Adaptive Tuning of Multi-Robot Systems for The Effective Mapping and Tracking of Dynamic Environments

多机器人系统(MRS)即将积极地颠覆和促进几个关键的经济部门，同时也为人类面临的一些重大挑战提供独特的解决方案，如应对气候变化和提高农业生产率。然而，即使是最先进的MRS技术，与在现实世界动态环境中自主操作所需的集体响应速度相比，也存在缓慢的集体响应速度。拟议的发现号研究计划将通过利用机器人群体在面对不断变化的环境时表现出的新颖的超响应集体行为的独特优势来解决这一关键问题。这项拟议研究的长期目标是设计、开发和展示在大规模、非结构化和快速演变的环境中运行的MRS的创新的超反应集体行为。该研究计划将集中于四个短期目标：(1)利用异质群体进行高响应的集体映射和跟踪；(2)通过自适应集体决策手段实现探索和开发的最佳平衡；(3)通过多智能体对抗性逆强化学习来推断新的快速集体演习；(4)开发一类基准问题来量化高动态环境下MRS操作的有效性。科学方法包括理论、多代理模拟和MRS实验的混合，以满足我们的目标，如果要向我们的长期目标取得真正进展，这些方法是必不可少的。这项工作将分为两篇博士论文和两篇硕士论文，为HQP提供机会参与令人兴奋的、具有高社会影响的尖端研究，直接造福于加拿大的高科技产业。几个国际合作(美国、法国、新加坡、荷兰)将扩大和扩大这一计划的成果。拟议的发现号研究计划及其在MRS超响应集体动力学方面的创新预计将对现场机器人、农业和工业机器人的应用产生重大影响。最终，该项目的成功有可能成为快速崛起的多机器人自动化市场的催化剂，由于加拿大经济的几个关键部门的非常高的需求，该市场正在经历戏剧性的激增。这一研究项目产生的创新可以为加拿大农业部门向超精密和自主农业的关键转型提供新的动力。为了充分利用机器人和自动化对农业5.0的潜力，需要在低技术准备水平下进行更多的基础研究。这正是发现号研究计划的意义所在，特别是针对具有学习和适应能力的不同系统的超快协调的新进展和创新解决方案。

Multi-robot systems (MRS) are about to positively disrupt and boost several key economic sectors, while also offering unique solutions to some of the grand challenges faced by humanity, such as dealing with climate change and increasing agricultural productivity. However, even the state-of-the-art in MRS technology suffers from a slow pace of collective response compared to what would be required to autonomously operate in real-world dynamic settings. The proposed Discovery research program will address this critical issue by exploiting the unique advantages of novel ultra-responsive collective behaviors exhibited by robot swarms when confronted with changing circumstances. The long-term aim of the proposed research is to design, develop and demonstrate innovative ultra-responsive collective behaviors of MRS operating in large-scale, unstructured, and fast-evolving environments. The research program will focus on four short-term objectives: (1) Leverage heterogeneous swarming for highly responsive collective mapping and tracking; (2) Optimally balance exploration and exploitation by means of adaptive collective decision-making; (3) Infer new swift collective maneuvers through multi-agent adversarial inverse reinforcement learning; (4) Develop a class of benchmark problems to quantify the effectiveness of MRS operations in highly dynamic environments. The scientific approaches consist of a blend of theory, multi-agent simulations, and MRS experiments appropriate to meet our objectives, and essential if real advances toward our long-term aim are to be made. The work will be partitioned between two doctoral and two master theses, providing opportunities for HQP to participate in exciting, cutting-edge research of high societal impact with direct benefit for Canada's high-tech industry. Several international collaborations (U.S., France, Singapore, The Netherlands) will amplify and augment the achievements of this program. The proposed Discovery research program with its innovations on ultra-responsive collective dynamics of MRS is expected to have a significant impact on applications in field robotics, agricultural and industrial robotics. Ultimately, the success of this program has the potential to be a catalyst in the fast-emerging market of multi-robot automation, which is experiencing a dramatic surge owing to a very high demand by several key sectors of Canada's economy. The innovations arising from this research program can give a fresh impetus to the pivotal transformation of Canada's agricultural sector toward ultra-precision and autonomous farming. To leverage the full potential of robotics and automation towards Agriculture 5.0, more fundamental research at low technological readiness levels is required. This is precisely what this Discovery research program is about, specifically novel advances and innovative solutions for the ultra-rapid coordination of heterogeneous systems, with learning and adaptation.