The aim of the paper is to propose a navigation strategy applied to a class of rotorcraft having a free rod-suspended load. The presented approach relies on the Linear Kalman Filter to estimate the not only the state vector but also a generalized disturbance term containing parametric, couplings and external uncertainties. A simple hierarchical control is used to drive the motion of the rotorcraft, which is thus updated with the estimation of the disturbance evolving during the a navigation task. Despite the time-scale separation due to the underactuated nature of the flying robot, the estimation approach has shown its effectiveness considering the same sampling time. A detailed simulation model is used to evaluate the performance of the proposal under different disturbed scenarios

In this paper, a state-feedback linear-quadratic regulator (LQR) is proposed for event-based control of a linear system. An interesting property of LQRs is that an optimal response of the system can be obtained in accordance to some specifications, like the actuator limits. An integral action is also added in order to not only restrict the study to null stabilization but also to tracking. The idea is to consider an external control loop and stabilize the integral of the error between the measurement and a desired setpoint to track. However, an event-triggered integral can lead to important overshoots when the interval between two successive events becomes large. Therefore, an exponential forgetting factor of the sampling interval is proposed as a solution to avoid such problems. The whole proposal is tested on a real-time system (a gyroscope) in order to highlight its ability, the reduction of control updates and the respect to the actuator limits.

The observer presented in this paper, which was based on the use of three minimalistic bio-inspired Visual Motion Sensors (VMS) detecting Optic Flow (OF) cues, states was intended as a backup solution in the case of Inertial Measurement Unit (IMU) failure. Contrary to most previous Guidance Navigation and Control (GNC) solutions for planetary landing, which have involved a sensor suite including an IMU, an innovative strategy is presented here for estimating states without any need for inertial measurements, based solely on information about the relative velocity of the images of the surrounding environment. A Linear Parameter Varying (LPV) observer designed on a LPV system linearized around a reference trajectory, estimates: the ventral OF, the expansion OF and the local pitch angle. A previously developed observer was applied here to a larger class of nonlinear systems by making an ingenious change of variable. Simulations performed on a lunar landing scenario yielded satisfactory performance and showed the robustness of the OF based observer to initial uncertainties and measurement noise.

The 6-pixel low-speed Visual Motion Sensor (VMS) inspired by insects' visual systems presented here performs local 1-D angular speed measurements ranging from 1.5°/s to 25°/s and weighs only 2.8 g. The entire optic flow processing system, including the spatial and temporal filtering stages, has been updated with respect to the original design. This new lightweight sensor was tested under free-flying outdoor conditions over various fields onboard a 80 kg unmanned helicopter called ReSSAC. The visual disturbances encountered included helicopter vibrations, uncontrolled illuminance, trees, roads, and houses. The optic flow measurements obtained were finely analyzed online and also offline, using the sensors of various kinds mounted onboard ReSSAC. The results show that the optic flow measured despite the complex disturbances encountered closely matched the approximate ground-truth optic flow.