In this paper, we present an exoskeleton called the Unmanned Technology Research Centre Exoskeleton (UTRCEXO), which utilizes a shadow walking assistance mechanism. The shadow walking assistance mechanism shadows the operators walking after visible movements by the operator, such as a heel-off event or a toe-off event. The UTRCEXO determines the operators walking intention with the toe-off event, when the toe leaves the ground, and then shadows the operators walking. The UTRCEXO implementing the shadow walking assistance mechanism shadows the first step of the operator immediately.
In this paper, we propose a novel method, illumination-invariant vegetation detection (IVD), to improve many aspects of agriculture for vision-based autonomous machines or robots. The proposed method derives new color feature functions from simultaneously modeling the spectral properties of the color camera and scene illumination. An experiment in which an image sample dataset was acquired under nature illumination, including various intensities, weather conditions, shadows and reflections, was performed. The results show that the proposed method (IVD) yields the highest performance with the lowest error and standard deviation and is superior to six typical methods. Our method has multiple strengths, including computational simplicity and uniformly high-accuracy image segmentation.
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