Connectivity-Preserving Distributed Informative Path Planning for Mobile Robot Networks
Connectivity-Preserving Distributed Informative Path Planning for Mobile Robot Networks
复制标题
DOI：
10.1109/lra.2024.3362133
复制
发表时间：
2024-03
影响因子：
5.2
通讯作者：
Binh T. Nguyen;Truong X. Nghiem;Linh Nguyen;H. M. La;Thang Nguyen
中科院分区：
文献类型：
--
作者：
Binh T. Nguyen;Truong X. Nghiem;Linh Nguyen;H. M. La;Thang Nguyen
这封信解决了分布式信息路径规划（IPP）问题的移动的机器人网络，以最佳地探索空间领域。每个机器人都能够在导航环境的同时收集嘈杂的环境测量值，并使用高斯过程和本地数据构建自己的空间现象模型。IPP优化问题是制定在一个信息丰富的方式，通过多步预测方案的连接保护和避免冲突的约束。局部高斯过程模型的共享超参数也被安排在路径规划优化问题中进行优化计算。利用乘子的邻近交替方向法，可以有效地分布式求解优化问题。从理论上证明了网络的连通性随着时间的推移而保持，同时优化问题的解收敛到一个稳定点。通过真实数据集的合成实验验证了该方法的有效性。
This letter addresses the distributed informative path planning (IPP) problem for a mobile robot network to optimally explore a spatial field. Each robot is able to gather noisy environmental measurements while navigating the environment and build its own model of a spatial phenomenon using the Gaussian process and local data. The IPP optimization problem is formulated in an informative way through a multi-step prediction scheme constrained by connectivity preservation and collision avoidance. The shared hyperparameters of the local Gaussian process models are also arranged to be optimally computed in the path planning optimization problem. By the use of the proximal alternating direction method of multiplier, the optimization problem can be effectively solved in a distributed manner. It theoretically proves that the connectivity in the network is maintained over time whilst the solution of the optimization problem converges to a stationary point. The effectiveness of the proposed approach is verified in synthetic experiments by utilizing a real-world dataset.