Ground cover classification (Joint feedback based)
The effectiveness of a legged robot’s gait is highly dependent on the ground cover of the terrain the robot is traversing. It is therefore advantageous for a legged robot to adapt its behaviour to suit the environment. In order to achieve this, the robot must be able to detect and classify the type of ground cover it is traversing. We present a novel approach for ground cover classification that utilises position measurements of the leg servos to estimate the errors between commanded and actual positions of each joint. This approach gives direct insight into how the robot is interacting with the terrain. These position sensors are usually built into the actuators and therefore our approach has the advantage of not requiring any additional sensors.
Ground cover classification (Acoustics based)
Legged robots offer a more versatile solution to traversing outdoor uneven terrain compared to their wheeled and tracked counterparts. They also provide a unique opportunity to perceive the terrain-robot interactions by listening to the sounds generated during locomotion. Legged robots such as hexapod robots produce rich acoustic information for each gait cycle which includes the foot fall sounds and feet pushing on the terrain (support phase), as well as the sounds produced when the feet travel through the air (stride phase). Interpreting this information to perceive the terrain it is traversing makes available another valuable sensing modality which can feed in to higher level systems to facilitate robust and efficient navigation through unknown terrain. We have implemented an online real- time terrain classification system for legged robots that utilise features from the acoustic signals produced during locomotion. The system was trained on 7 different terrain types and the results of the experimental evaluations show a true positive rate of up to 95.1.