Intelligent and distributed multi-objective methods for optimization and control of multiagents/cooperative systems

用于多智能体/协作系统优化和控制的智能分布式多目标方法

• 批准号: RGPIN-2021-03737
• 负责人: Blondin, MaudeJosée
• 金额: $ 2.4万
• 依托单位: Université de Sherbrooke
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=754108
• 关键词: Intelligent; distributed; multi; objective; methods

Large teams of robots can accomplish complex tasks such as land-mine clearance. To do so, researchers from robotics to computer science have developed algorithms to have each agent in the multiagent robotic swarm make smart choices in optimizing several objectives. At this level, multi-objective optimization techniques will make multiagent systems more efficient and customized to accomplish the tasks at hand, saving time, life, and energy; however, the existing theory is falling behind. Indeed, in multiagent problems with many objectives, the existing literature considers mainly problems in which agents optimize the diverse functions with equal priorities; however, there exist many cases in which there are objectives with different importance. For example, teams of robots may want to explore different regions of an area, or agents may have different priorities in trajectory planning when minimizing both energy consumption and travel time. Therefore, the next breakthrough in multi-objective optimization multiagent problems is to capacitate agents to prioritize objectives individually.  Thus, through distributed multi-objective optimization, the research program's objective is enhancing the decision-making skills of multiagent systems to enable new practical features, increase agent capacities, and provide a broader range of operating conditions of such systems. To support the research program's objective and help with the next breakthrough, this discovery grant consists of four innovative and independent projects but that are interconnected. In particular, the first five years of this research program will focus on developing distributed multi-objective optimization methods, addressing the existing theoretical limitations in a multiagent context, including in swarm intelligence techniques, and integrating distributed multi-objective optimization methods into foraging and target search tasks. The proposed program will have a significant impact on various fields. From a technological and humanitarian standpoint, with the developed algorithms, land-mine clearance robots will independently prioritize areas requiring more exploration according to external data received in real-time by the agents. It will boost exploration efficiency, resulting in saving more lives. Moreover, the developed algorithms will improve foraging tasks performed by multi-robot systems used in many applications such as surface chemical skimming of unintentional oil spills. From an environmental and social standpoint, the algorithms will dispatch the distributed energy resources in smart-grid more efficiently, resulting in saving money for the consumers and the stakeholders and preserving the environment. Also, in the domain of intelligent transportation systems, the algorithms will be useful tools for distributed routes planning.

大型机器人的团队的陆地上许可等人都可以在多种机器人群Marm Marm Mart选择中，以在此级别优化多个目标。自定义的是，在手头，生命和生活中，现有的理论确实落后于许多目标的多种问题例如，机器人的团队可能需要探索一个区域的不同，否则代理在轨迹计划中可能具有不同的优先级，在最小化能源消耗和旅行时间时， Thous CH计划的目标是增强多种系统的决策技巧，以支持研究计划的目标。研究计划将着重于开发分布式的多目标IMIZAIDE方法，现有的理论限制在多种环境中分布的多目标优化方法将其觅食和目标搜索任务，土地清除机器人将优先考虑对代理商实时收到的外部数据的优先级别。非确定的石油溢出。