The Design and Characterization of a Testing Platform for Quantitative Evaluation of Tread Performance on Multiple Biological Substrates

In this study, an experimental platform is developed to quantitatively measure the performance of robotic wheel treads in a dynamic environment. The platform imposes a dynamic driving condition for a single robot wheel, where the wheel is rotated on a translating substrate, thereby inducing slip. The normal force of the wheel can be adjusted mechanically, while the rotational velocity of the wheel and the translational velocity of the substrate can be controlled using an open-loop control system. Wheel slip and translational speed can be varied autonomously while wheel traction force is measured using a load cell. The testing platform is characterized by testing one micropatterned polydimethylsiloxane (PDMS) tread on three substrates (dry synthetic tissue, hydrated synthetic tissue, and excised porcine small bowel tissue), at three normal forces (0.10, 0.20, and 0.30 N), 13 slip ratios (−0.30 to 0.30 in increments of 0.05), and three translational speeds (2, 3, and 6 mm/s). Additionally, two wheels (micropatterned and smooth PDMS) are tested on beef liver at the same three normal forces and translational speeds for a tread comparison. An analysis of variance revealed that the platform can detect statistically significant differences between means when observing normal forces, translational speeds, slip ratios, treads, and substrates. The variance due to within (platform error, P = 1) and between trials (human error, P = 0.152) is minimal when compared to the normal force (P = 0.036), translational speed ( P = 0.059), slip ratio (P = 0), tread (P = 0.004), and substrate variances ( P = 0). In conclusion, this precision testing platform can be used to determine wheel tread performance differences on the three substrates and for each of the studied parameters. Future use of the platform could lead to an optimized micropattern-based mobility system, under given operating conditions, for implementation on a robotic capsule endoscope.