Manipulation and assembly of objects using spatially nonlinear stochastic force fields

Spatially nonlinear stochastic force fields, omnipresent in nature, exhibit extraordinary capabilities for shaping, transporting, and assembling objects—yet they remain vastly underexplored and underutilized. For example, winds, with stochastic variations in speed and direction, gradually form sand dunes with complex shapes and transport various objects, from seeds to debris, over vast distances. Inspired by such natural phenomena, this thesis explores the potential of such forces for the controlled assembly and remote manipulation of objects through two exemplary fields: vibration fields and airflow fields. Despite differing in physical origin, these fields share common qualitative properties, including nonlinear spatial variations in force strength and direction, combined with random fluctuations. First, the thesis demonstrates that acoustic vibration fields can be programmed to assemble particles into desired two-dimensional shapes on a vibrating plate. Prior approaches to vibrationinduced particle assembly were constrained to shapes aligned with the intrinsic nodal patterns of the vibrating plate. This work extends these boundaries by employing data-driven models to predict stochastic particle motion and an optimization algorithm to iteratively minimize the gap between the desired and actual particle distributions on the plate. This enables the assembly of up to 100 particles into complex, recognizable shapes, such as Latin letters and geometric figures. Unlike conventional externally directed assembly methods that rely on static field- or template-based energy minimization, the proposed approach mimics natural shaping processes driven by long-term, timevarying, nonlinear external stimuli. Second, airflow fields are examined as tools for remote, non-contact manipulation of diverseobjects. Conventional non-contact techniques, such as magnetic, acoustic, or optical manipulation, are mostly limited by material specificity, strict shape requirements, or short operational ranges, typically confined to a few centimeters. In contrast, this thesis demonstrates that the airflow field induced by a single air jet can remotely and automatically manipulate objects of diverse materials and shapes over distances of up to 2.7 meters away, achieving a mean path-following error of 1.5 cm or less. This approach is shown to be effective on both solid and water surfaces, demonstrates robustness in the presence of obstacles and airflow disturbances, and is applicable to a range of practical scenarios. To achieve automatic object manipulation, two control strategies are introduced: a model-free controller, which operates purely on machine vision feedback, and a model-based controller, which leverages an analytical airflow field model and learned object dynamics, enabling multi-object control. This research addresses the gap between nature-inspired and engineering-driven manipulation and assembly methods. It underscores the transformative potential of spatially nonlinear stochastic force fields in manufacturing, robotics, and other applications, offering novel paradigms for fieldbased assembly and versatile, non-contact manipulation technologies.