Sensing Contact Constraints in a Worm-like Robot by Detecting Load Anomalies

In earthworms, traveling waves of body contraction and elongation result in soft body locomotion. This simple strategy is called peristaltic locomotion. To mimic this kind of locomotion, we developed a compliant modular worm-like robot. This robot uses a cable actuation system where the actuating cable acts like the circumferential muscle. When actuated, this circumferential cable contracts the segment diameter causing a similar effect to the contraction due to the circumferential muscles in earthworms. When the cable length is increased, the segment diameter increases due to restoring forces from structural compliance. When the robot comes in contact with an external constraint (e.g., inner walls of a pipe) continued cable extension results in both slack in the cable and inefficiency of locomotion. In this paper we discuss a probabilistic approach to detect slack in a cable. Using sample distributions over multiple trials and naive Bayes classifier, we can detect anomalies in sampled data which indicate the presence of slack in the cable. Our training set included data samples from pipes of different diameters and flat surfaces. This algorithm detected slack within +/- 0.15 ms of slack being introduced in the cable with a success rate of 75 %. We further our research in understanding reasons for failure of the algorithm and working towards improvements on our robot.