Feasibility Investigation of Obstacle-Avoiding Sensors Unit without Image Processing

Feasibility of a simple method to detect step height, slope angle, and trench width using four infrared-light-source PSD range sensors is examined, and the reproducibility and accuracy of characteristic parameter detection are also examined. Detection error of upward slope angle is within 2.5 degrees, while it is shown that the detection error of downward slope angle exceeding 20 degrees is very large. In order to reduce such errors, a method to improve range-voltage performance of a range sensor is proposed, and its availability is demonstrated. We also show that increase in trial frequency is a better way, although so as not to increase the detection delay. Step height is identified with an error of ±1.5？mm. It is shown that trench width cannot be reliably measured at this time. It is suggested that an additional method is needed if we have to advance the field of obstacle detection. 1. Introduction In the last decade, autonomous mobile robots have been attracting wide attention, and technical levels have dramatically advanced (see, for instance, [1]). Many robots for entertainment, room cleaning, and other services have already been developed [2]. To be really practical, robots must be able to acquire environmental events and/or spatial information of their environment. Some robots for entertainment have optical sensors, ultrasonic sensors, touch sensors, and other configurations which have been implemented. To create more autonomous robots that suit future applications, the 2D infrared range sensor [3] and CMOS-imager camera [4] are being studied extensively. In these studies, sensor downsizing is an ongoing concern. However, the newly developed sensors are still expensive, and computing overhead is apt to increase. This is a fundamental problem with the present research roadmap. 2D path planning for mobile robots has also been studied extensively [5, 6]; it is considered that combining a path planning method [7, 8] with a potential-field method [9, 10] or a mapping technique is a promising approach. These techniques are also needed for future self-learning robots. On the other hand, recently, a passive intelligent walker is proposed using a servo breaks [11]; in that trial, some obstacles (such as steep slope and steps) are detected. However, a user must change his/her front direction when the sensor has found an obstacle. In addition, the robot does not guide a better direction for walking to the user. Therefore, at least now, blind persons cannot use the walker. In this paper, how to detect and classify obstacles in front of a robot without a