The study and application of machine vision in early years mostly focus on a single camera. However, the trend of research on multiple cameras has been developed recently. Due to highly complicated correlation among multiple images, the arrangement of multiple cameras was restricted to the encirclement layout for acquiring more than one views of a target object. Furthermore, it has been well known that the special architecture of insect compound eyes contributes outstanding capability for precise and efficient observation of moving objects. If this technique can be transferred to the domain of engineering applications, significant improvement on visual tracking of moving objects will be greatly expected. This thesis builds a visual servo system with trinocular cameras by mimicking the configuration of compound eye of insects for tracking an object moving in 2D space. The arrangement of the trinocular cameras is divergent, and this system can function properly without the information of distance between the object and the cameras.

It has been well known that the architecture of insect compound eyes contributes outstanding capability for precise and efficient observation of moving objects. If this technique can be transferred to the domain of engineering applications, significant improvement on visual tracking of moving objects will be greatly expected. The brightness variation, caused by relative velocity of the camera and environment in a sequence of images, is called optical flow. The advantage of the optical-flow-based visual servo methods is that features of the moving object do not have to be known in advance. Therefore, they can be applied for general positioning and tracking tasks. The purpose of this thesis is to develop a visual servo system with trinocular cameras. For mimicking the configuration of compound eyes of insects, the arrangement of the divergent trinocular cameras is applied. In order to overcome possible difficulties of unknown or uncertain parameters, an image servo technique using the robust discrete-time sliding-mode control algorithm to track an object moving in 2D space is developed.

This dissertation presents a technique for recovering translational motion parameters using two simplified planar compound-like eye schemes, namely a parallel trinocular system and a single-row Superposition-type Planar Compound-like Eye (SPCE). In the parallel trinocular scheme, a least squares estimation algorithm is developed for recovering the translational motion parameters. The proposed approach resolves the matrix singularity problem encountered when attempting to recover motion parameters using a conventional binocular scheme. To further reduce the computational complexity of the motion estimation process, a compact closed-form scheme is also proposed to estimate the translational motion parameters. The closed-form algorithm not only resolves the matrix singularity problem, but also avoids the requirement for matrix manipulation. As a result, it has a low computational complexity and is therefore an ideal solution for performing motion estimation in complex, real-world visual imaging applications following an initial image filtering process. The performance of the closed-form algorithm is evaluated by performing a series of numerical simulations in which translational displacements of various magnitudes in three-dimensional space are recovered in both noise-free and perturbed environments. In general, the results demonstrate that the translational motion parameters can be reconstructed with a high degree of accuracy provided that the motion in the depth direction is limited to small displacements only. Having developed a motion estimation scheme for a parallel trinocular system, additional charge coupled device (CCD) cameras are added in the horizontal direction to create a single-row SPCE. Translational motion models for the SPCE are then constructed by stacking the optical flow equations in the horizontal direction. The ego-translational parameters are then extracted using a simple least squares estimation algorithm. The simulation results reveal that the introduction of additional cameras to the machine vision system ensures an excellent motion estimation performance without the need for filters of any kind even when the viewing field is characterized by significant noise or the CCD deployment within the SPCE configuration has a non-uniform distribution. Overall, the parallel binocular scheme and single-row SPCE configuration presented in this dissertation demonstrate a high degree of robustness toward noise and enable the motion estimation process to be performed in a rapid and computationally efficient manner using a simple least squares approximation approach. Whilst science can not realistically hope to improve upon the visioning capabilities found in the insect world, the techniques presented in this dissertation nonetheless provide a sound foundation for the development of artificial planar-array compound-like eyes which mimic the mechanisms at work in biological compound eyes and attain an enhanced visioning performance as a result.