Gait Based Directional Bias Detection of Four-Legged Walking Robots

As an inevitable trend, more and more robots are designed to be sold as household products in recent years. Famous examples like AIBO, RoboSapien, and Kondo, though aiming on different functionality respectively, are all affordable by general family. Among the robots stated above, four-legged robots have great advantage on locomotion over stair, uneven or multilevel floor, and floor with scattered stuff. Four-legged robots can also be used as a mechanical mule and are capable of carrying significant payloads, such as BigDog manufactured by Boston Dynamics (Raibert et al., 2008). In this chapter, we discuss the directional bias problem in depth and introduce an approach to dynamically detect the direction bias utilizing gait pattern information and the feedback of accelerator sensor. To evaluate how effective this approach is, experiments are performed on two Sony’s AIBO robots. There are lots of research topics of four-legged robots such as balance control, gait generation, image recognition, walking bias detection, to name but a few. In this chapter, we focus on bias detection technique of four-legged robots. Comparing to mechanical mule, AIBO robot is designed to be light weighted and is equipped with plastic hemisphere on its feet such that it does not scratch the walking plane such as beech solid wood floor. This design is a reasonable result to a household robot, but it also makes the robot not able to step firmly, thus produces directional bias even when walking straight on flat and smooth plane. Since the directional bias of some AIBO robots is obvious and this kind of bias tends to accumulates as long as the robot is walking, it would be nice if there is an algorithm to automatically detect and correct the walking directional bias in real-time. The most popular sensors used to detect heading direction of robots are video cameras, gyroscopes and accelerometers. Most image processing algorithms to detecting directional bias are time consuming and require more computing power than accelerometer based approaches. Since AIBO does not have gyroscope equipped, we choose to develop our algorithm according to accelerometer data. In theory, the distance of bias can be calculated by integrating the acceleration twice, but the acceleration data obtained from AIBO is not accurate enough to generate trustworthy data. Therefore, another data source is necessary to enhance our algorithm. After analyzed the characteristics of the three-axial acceleration sensors on AIBO, we confirmed the reliability of three-axial acceleration sensor on AIBO 17