Self-Organizing Microrobotic Systems with Physically Encoded Interactions

Abstract

Microrobots are sub-millimetric untethered devices that can move, interact with a given environment, and perform specific functions to realize a given task. Incorporation of self-organization capabilities such as collective motion and self-assembly into existing microrobotic systems offers significant potential for enhancing their performance and functional capabilities. For instance, organized swarms can amplify functional output with coordinated operation of itsagents. On the other hand, sophisticated micromachines can be formed by self-assembly of heterogeneous modular components, which address different functions. Advances in these areas can enhance current microrobots with new capabilities that allows them to reconfigure theirstructures and dynamics for adaptability and multifunctionality, and to form swarms that effectivelyaddress their macroscopic targets with cooperation of large numbers of microrobots. Despite the potential of self-organizing microrobotic systems, there are still several keychallenges that restrict them from realizing their full potential. Microrobotic swarms can utilize different group formations for environmental adaptability and collective manipulation, and leverage collective effects to enhance their operation. However, current swarm formations arelimited to disordered aggregations of particles, and collective effects are not understood enough for implementing further improvements. On the other hand, the key challenge to the design of micromachines with modular components is their programmable assembly. Field-directed and self-propelled colloidal assembly have been used to build colloidal machines capable of performing complex motions and functions. However, as of now, there are no established methods to integrate heterogeneous components into micromachines with specified structure, dynamicsand function. The underlying challenge is the design of physical interactions to direct these self-organization processes in a programmable manner. This thesis addresses these limitations by developing two methods to encode physical interactions directing collective motion andself-assembly via the temporal complexity of actuating fields and the shape of building blocks, respectively.The first methodology is developed for guiding formation and control of mobile microrobotic swarms with well-defined collective behavior via engineered magnetic interactionsbetween microrobots. Chain microrobots are self-assembled from magnetic particles and can locomote over a solid substrate in response to a precessing magnetic field. Control over precessing magnetic field angles allows engineering attractive and repulsive magnetic interactionsamong microrobots and, thus, collective order with well-defined spatial organization and stable parallel operation over macroscale distances (1 cm). The swarm adopts a spreading formation under repulsive interactions to provide a homogeneous coverage of the workspace, which facilitates its navigation through obstacles and enhance its cargo transportation capacity. The significance of the demonstrated approach is that it addresses assembly, propulsion, and collective behavior of dense mobile microrobot swarms simultaneously via the precessingmagnetic field. From a general perspective, our results show how the temporal complexity of the driving field can serve to encode the types of structural and dynamical information needed to create functional microrobot swarms. Many swarm operations will require cohesive group formation to maintain self-bounded teams in the absence of confining boundaries. Cohesive group formation relies on a balancebetween attractive and repulsive interactions between agents. Using a precessing magnetic field, we found that chain microrobots self-organize into cohesive clusters due to a balance of their pairwise magnetic dipolar attraction andmultipolar repulsion. Self-organized microroboticclusters translate above a solid substrate via a hydrodynamic self-propulsion mechanism. Clustervelocity increases with cluster size, resulting from collective hydrodynamic effects. Clustering is promoted by the strength of cohesive interactions and hindered by heterogeneities of individual microrobots. Scalability of cohesive interactions allows formation of larger groups,whose internal spatiotemporal organization undergoes a transition from solid-like ordering to liquid-like behavior with increasing cluster size. Our work elucidates the dynamics of clustering under cohesive interactions, and presents an approach for addressing operation of microrobotsas localized teams. The second methodology describes the dynamic self-assembly of mobile micromachineswith desired configurations through pre-programmed physical interactions between structural and motor units. The assembly is driven by dielectrophoretic interactions, encoded in the three dimensional shape of individual parts. This method leverages the shape-dependent electricalpolarization of three dimensional bodies under electric fields to encode assembly pathways between heterogeneous components in terms of site and direction of the connecting parts. Using this methodology, micromachines assembled from magnetic and self-propelled motor parts exhibitreconfigurable locomotion modes and additional rotational degrees of freedom not available to conventional monolithic microrobots. The versatility of this site-selective assemblystrategy was further demonstrated on different reconfigurable, hierarchical, and 3D micromachineassemblies. This work demonstrates how shape-encoded assembly pathways enable programmable,reconfigurable mobile micromachines. We anticipate that the presented findings and methodologies can inspire scientist and engineers to design their functional microrobotic swarms and advanced micromachines in a programmable manner using self-organization. Developments in this area will allow the designof microrobotic swarms that can adopt different group formations (e.g., spreading, cohesion,aggregation), and leverage collective effects to boost their performance, as well as the creation of more sophisticated, modular micromachines, and their integration to multiscale hierarchicalsystems.