Implementation of an active perceptual scheme for legged locomotion of robots

For robots to traverse rugged terrain successfully using legged locomotion, they need not only constantly to maintain structural stability but also, and perhaps more importantly, to detect and adapt to changes in the terrain properties. The authors address the issue of exploration to extract material properties from a given surface for the specific purpose of aiding in and improving the quality of legged locomotion. While it is important to evaluate terrain properties prior to the start of locomotion, it is even more important to evaluate these properties actively during locomotion so that the robot does not sink, slip or fall. It is proposed that the legs of a robot be used not only for stepping and walking but also as probes to examine those properties of the surface that would contribute to the efficiency of locomotion, one way or another. The proposed framework for active perception for legged locomotion suggests that for stable stepping and walking in an unknown environment, it is necessary actively to recover the material properties of penetrability, compliance and surface traction from the supporting surface. These attributes must be recovered by exploratory procedures that are built into the mobile robotic system. This paper focusses on the implementation of the perceptual scheme so that feedback from the measurement of material properties is used to control robot foot forces during legged locomotion