基于长短期记忆的多机器人轨迹规划:从 MPCC 中学习并使其更好

IF 6.8 Q1 AUTOMATION & CONTROL SYSTEMS
Jianbin Xin, Tao Xu, Jihong Zhu, Heshan Wang, Jinzhu Peng
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引用次数: 0

摘要

当前的多机器人系统轨迹规划方法面临着计算负担高、对复杂受限环境适应性不足等挑战,阻碍了生产和物流效率的提高。本文通过整合模型预测轮廓控制(MPCC)和长短期记忆(LSTM)网络,提出了一种创新的解决方案,用于多移动机器人的实时轨迹规划。基于 MPCC 生成的数据集,构建了一个定制的 LSTM 网络,用于离线学习这些数据集中的协作规划行为,随后以较低的计算负担在线生成平滑高效的轨迹。此外,混合控制方案结合了基于激光雷达的安全评估器,可在必要时切换到 MPCC,从而避免意外碰撞风险,确保多机器人系统的整体安全性和可靠性。我们在机器人操作系统(ROS)中实现并测试了所提出的混合 LSTM 方法。实验结果表明,与 MPCC 相比,混合 LSTM 方法的轨迹生产率提高了≈6%,计算负担减少了约 75%,从而为物流运输任务中的局部多机器人轨迹规划提供了一种前景广阔的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Long Short-Term Memory-Based Multi-Robot Trajectory Planning: Learn from MPCC and Make It Better

Long Short-Term Memory-Based Multi-Robot Trajectory Planning: Learn from MPCC and Make It Better

The current trajectory planning methods for multi-robot systems face challenges due to high computational burden and inadequate adaptability in complex constrained environments, obstructing efficiency improvements in production and logistics. This article presents an innovative solution by integrating model predictive contouring control (MPCC) and long short-term memory (LSTM) networks for real-time trajectory planning of multiple mobile robots. Based on the datasets generated by MPCC, a customized LSTM network is constructed to learn the collaborative planning behavior from these datasets offline, subsequently producing smooth and efficient trajectories online with a low computational burden. Moreover, a hybrid control scheme, incorporating a lidar-based safety evaluator, avoids unexpected collision risks by switching to MPCC when necessary, ensuring the overall safety and reliability of the multi-robot system. The proposed hybrid LSTM method is implemented and tested in the robot operating system (ROS) within diverse constrained scenarios. Experimental results demonstrate that the hybrid LSTM method achieves ≈6% enhancements in trajectory productivity and a reduced computational burden of roughly 75% compared to MPCC, thereby providing a promising solution for local multi-robot trajectory planning in logistics transportation tasks.

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来源期刊
CiteScore
1.30
自引率
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