Optimal mobile-agent measurement and mapping of a dynamic field
Creators: name: Li, Tianwei
Publisher: name: No Publisher Supplied
Year: 2021

description: In this dissertation, the problem of optimal measurement and mapping of dynamic signals over a multi-dimensional field using mobile agent is studied. Great efforts have been made in mobile sensing since a couple of decades ago due to its applications in a broad range of areas from ocean floor mapping to nano material studies and significant hardware-software improvements that dramatically lowered the technological barriers and cost. The existing works on mobile sensing, however, have largely ignored the dynamics evolutions of the signals across the field. Instead, the studies have been focused on the motion planning and communication of the agents to optimize the sensing performance and improve the robustness against disturbances and/or communication constraints. Thus, large temporal errors can be induced in the signals acquired, particularly when the sensing capability of the mobile agent is limited compared to the field and the dynamics to be measured/mapped. Capturing the dynamics of the signals across the field is important in, for example, measuring the evolutions of the nanomechanical properties of live cells during the endocytosis process using atomic force microscope (AFM). Mobile sensing of a dynamic field is challenging in applications such as AFM measurement of dynamics evolutions at nanoscale as the signals to be measured are coupled together (e.g., the topography evolutions and the mechanical variations of a live cell undergoing biological processes). Moreover, challenges also arise from the fact that compared to the dynamics of the signals and the size of the field, the mobile sensing capability available tends to be limited. As a result, the temporal resolution of the mapping obtained via continuous scanning through the surface becomes low, whereas large temporal errors occur when measuring only at discrete points of interests. This dissertation of research work, thereby, is motivated to tackle these challenges. Through the research of this dissertation, a suite of control and motion planning algorithms have been developed for optimal measurement and mapping of dynamic fields using a mobile agent. First, for measurement and mapping of multiple signals coupled together across the entire field, a Kalman-filtering-based approach integrated with a data-driven online iterative feedforward and feedback control technique is developed to decouple the signals of sample topography tracking and the nanomechanical mapping, and then experimentally implemented for topography and nanomechanical mapping of a homogeneous sample on AFM. Second, for measurement of location-depended multiple distributions across the mapping area, a gradient-based adaptive Kalman-filtering technique was developed to account for the effect of spatial variation when decoupling the sample topography tracking signal and the nanomechanical mapping signal, and then implemented on simultaneous topography and nanomechanical mapping of a heterogeneous sample on AFM. Then, for measuring the dynamic evolutions of a field by using single mobile sensor with limited mobility, a compressed-sensing based method was proposed to account for the temporal-spatial coupling and the optimization of the sensing performance, then analyzed in details through a computer simulation of the discrete nanomechanical mapping (DNM) process. Finally, the dissertation is concluded with retrospection of the results obtained and some prospection on future work.