A bio-inspired robotic climbing robot to understand kinematic and morphological determinants for an optimal climbing gait

Abstract

Robotic
advances for increasingly complex tasks especially in the field of search and
rescue or exploration are limited for wheeled systems, therefore the study of
legged locomotion for robotic applications has become more important. To successfully
navigate in regions with rough terrain, a robot must not only be able to
negotiate obstacles, but also climb steep inclines. Following the principles of biomimetics, we
developed a modular bio-inspired climbing robot, using generic hardware components
and rapid prototyping, aiming to mimic the lizards bauplan including an
actuated spine, shoulders, and feet which interlock with the surface via claws.
We included the ability to modify gait and hardware parameters and
simultaneously collect data with the robot’s sensors on climbed distance, slip
occurrence and efficiency.

We first
explored the speed-stability trade off and its interaction with limb swing
phase dynamics, finding a sigmoidal pattern of limb movement resulted in the
greatest distance travelled. By modifying wrist orientation, we found two optima
for both speed and stability, suggesting multiple stable configurations. We varied
spine and limb range of motion, again showing two possible optimum
configurations, and finally varied the centre of pro- and retraction on
climbing performance, showing an advantage for protracted limbs during the
stride. We then stacked optimal regions of performance and show that combining
optimal dynamic patterns with either foot angles or ROM configurations have the
greatest performance, but further optima stacking resulted in a decrease in
performance, suggesting complex interactions between kinematic parameters. The
search of optimal parameter configurations might not only be beneficial to
improve robotic in-field operations but may also further the study of the
locomotive evolution of climbing of animals, like lizards or insects.