**3.1 Sensors**

Throughout the history of humanoid robotics research there has been an uneven study of the different human senses. Visual, auditory and tactile modalities have received more attention than olfactory and gustatory modalities. It may not be necessary for a robot to eat something but the information contained in taste and odor becomes important when developing higher levels of cognitive processes. Thinking about the future of humanoid robots we should be careful not to leave behind information that may be helpful for these machines to successfully interact with humans.

In this section Rob studies some of the devices that are either currently being used or are under development in different robotic platforms; they are categorized according to the human sense they relate to the most.

### **3.1.1 Vision**

Humanoid robots arguably pose the largest challenge for the field of computer vision due to the unconstrained nature of this application. Rob's plans for his autonomous humanoid involve it coping with an unlimited stream of information changing always in space, time, intensity, color, etc. It makes sense for Rob that among all sensory modalities within robotic applications, vision is (so far) the most computationally expensive. Just to have an idea of how challenging and broad the area of computer vision is, Rob remembers that in 1966 Marvin Minsky, considered by many to be the father of artificial intelligence (AI), thought one master project could "solve the problem of computer vision". More than 40 years have passed and the problem is far from solved.

In robotic applications, a fine balance between hardware and software is always used when working with vision. During the last decade, Rob witnessed an increasing interest on transferring some software tasks to the hardware side of this modality. Inspired by its biological counterpart, several designs of log-polar CCD or CMOS image sensors (Traver & Bernardino, 2010) (Fig. 2(b)) and hemispherical eye-like cameras (Ko et al., 2008) (Fig. 2(c)) have been proposed. However, the always increasing processing power of today's computers and specialized graphical processing units (GPU) have allowed many researchers to successfully tackle different areas of vision while keeping their low cost Cartesian cameras (Fig. 2(a)) and solve their algorithms with software. Nonetheless, Rob welcomes the attempts to reproduce the high resolution area at the center of the visual field, fovea, with hardware and/or software. He thinks that nature developed this specific structure for our eyes throughout evolution and it may be wise to take advantage of that development.

Fig. 2. Samples of different configurations for image sensors.

Rob identifies several challenges for the hardware component of vision sensors. Potentially the most important is the need to widen the field of view of current cameras. Normal limits for the human field of view are approximately 160 degrees in the horizontal direction and 135 degrees in the vertical direction. A typical camera used in robotic applications has a field of view around 60 degrees for the horizontal direction and 45 degrees for the vertical direction. Important information is lost in those areas where current cameras can not reach in a single stimulus. Adaptation to changes in light conditions is also a complex and difficult process for artificial vision, especially when working within a dynamic environment. Rob is considering a few options from the video security market based on infra-red and Day/Night technology that could be adapted for use in humanoid robots. Finally, the robotics community has experienced a growing interest on using RGB-D (Red-Green-Blue-Depth) cameras after the release of the low-cost motion capture system Kinect by Microsoft (Shotton & Sharp, 2011), Fig. 2(d). This technology combines visual information and high-resolution depth, opening therefore new possibilities for overcoming the challenges of 3D mapping and localization, object identification and recognition, tracking, manipulation, etc.
