Integrated task and motion planning in multi-robot systems.

This academic paper explores the complex utilization of integrated task and motion planning (ITMP) in multi-robot systems. Given the rapid advancement of robotics and automation, multi-robot systems have gained increasing attention in various applications, such as smart manufacturing, logistics storage, environmental surveillance, and dual or multi-armed robotic platforms. The fusion of task and motion planning has become a key technology in the development of multi-robot systems. Initially, the paper suggests numerous strategies to integrate task and motion data to improve planning efficiency, thus boosting the motion planning efficiency of robots during task execution. It then investigates the use of learning techniques, such as reinforcement learning and deep learning, to enhance task and motion planning. Ultimately, it presents experimental evidence to support the claim that the integration of task and motion planning can effectively increase the efficiency of multi-robot systems and optimize their performance. The paper also examines modeling methods for multi-robot systems, typically incorporating task data with motion data to better guide robots during task execution. It presents shared space graph methods, sample-based planning methods, and market-based methods. Furthermore, it reviews learning models used to train and optimize machine learning models in ITMP. It introduces the INFORMED algorithm, a learning search issue in the field of task and motion planning that employs multilayer perceptron (MLP) and graph neural networks (GNN) to model relationships between streams and objects. Through this paper, the authors aim to offer valuable insights for the research and application of ITMP in multi-robot systems, thereby promoting further progress in this area. [ABSTRACT FROM AUTHOR]