Bio-Inspired Multi-Mode Optic Flow Sensors for Micro Air Vehicles

Monitoring wide-field surrounding information is essential for vision-based autonomous navigation in micro-air-vehicles (MAV). Our image-cube (iCube) module, which consists of multiple sensors that are facing different angles in 3-D space, can be applied to the wide-field of view optic flows estimation ( -Compound eyes) and to attitude control ( Ocelli) in the Micro Autonomous Systems and Technology (MAST) platforms. In this paper, we report an analog/digital (A/D) mixed-mode optic-flow sensor, which generates both optic flows and normal images in different modes for Compound eyes and -Ocelli applications. The sensor employs a time-stamp based optic flow algorithm which is modified from the conventional EMD (Elementary Motion Detector) algorithm to give an optimum partitioning of hardware blocks in analog and digital domains as well as adequate allocation of pixel-level, column-parallel, and chiplevel signal processing. Temporal filtering, which may require huge hardware resources if implemented in digital domain, is remained in a pixel-level analog processing unit. The rest of the blocks, including feature detection and timestamp latching, are implemented using digital circuits in a column-parallel processing unit. Finally, time-stamp information is decoded into velocity from look-up tables, multiplications, and simple subtraction circuits in a chip-level processing unit, thus significantly reducing core digital processing power consumption. In the normal image mode, the sensor generates 8-b digital images using single slope ADCs in the column unit. In the optic flow mode, the sensor estimates 8-b 1-D optic flows from the integrated mixed-mode algorithm core and 2-D optic flows with an external timestamp processing, respectively.