17 research outputs found
Body Lift and Drag for a Legged Millirobot in Compliant Beam Environment
Much current study of legged locomotion has rightly focused on foot traction
forces, including on granular media. Future legged millirobots will need to go
through terrain, such as brush or other vegetation, where the body contact
forces significantly affect locomotion. In this work, a (previously developed)
low-cost 6-axis force/torque sensing shell is used to measure the interaction
forces between a hexapedal millirobot and a set of compliant beams, which act
as a surrogate for a densely cluttered environment. Experiments with a
VelociRoACH robotic platform are used to measure lift and drag forces on the
tactile shell, where negative lift forces can increase traction, even while
drag forces increase. The drag energy and specific resistance required to pass
through dense terrains can be measured. Furthermore, some contact between the
robot and the compliant beams can lower specific resistance of locomotion. For
small, light-weight legged robots in the beam environment, the body motion
depends on both leg-ground and body-beam forces. A shell-shape which reduces
drag but increases negative lift, such as the half-ellipsoid used, is suggested
to be advantageous for robot locomotion in this type of environment.Comment: First three authors contributed equally. Accepted to ICRA 201
Learning Image-Conditioned Dynamics Models for Control of Under-actuated Legged Millirobots
Millirobots are a promising robotic platform for many applications due to
their small size and low manufacturing costs. Legged millirobots, in
particular, can provide increased mobility in complex environments and improved
scaling of obstacles. However, controlling these small, highly dynamic, and
underactuated legged systems is difficult. Hand-engineered controllers can
sometimes control these legged millirobots, but they have difficulties with
dynamic maneuvers and complex terrains. We present an approach for controlling
a real-world legged millirobot that is based on learned neural network models.
Using less than 17 minutes of data, our method can learn a predictive model of
the robot's dynamics that can enable effective gaits to be synthesized on the
fly for following user-specified waypoints on a given terrain. Furthermore, by
leveraging expressive, high-capacity neural network models, our approach allows
for these predictions to be directly conditioned on camera images, endowing the
robot with the ability to predict how different terrains might affect its
dynamics. This enables sample-efficient and effective learning for locomotion
of a dynamic legged millirobot on various terrains, including gravel, turf,
carpet, and styrofoam. Experiment videos can be found at
https://sites.google.com/view/imageconddy
Fast Untethered Soft Robotic Crawler with Elastic Instability
High-speed locomotion of animals gives them tremendous advantages in
exploring, hunting, and escaping from predators in varying environments.
Enlightened by the fast-running gait of mammals like cheetahs and wolves, we
designed and fabricated a single-servo-driving untethered soft robot that is
capable of galloping at a speed of 313 mm/s or 1.56 body length per second
(BL/s), 5.2 times and 2.6 times faster than the reported fastest predecessors
in mm/s and BL/s, respectively, in literature. An in-plane prestressed hair
clip mechanism (HCM) made up of semi-rigid materials like plastic is used as
the supporting chassis, the compliant spine, and the muscle force amplifier of
the robot at the same time, enabling the robot to be rapid and strong. The
influence of factors including actuation frequency, substrates,
tethering/untethering, and symmetric/asymmetric actuation is explored with
experiments. Based on previous work, this paper further demonstrated the
potential of HCM in addressing the speed problem of soft robots
Detection of Slippery Terrain with a Heterogeneous Team of Legged Robots
Legged robots come in a range of sizes and capabilities. By combining these robots into heterogeneous teams, joint locomotion and perception tasks can be achieved by utilizing the diversified features of each robot. In this work we present a framework for using a heterogeneous team of legged robots to detect slippery terrain. StarlETH, a large and highly capable quadruped uses the VelociRoACH as a novel remote probe to detect regions of slippery terrain. StarlETH localizes the team using internal state estimation. To classify slippage of the VelociRoACH, we develop several Support Vector Machines (SVM) based on data from both StarlETH and VelociRoACH. By combining the team’s information about the motion of VelociRoACH, a classifier was built which could detect slippery spots with 92% (125/135) accuracy using only four features
Expressivity in Natural and Artificial Systems
Roboticists are trying to replicate animal behavior in artificial systems.
Yet, quantitative bounds on capacity of a moving platform (natural or
artificial) to express information in the environment are not known. This paper
presents a measure for the capacity of motion complexity -- the expressivity --
of articulated platforms (both natural and artificial) and shows that this
measure is stagnant and unexpectedly limited in extant robotic systems. This
analysis indicates trends in increasing capacity in both internal and external
complexity for natural systems while artificial, robotic systems have increased
significantly in the capacity of computational (internal) states but remained
more or less constant in mechanical (external) state capacity. This work
presents a way to analyze trends in animal behavior and shows that robots are
not capable of the same multi-faceted behavior in rich, dynamic environments as
natural systems.Comment: Rejected from Nature, after review and appeal, July 4, 2018
(submitted May 11, 2018
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Design, Manufacturing, and Locomotion Studies of Ambulatory Micro-Robots
Biological research over the past several decades has elucidated some of the mechanisms behind highly mobile, efficient, and robust locomotion in insects such as the cockroach. Roboticists have used this information to create biologically-inspired machines capable of running, jumping, and climbing robustly over a variety of terrains. To date, little work has been done to develop an at-scale insect-inspired robot capable of similar feats, due to limitations in fabrication, actuation, and electronics integration at small scales. This thesis addresses these challenges, focusing on the mechanical design and fabrication of a sub-2g walking robot, the Harvard Ambulatory MicroRobot (HAMR). The development of HAMR includes modeling and parameter selection for a two degree of freedom leg powertrain that enables locomotion. In addition, a design inspired by pop-up books that enables fast and repeatable assembly of the miniature walking robot is presented. Finally, a method to drive HAMR resulting in speeds up to 37cm/s is presented, along with simple control schemes.Engineering and Applied Science