This work explores the use of artificial whiskers as tactile sensors for enhancing the perception and navigation capabilities of mobile robots in challenging settings such as caves and underground mines. These environments exhibit inconsistent lighting conditions, locally self-similar textures, and general poor visibility conditions, that can cause the performance of state-of-the-art vision-based methods to decline. In order to evaluate the efficacy of tactile sensing in this context, three algorithms were developed and tested with simulated and physical experiments: a wall-follower, a navigation algorithm based on Theta*, and a hybrid approach that combines the two. The obtained results highlight the efficacy of tactile sensing for wall-following in intricate environments. When paired with an external method for pose estimation, it further aids in navigating unknown environments. Moreover, by integrating navigation with wall-following, the third, hybrid algorithm enhanced the map traversal speed by roughly 26−43% compared to standard navigation methods without wall-following.

Fine motor tests have been a workhorse in neurology, psychology and psychiatry nearly for one hundred years. In spite of its simplicity, just paper and pen required to conduct the test, their results were proven to be reliable and accepted by the medical community. Nevertheless, it should be noted that assessment of the testing results was and is performed visually by the practitioner, whereas no measurable numeric parameters are used. Such setting inevitably introduces a subjective component to the assessment. Introduction of digital tables and later tablet computers has opened new frontiers in the fine motor analysis. Nowadays tablet computer equipped with stylus pen allows collecting kinematic and pressure parameters describing aspects of the test invisible to the naked eye. In spite of the recent achievements in the digitisation of fine motor tests, very few attention was paid to the parameters of the tests itself. In this paper, the length of the alternating series test is investigated with respect to the accuracy of the classifiers, used to support diagnostics of the Parkinson's disease.