Dynamic and Self-adaptive Multi-agent Network for Optimal Operation of Engineering Processes

这一建议涉及具有共享资源的动态和网络化工程过程。在危险和部分已知的环境中，困难和复杂的工程任务可能需要自主、动态和异构代理（如机器人（移动或固定）、无人驾驶飞行器（uav）、水中推进设备和移动传感器节点）的协作、自动化和无线操作。可以使用多个传感器、执行器和其他设备，它们可能是移动的，并且可能必须在任务之间共享。申请人在本课程中涉及的工程应用示例如下：自然水源时空质量评价（基于移动传感器节点）2. 家庭护理、人类救援、工业生产的多机器人协作；3. 提取和输送石油沥青的管网的检查和维修。所建议的工作的主要目标是开发一个可扩展的系统框架，它将：容纳多个工程应用；2. 根据约束条件（如功耗、系统复杂性、成本），选择合适的智能体协同最优执行指定任务；3. 调整网络结构（例如，添加/删除代理；改变：传感器位置/方向和激活选择，采样率，代理位置和姿态，连接和设备参数）以提高性能，针对多个目标进行优化。这项工作涉及分析研究、计算机模拟、技术开发、实施和评估。研究活动将涉及自适应传感、传感数据估计和多传感器数据融合；多代理合作;以及多目标和参数/结构优化。具体的研究成果将包括新的或增强的：制定智能体模型和成本函数（用于性能、误差等）的方法，可以采用感官估计、设备定位和导航技术，以及智能体的自我意识建模；2. 传感器融合方法，可能包括改进和/或混合形式的技术，如贝叶斯方法、Dempster-Shafer证据理论、卡尔曼滤波的非线性变化和智能/软计算，它们具有相对的优点和缺点；3. 最优合作和决策技术，可能包括增强或混合生物学启发的方法（例如，群体智能，人工免疫系统，以机电设计商mdq和服务质量为目标函数的进化计算），马尔可夫决策过程mdp，博弈论，软计算和帕累托最优集。开发的方法将在一个工业现场实施和测试，结合水质监测和管道检查。

This proposal concerns dynamic and networked engineering processes with sharable resources. Difficult and complex engineering tasks in hazardous and partially-known environments may require cooperative, automated, and wireless operation of autonomous, dynamic, and heterogeneous agents such as robots (mobile or stationary), unmanned aerial vehicles (UAVs), devices that propel in water, and mobile sensor nodes. Multiple sensors, actuators, and other devices may be used, they may be mobile, and may have to be shared among tasks. Examples of engineering applications in this class, which the applicant is involved in are: 1. Spatiotemporal quality assessment (using mobile sensor nodes) of natural sources of water; 2. Multi-robot cooperation for homecare, human rescue, and industrial production; 3. Inspection and repair of pipeline networks that extract and distribute oil bitumen. The main objective of the proposed work is to develop a scalable system framework that will: 1. Accommodate more than one engineering application; 2. Select proper agents for cooperative optimal execution of a specified task, subjected to constraints (e.g., power consumption, system complexity, cost); 3. Adapt the network structure (e.g., add/drop agents; change: sensor location/orientation and activation choice, sampling rate, agent location and pose, connectivity and device parameters) for performance improvement, optimized with respect to multiple objectives. The work involves analytical research, computer simulation, technology development, implementation, and evaluation. The research activities will pertain to adaptive sensing, estimation using sensory data, and multi-sensor data fusion; multi-agent cooperation; and multi-objective and parameter/structure optimization. Specific research outcomes will include new or enhanced: 1. Methodologies for formulating agent models and cost functions (for performance capability, error, etc.), which may employ sensory estimation, device localization and navigation techniques, and self-awareness modeling for intelligent agents; 2. Sensor fusion methodologies, which may incorporate improved and/or hybrid forms of such techniques as the Bayesian approach, Dempster-Shafer evidence theory, nonlinear variations of Kalman filter, and intelligent/soft computing, which have relative advantages and disadvantages; 3. Optimal cooperation and decision making techniques, which may incorporate enhanced or hybridized biology-inspired methods (e.g., swarm intelligence, artificial immune systems, evolutionary computing with mechatronic design quotientMDQ and quality of service as objective functions), Markov decision processMDP, game theory, soft computing, and Pareto-optimal sets. The developed methodologies will be implemented and tested at an industrial site, combining water quality monitoring and pipeline inspection.