Agile quadrupedal locomotion in animals and robots is yet to be fully understood, quantified
or achieved. An intuitive notion of agility exists, but neither a concise definition nor a common
benchmark can be found. Further, it is unclear, what minimal level of mechatronic complexity
is needed for this particular aspect of locomotion.
In this thesis we address and partially answer two primary questions: (Q1) What is agile
legged locomotion (agility) and how can wemeasure it? (Q2) How can wemake agile legged
locomotion with a robot a reality?
To answer our first question, we define agility for robot and animal alike, building a common
ground for this particular component of locomotion and introduce quantitative measures
to enhance robot evaluation and comparison. The definition is based on and inspired by
features of agility observed in nature, sports, and suggested in robotics related publications.
Using the results of this observational and literature review, we build a novel and extendable
benchmark of thirteen different tasks that implement our vision of quantitatively classifying
agility. All scores are calculated from simple measures, such as time, distance, angles and
characteristic geometric values for robot scaling. We normalize all unit-less scores to reach
comparability between different systems. An initial implementation with available robots and
real agility-dogs as baseline finalize our effort of answering the first question.
Bio-inspired designs introducing and benefiting from morphological aspects present in nature
allowed the generation of fast, robust and energy efficient locomotion. We use engineering
tools and interdisciplinary knowledge transferred from biology to build low-cost robots able
to achieve a certain level of agility and as a result of this addressing our second question. This
iterative process led to a series of robots from Lynx over Cheetah-Cub-S, Cheetah-Cub-AL,
and Oncilla to Serval, a compliant robot with actuated spine, high range of motion in all joints.
Serval presents a high level of mobility at medium speeds. With many successfully implemented
skills, using a basic kinematics-duplication from dogs (copying the foot-trajectories
of real animals and replaying themotion on the robot using a mathematical interpretation),
we found strengths to emphasize, weaknesses to correct and made Serval ready for future
attempts to achieve even more agile locomotion. We calculated Servalâ s agility scores with the
result of it performing better than any of its predecessors. Our small, safe and low-cost robot
is able to execute up to 6 agility tasks out of 13 with the potential to reachmore after extended
development. Concluding, we like to mention that Serval is able to cope with step-downs,
smooth, bumpy terrain and falling orthogonally to the ground.