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 2.8g. 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 80kg 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.

Optic flow-based autopilots for Micro-Aerial Vehicles (MAVs) need lightweight, low-power sensors to be able to fly safely through unknown environments. The new tiny 6-pixel visual motion sensor presented here meets these demanding requirements in terms of its mass, size, and power consumption. This 1-gram, low-power, fly-inspired sensor accurately gauges the visual motion using only this 6-pixel array with two different panoramas and illuminance conditions. The new visual motion sensor's output results from a smart combination of the information collected by several 2-pixel Local Motion Sensors (LMSs), on the basis of the "time of travel" scheme originally inspired by the common housefly's Elementary Motion Detector (EMD) neurons. The proposed sensory fusion method enables the new visual sensor to measure the visual angular speed and determine the main direction of the visual motion without any prior knowledge. Through computing the median value of the output from several LMSs, we also end up with a more robust, more accurate, and more frequently refreshed measurement of the one-dimensional angular speed.

One of the major limitations to the development of advanced wave energy converters (WECs) control strategies are the associated computational costs. For instance, model predictive control (MPC) strategies have the potential to obtain almost optimal performance, provided that the imperfect power conversion in the power take-off (PTO) system is correctly taken into account in the optimization criterion and that the incoming wave force can be estimated and forecast. However, demanding computational requirements as well as the unresolved issue of wave force estimation have so far prevented real-time implementation and validation of such MPC strategies. In this paper, we present the successful experimental results obtained on a scaled-down prototype of the well-known Wavestar machine. Performance comparisons are provided for nonlinear MPC versus a reference PI controller.