681 research outputs found
A Pendulum-Driven Legless Rolling Jumping Robot
In this paper, we present a novel rolling, jumping robot. The robot consists
of a driven pendulum mounted to a wheel in a compact, lightweight, 3D printed
design. We show that by driving the pendulum to shift the robot's weight
distribution, the robot is able to obtain significant rolling speed, achieve
jumps of up to 2.5 body lengths vertically, and clear horizontal distances of
over 6 body lengths. The robot's dynamic model is derived and simulation
results indicate that it is consistent with the rolling motion and jumping
observed on the robot. The ability to both roll and jump effectively using a
minimalistic design makes this robot unique and could inspire the use of
similar mechanisms on robots intended for applications in which agile
locomotion on unstructured terrain is necessary, such as disaster response or
planetary exploration.Comment: Final version of paper in IROS 2023. View the supplemental video at
https://youtu.be/9hKQilCpea
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
Task-Driven Estimation and Control via Information Bottlenecks
Our goal is to develop a principled and general algorithmic framework for
task-driven estimation and control for robotic systems. State-of-the-art
approaches for controlling robotic systems typically rely heavily on accurately
estimating the full state of the robot (e.g., a running robot might estimate
joint angles and velocities, torso state, and position relative to a goal).
However, full state representations are often excessively rich for the specific
task at hand and can lead to significant computational inefficiency and
brittleness to errors in state estimation. In contrast, we present an approach
that eschews such rich representations and seeks to create task-driven
representations. The key technical insight is to leverage the theory of
information bottlenecks}to formalize the notion of a "task-driven
representation" in terms of information theoretic quantities that measure the
minimality of a representation. We propose novel iterative algorithms for
automatically synthesizing (offline) a task-driven representation (given in
terms of a set of task-relevant variables (TRVs)) and a performant control
policy that is a function of the TRVs. We present online algorithms for
estimating the TRVs in order to apply the control policy. We demonstrate that
our approach results in significant robustness to unmodeled measurement
uncertainty both theoretically and via thorough simulation experiments
including a spring-loaded inverted pendulum running to a goal location.Comment: 9 pages, 4 figures, abridged version accepted to ICRA2019;
Incorporates changes in final conference submissio
Terrestrial Locomotion of PogoX: From Hardware Design to Energy Shaping and Step-to-step Dynamics Based Control
We present a novel controller design on a robotic locomotor that combines an
aerial vehicle with a spring-loaded leg. The main motivation is to enable the
terrestrial locomotion capability on aerial vehicles so that they can carry
heavy loads: heavy enough that flying is no longer possible, e.g., when the
thrust-to-weight ratio (TWR) is small. The robot is designed with a pogo-stick
leg and a quadrotor, and thus it is named as PogoX. We show that with a simple
and lightweight spring-loaded leg, the robot is capable of hopping with TWR
. The control of hopping is realized via two components: a vertical height
control via control Lyapunov function-based energy shaping, and a step-to-step
(S2S) dynamics based horizontal velocity control that is inspired by the
hopping of the Spring-Loaded Inverted Pendulum (SLIP). The controller is
successfully realized on the physical robot, showing dynamic terrestrial
locomotion of PogoX which can hop at variable heights and different horizontal
velocities with robustness to ground height variations and external pushes.Comment: 7 pages, 7 figure
Motion Planning and Control of Dynamic Humanoid Locomotion
Inspired by human, humanoid robots has the potential to become a general-purpose platform that lives along with human. Due to the technological advances in many field, such as actuation, sensing, control and intelligence, it finally enables humanoid robots to possess human comparable capabilities. However, humanoid locomotion is still a challenging research field. The large number of degree of freedom structure makes the system difficult to coordinate online. The presence of various contact constraints and the hybrid nature of locomotion tasks make the planning a harder problem to solve. Template model anchoring approach has been adopted to bridge the gap between simple model behavior and the whole-body motion of humanoid robot.
Control policies are first developed for simple template models like Linear Inverted Pendulum Model (LIPM) or Spring Loaded Inverted Pendulum(SLIP), the result controlled behaviors are then been mapped to the whole-body motion of humanoid robot through optimization-based task-space control strategies. Whole-body humanoid control framework has been verified on various contact situations such as unknown uneven terrain, multi-contact scenarios and moving platform and shows its generality and versatility. For walking motion, existing Model Predictive Control approach based on LIPM has been extended to enable the robot to walk without any reference foot placement anchoring. It is kind of discrete version of \u201cwalking without thinking\u201d.
As a result, the robot could achieve versatile locomotion modes such as automatic foot placement with single reference velocity command, reactive stepping under large external disturbances, guided walking with small constant external pushing forces, robust walking on unknown uneven terrain, reactive stepping in place when blocked by external barrier. As an extension of this proposed framework, also to increase the push recovery capability of the humanoid robot, two new configurations have been proposed to enable the robot to perform cross-step motions. For more dynamic hopping and running motion, SLIP model has been chosen as the template model. Different from traditional model-based analytical approach, a data-driven approach has been proposed to encode the dynamics of the this model. A deep neural network is trained offline with a large amount of simulation data based on the SLIP model to learn its dynamics.
The trained network is applied online to generate reference foot placements for the humanoid robot. Simulations have been performed to evaluate the effectiveness of the proposed approach in generating bio-inspired and robust running motions. The method proposed based on 2D SLIP model can be generalized to 3D SLIP model and the extension has been briefly mentioned at the end
Effective Viscous Damping Enables Morphological Computation in Legged Locomotion
Muscle models and animal observations suggest that physical damping is
beneficial for stabilization. Still, only a few implementations of mechanical
damping exist in compliant robotic legged locomotion. It remains unclear how
physical damping can be exploited for locomotion tasks, while its advantages as
sensor-free, adaptive force- and negative work-producing actuators are
promising. In a simplified numerical leg model, we studied the energy
dissipation from viscous and Coulomb damping during vertical drops with
ground-level perturbations. A parallel spring-damper is engaged between
touch-down and mid-stance, and its damper auto-disengages during mid-stance and
takeoff. Our simulations indicate that an adjustable and viscous damper is
desired. In hardware we explored effective viscous damping and adjustability
and quantified the dissipated energy. We tested two mechanical, leg-mounted
damping mechanisms; a commercial hydraulic damper, and a custom-made pneumatic
damper. The pneumatic damper exploits a rolling diaphragm with an adjustable
orifice, minimizing Coulomb damping effects while permitting adjustable
resistance. Experimental results show that the leg-mounted, hydraulic damper
exhibits the most effective viscous damping. Adjusting the orifice setting did
not result in substantial changes of dissipated energy per drop, unlike
adjusting damping parameters in the numerical model. Consequently, we also
emphasize the importance of characterizing physical dampers during real legged
impacts to evaluate their effectiveness for compliant legged locomotion
- …