3,332 research outputs found
Extending The Lossy Spring-Loaded Inverted Pendulum Model with a Slider-Crank Mechanism
Spring Loaded Inverted Pendulum (SLIP) model has a long history in describing
running behavior in animals and humans as well as has been used as a design
basis for robots capable of dynamic locomotion. Anchoring the SLIP for lossy
physical systems resulted in newer models which are extended versions of
original SLIP with viscous damping in the leg. However, such lossy models
require an additional mechanism for pumping energy to the system to control the
locomotion and to reach a limit-cycle. Some studies solved this problem by
adding an actively controllable torque actuation at the hip joint and this
actuation has been successively used in many robotic platforms, such as the
popular RHex robot. However, hip torque actuation produces forces on the COM
dominantly at forward direction with respect to ground, making height control
challenging especially at slow speeds. The situation becomes more severe when
the horizontal speed of the robot reaches zero, i.e. steady hoping without
moving in horizontal direction, and the system reaches to singularity in which
vertical degrees of freedom is completely lost. To this end, we propose an
extension of the lossy SLIP model with a slider-crank mechanism, SLIP- SCM,
that can generate a stable limit-cycle when the body is constrained to vertical
direction. We propose an approximate analytical solution to the nonlinear
system dynamics of SLIP- SCM model to characterize its behavior during the
locomotion. Finally, we perform a fixed-point stability analysis on SLIP-SCM
model using our approximate analytical solution and show that proposed model
exhibits stable behavior in our range of interest.Comment: To appear in The 17th International Conference on Advanced Robotic
Multiple chaotic central pattern generators with learning for legged locomotion and malfunction compensation
An originally chaotic system can be controlled into various periodic
dynamics. When it is implemented into a legged robot's locomotion control as a
central pattern generator (CPG), sophisticated gait patterns arise so that the
robot can perform various walking behaviors. However, such a single chaotic CPG
controller has difficulties dealing with leg malfunction. Specifically, in the
scenarios presented here, its movement permanently deviates from the desired
trajectory. To address this problem, we extend the single chaotic CPG to
multiple CPGs with learning. The learning mechanism is based on a simulated
annealing algorithm. In a normal situation, the CPGs synchronize and their
dynamics are identical. With leg malfunction or disability, the CPGs lose
synchronization leading to independent dynamics. In this case, the learning
mechanism is applied to automatically adjust the remaining legs' oscillation
frequencies so that the robot adapts its locomotion to deal with the
malfunction. As a consequence, the trajectory produced by the multiple chaotic
CPGs resembles the original trajectory far better than the one produced by only
a single CPG. The performance of the system is evaluated first in a physical
simulation of a quadruped as well as a hexapod robot and finally in a real
six-legged walking machine called AMOSII. The experimental results presented
here reveal that using multiple CPGs with learning is an effective approach for
adaptive locomotion generation where, for instance, different body parts have
to perform independent movements for malfunction compensation.Comment: 48 pages, 16 figures, Information Sciences 201
Feedback MPC for Torque-Controlled Legged Robots
The computational power of mobile robots is currently insufficient to achieve
torque level whole-body Model Predictive Control (MPC) at the update rates
required for complex dynamic systems such as legged robots. This problem is
commonly circumvented by using a fast tracking controller to compensate for
model errors between updates. In this work, we show that the feedback policy
from a Differential Dynamic Programming (DDP) based MPC algorithm is a viable
alternative to bridge the gap between the low MPC update rate and the actuation
command rate. We propose to augment the DDP approach with a relaxed barrier
function to address inequality constraints arising from the friction cone. A
frequency-dependent cost function is used to reduce the sensitivity to
high-frequency model errors and actuator bandwidth limits. We demonstrate that
our approach can find stable locomotion policies for the torque-controlled
quadruped, ANYmal, both in simulation and on hardware.Comment: Paper accepted to IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS 2019
A Terradynamics of Legged Locomotion on Granular Media
The theories of aero- and hydrodynamics predict animal movement and device
design in air and water through the computation of lift, drag, and thrust
forces. Although models of terrestrial legged locomotion have focused on
interactions with solid ground, many animals move on substrates that flow in
response to intrusion. However, locomotor-ground interaction models on such
flowable ground are often unavailable. We developed a force model for
arbitrarily-shaped legs and bodies moving freely in granular media, and used
this "terradynamics" to predict a small legged robot's locomotion on granular
media using various leg shapes and stride frequencies. Our study reveals a
complex but generic dependence of stresses in granular media on intruder depth,
orientation, and movement direction and gives insight into the effects of leg
morphology and kinematics on movement
Beyond Basins of Attraction: Quantifying Robustness of Natural Dynamics
Properly designing a system to exhibit favorable natural dynamics can greatly
simplify designing or learning the control policy. However, it is still unclear
what constitutes favorable natural dynamics and how to quantify its effect.
Most studies of simple walking and running models have focused on the basins of
attraction of passive limit-cycles and the notion of self-stability. We instead
emphasize the importance of stepping beyond basins of attraction. We show an
approach based on viability theory to quantify robust sets in state-action
space. These sets are valid for the family of all robust control policies,
which allows us to quantify the robustness inherent to the natural dynamics
before designing the control policy or specifying a control objective. We
illustrate our formulation using spring-mass models, simple low dimensional
models of running systems. We then show an example application by optimizing
robustness of a simulated planar monoped, using a gradient-free optimization
scheme. Both case studies result in a nonlinear effective stiffness providing
more robustness.Comment: 15 pages. This work has been accepted to IEEE Transactions on
Robotics (2019
The implications of embodiment for behavior and cognition: animal and robotic case studies
In this paper, we will argue that if we want to understand the function of
the brain (or the control in the case of robots), we must understand how the
brain is embedded into the physical system, and how the organism interacts with
the real world. While embodiment has often been used in its trivial meaning,
i.e. 'intelligence requires a body', the concept has deeper and more important
implications, concerned with the relation between physical and information
(neural, control) processes. A number of case studies are presented to
illustrate the concept. These involve animals and robots and are concentrated
around locomotion, grasping, and visual perception. A theoretical scheme that
can be used to embed the diverse case studies will be presented. Finally, we
will establish a link between the low-level sensory-motor processes and
cognition. We will present an embodied view on categorization, and propose the
concepts of 'body schema' and 'forward models' as a natural extension of the
embodied approach toward first representations.Comment: Book chapter in W. Tschacher & C. Bergomi, ed., 'The Implications of
Embodiment: Cognition and Communication', Exeter: Imprint Academic, pp. 31-5
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