Research on insect-inspired flapping-wing micro-aerial vehicles (FWMAV) has grown steadily in the past decade, aiming to address unique challenges in morphological construction, force production, and control strategy. In particular, effective methods for motion control still remain an open problem. This paper analyzes the mechanical impedance properties of the joint and their role in rotation of the wing and force production. The results suggest that in addition to previously observed relationship between set point and drag [1], the average lift force is also related to the stiffness of the joint. Furthermore, as long as changes in impedance properties are small, net lift and drag production are almost independent. These relationships are the basis of `tunable impedance' technique, a new approach to force/motion control in FWMAVs. A simple controller designed based on this method is used to simulate various flight maneuvers. The simulated MAV demonstrates exceptional performance, even in presence of measurement noise. This technique requires a fixed stroke profile for both wings, thus allowing to use a single stroke actuator - in a real MAV - with a bandwidth as low as the frequency of flapping. Impedance actuators also prove to have low bandwidth requirements.

This paper discusses a novel step by step path planning method which uses genetic algorithm (GA) to find the feasible and suitable paths in an environment with static and dynamic obstacles. To increase the speed of calculations, dimension of the search space is reduced by developing a new method to represent the environment. This representation method is based on detecting the corners of circumferential polygons of all obstacles as representatives of the environment. Each individual (or path) is a subsequence of these points including the start and destination points as the first and last genes in the sequence. Thus, each individual will be of a variable length. If an individual represents a path which passes over some obstacles, it will be unfeasible; otherwise it represents a feasible path. To produce new generations, four evolutionary operators are defined. The goal is to find a short feasible path, which clearly is not unique. In addition, using a step by step path planning approach and updating the representation of the environment at each step will result in a robust performance of the algorithm in two dimensional (2D) static and dynamic environments.