Large-scale Multi-robot System

多机器人系统具有对故障更鲁棒和完成任务更快等优点。在分布式多智能体系统中，由于一个机器人的故障不会停止整个使命，因此整个团队更加健壮。在大面积测量或自然灾害中部署第一响应者等应用中，多机器人系统能够更快，更有效地实现目标。多机器人系统中的任务分配、编队控制和协调等问题正受到研究人员的积极研究。随着最近机器学习和深度学习方法的出现，正在提出有希望的结果来解决多智能体系统中存在的时间和空间复杂性等问题。 多智能体系统中的一个关键领域是多机器人同时定位和地图绘制（SLAM），其动机是多个机器人可以比单个机器人更快，更准确地完成探索和地图绘制任务。许多基于协作的操作需要快速、自主地完成，并且需要定位和映射。其中一些应用包括消防、清洁作业（如清除海上溢油）、水下勘探、救援作业和维护调查。基于团队的SLAM的一个重要优势是，配备不同传感器的不同类型的机器人可以更好地了解环境。 提出的设备，一个大规模的异构机器人团队，是必不可少的研究，在多机器人SLAM，也为其他程序的研究，如编队控制，任务分配等，提出的设备是迫切需要使用的新的研究计划，如使用触觉和操作为基础的SLAM在多机器人SLAM，多机器人主动神经SLAM，开发多机器人消毒系统以防止COVID-19的传播，并使用机器学习方法提高多机器人系统的计算复杂性。拟议的设备将用于验证算法设计在现实世界中的场景与动态和移动的对象。

Multi-robot systems have benefits such as being more robust to failures and being faster in accomplishing the tasks. In a distributed and multi-agent system, the whole team is more robust since the failure of one of the robots does not halt the entire mission. In applications such as surveying large areas or deploying first responders in natural disasters, multi-robots systems are able to achieve the objectives faster and more efficiently. Topics such as task allocation, formation control, and coordination in multi-robot systems are being actively investigated by researchers. With the advent of the recent machine learning and deep learning methods, promising results are being presented to address the existing problems such as time and space complexity in multi-agent systems. One of the key areas in multi-agent systems is multiple-robot simultaneous localization and mapping (SLAM), motivated by the fact that exploration and mapping tasks can be done faster and more accurately by multiple robots than by a single robot. Many collaboration-based operations need to be completed fast and autonomously and require localization and mapping. Some of these applications include fire fighting, cleaning operations (like removing marine oil spills), underwater exploration, rescue operations, and maintenance investigations. An important advantage of team-based SLAM is that different types of robots, equipped with different sensors, can provide a better understanding of the environment. The proposed equipment, a large-scale heterogeneous team of robots, is essential for research in multiple-robot SLAM and also for research in other programs such as formation control, task allocation, etc. The proposed equipment is urgently needed to be used in novel research programs such as using haptics and manipulation-based SLAM in multiple-robot SLAM, multi-robot active neural SLAM, developing multi-robot disinfection systems to prevent the spread of COVID-19, and improving the computational complexity of multi-robot systems using machine learning methods. The proposed equipment will be used to verify the algorithmic designs in real-world scenarios with dynamic and moving objects.