Proprioceptive feedback control improves peristaltic turning in confined environments.

Due to their compliant mechanics, many soft and flexible robots are well-suited to operate in confined spaces that challenge traditional legged and wheeled robots. In particular, undulatory and peristaltic robots excel in enclosed tunnels and pipes, as their locomotion methods and compliance allow them to wriggle and squeeze through narrowings and gaps. However, these environments frequently contain features that are impassable with passive deformation alone, such as sharp turns and T-junctions. In this work, we developed the first closed-loop, peristaltic turning algorithm that scales turning angle based on contact force feedback in confined spaces. We verified the controller's effectiveness using a dynamic model of a soft worm robot as our test platform. The model was sent through pipe bends with varying radii of curvature, diameters, and geometries, using both open-loop and closed-loop controllers. The different turning strategies were compared using speed and the cost of transport (COT) as the primary metrics. Our adaptive algorithm was the only turning method which maintained high speed performance across all bends tested. The model averaged just a 1.6% loss in speed compared to the best fixed turning angle strategy for each pipe curvature. Adaptive turning also allowed the model to traverse small radius of curvature (ROC) bends (0.5 and 0.6 m) in which the model became stuck when using straightline locomotion. Furthermore, of the controllers we tested, adaptive turning offered the lowest COT in most pipe bend geometries. We also showed that the controller could autonomously transition from left to right turns (and vice versa) and adjust to a spiral turn with a continuously changing ROC. A worm robot with this controller, these sensors, and turning capabilities would be well equipped for operation in a wide variety of unstructured and confined environments.