Leaping Lizards and Self-righting Robots
Combining the efforts of students and teachers in multiple disciplines can bring about advantageous consequences.
The separation between biology and engineering has been ignored in an interdisciplinary research project carried out at UC Berkeley. Led by Professor Robert J. Full, a team of biologists and engineers including students, both undergraduate and graduate, worked together to harmonise the approaches of both academic fields. They were investigating how a lizard utilised its tail to ensure success when jumping. Their goal was to use information from this project, and their differing perspectives on this problem, to inform future work into robotic motion.
This is not the first project in which Professor Full has connected the natural world and opportunities for engineering solutions. He has already looked into how the microscopic hairs of a gecko's foot enable its almost supernatural ability to climb smooth surfaces. More recently, he has investigated the role of a lizard's tail in the self-righting motion seen as it falls or slips.
The motion of an Agama lizard was captured and analysed using high-speed cameras and motion capture software. The experiment was done numerous times on surfaces with various levels of friction designed to create problems for the jumping reptile. From this, the team formulated a mathematical model that could simulate the motion and created a device to test the model. The team created 'Tailbot': a simple radio-controlled car with a tail, a gyroscope and an innovative 'inertial-assisted' feedback mechanism.
As shown in the video that accompanies this article, the model transformed the motion of the Tailbot. Without the benefit of the tail reacting to the form of the jump, the Tailbot would land nose-down, but with the model controlling the motion of the tail, a safer, nose-up landing was seen.
This work was intended to have practical uses and the implications for robotics are not insignificant. Maintaining balance and coping with difficult surfaces are big challenges for engineers working on autonomous robotic systems. Animals have had millions of years of evolution to overcome these hurdles and using the tried and tested methods that have resulted is an intelligent shortcut for researchers in this field.
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