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

大型机器人团队可以完成诸如扫雷等复杂任务。为了做到这一点，从机器人到计算机科学的研究人员已经开发出算法，让多智能体机器人群中的每个智能体在优化多个目标时做出明智的选择。在这个层面上，多目标优化技术将使多智能体系统更有效，更定制，以完成手头的任务，节省时间，寿命和能源;然而，现有的理论已经落后。事实上，在具有多个目标的多智能体问题中，现有文献主要考虑智能体以相同优先级优化不同功能的问题;然而，存在许多情况下目标的重要性不同。例如，机器人团队可能想要探索一个区域的不同区域，或者智能体在最小化能耗和旅行时间时可能在轨迹规划中具有不同的优先级。因此，多目标优化多智能体问题的下一个突破是使智能体能够单独确定目标的优先级。 因此，通过分布式多目标优化，研究计划的目标是提高多智能体系统的决策技能，使新的实用功能，增加代理能力，并提供更广泛的操作条件，这样的系统。为了支持研究计划的目标，并帮助下一个突破，这项发现补助金包括四个创新和独立的项目，但这些项目是相互关联的。特别是，该研究计划的前五年将专注于开发分布式多目标优化方法，解决多智能体环境中现有的理论局限性，包括群体智能技术，并将分布式多目标优化方法集成到觅食和目标搜索任务中。该计划将对各个领域产生重大影响。从技术和人道主义的角度来看，通过开发的算法，排雷机器人将根据代理实时接收的外部数据独立地优先考虑需要更多勘探的地区。它将提高勘探效率，从而拯救更多的生命。此外，所开发的算法将改善觅食任务的多机器人系统中使用的许多应用程序，如无意的石油泄漏的表面化学撇除。从环境和社会的角度来看，该算法将更有效地调度智能电网中的分布式能源，从而为消费者和利益相关者节省资金，并保护环境。同时，在智能交通系统领域，该算法将是分布式路径规划的有用工具。

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.