.10160914
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
Randall T. Fawcett;Leila Amanzadeh;Jeeseop Kim;A. Ames;K. Hamed
中科院分区：
文献类型：
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作者：
Randall T. Fawcett;Leila Amanzadeh;Jeeseop Kim;A. Ames;K. Hamed
这项工作的目的是定义一个计划，使强大的腿运动复杂的多智能体系统组成的几个完整的约束四足动物。为此，我们采用基于行为系统理论的方法来模拟复杂的和高维的完整约束引起的结构。然后，将所得到的模型与分布式控制技术相结合，使计算负担在代理之间共享，同时保持代理之间的耦合。最后，这个分布式模型的框架内的预测控制器，从而在一个鲁棒稳定的轨迹规划方法。这种方法进行了测试，在模拟多达五个代理，并进一步实验验证三个A1四足机器人受到各种不确定性，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.