.png){kind=icon}
返回
Deep reinforcement learning path planning and task allocation for multi-robot collaboration
.png){kind=icon}
OA
AI
DOI：10.1016/j.aej.2024.08.102.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In the current technological landscape, Multi-Robot Systems (MRS) have become crucial for complex tasks, with applications in industrial automation, search and rescue, and intelligent transportation. However, existing techniques face challenges in path planning and task allocation, particularly regarding adaptability, real-time decision-making, and efficiency. Deep Reinforcement Learning (DRL) has emerged as a promising solution due to its robust learning capabilities. To address these challenges, we propose an innovative DRL-MPCGNNs model that integrates Deep Reinforcement Learning, Model Predictive Control (MPC), and Graph Neural Networks (GNNs). Our model aims to optimize path planning and task allocation in multi-robot systems. Through rigorous experiments in simulated environments, we validated our model's effectiveness, demonstrating significant improvements in path planning precision, task allocation efficiency, and inter-robot collaboration performance. These results highlight our model's practicality and offer new insights for future research and applications in multi-robot systems. Overall, our integrated model addresses key issues in multi- robot collaboration, contributing an innovative solution to the field's development. This research provides a novel approach for path planning and task allocation in multi-robot systems, laying a solid foundation for deploying intelligent robotic systems in complex and dynamic environments.
Keyword:
Path planning
Task allocation
Multi-robot
Deep reinforcement learning
Model predictive control
Graph neural networks
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.8
论文数:
6.3K
被引数:
2.6W
机构
引用论文
Towards Real-Time Path Planning through Deep Reinforcement Learning for a UAV in Dynamic Environments通过深度强化学习实现动态环境中无人机的实时路径规划
PREAI
分享收藏Faster nonconvex low-rank matrix learning for image low-level and high-level vision: A unified framework.png)
INFORMATION FUSION
INFORMATION FUSIONIF15.5
PREAI
分享收藏A new energy-aware tasks scheduling approach in fog computing using hybrid meta-heuristic algorithm
PREAI
分享收藏
查看更多内容