The rapid and ubiquitous proliferation of reliable rotorcraft platforms such as quadcopters has resulted in a boom in aerial robotics. However, rotorcraft have issues of safety, high noise levels, and low efficiency for forward flight. The objective of this NSF CAREER project, motivated by the problem of keeping airfields clear of disruptive avian flocks, is to develop control and sensing strategies for bird-like flapping robots that can be deployed in swarms to fend off “antagonists.” This talk gives an overview of technical challenges in developing a bio-inspired aerial robot platform from the dynamics and controls standpoint. We study the stability of coupled nonlinear oscillators by using contraction analysis to prove that flapping flight dynamics without traditional aerodynamic control surfaces can be effectively controlled by a reduced set of central pattern generator (CPG) parameters that generate complex 3D oscillatory motions of two main wings. New motion planning and flight control strategies are used to demonstrate agile, high-speed flight in a forest and perform perched landings on a human hand. This talk also presents a PDE boundary control formulation of controlling flexible wings described by PDEs and whose output is given by a spatial integral of weighted functions of the state. For wing bending, this talk discusses a novel control scheme based on a dyadic perturbation observer (DPO). A new design approach to optimal nonlinear estimation is discussed with emphasis on its application to vision-based Simultaneous Localization and Mapping (SLAM). The observer gain synthesis algorithm, called linear matrix inequality state-dependent algebraic Riccati equation (LMI-SDARE) guarantees stochastic incremental stability for a set of Itô stochastic nonlinear systems.