264 research outputs found
Fast and Continuous Foothold Adaptation for Dynamic Locomotion through CNNs
Legged robots can outperform wheeled machines for most navigation tasks
across unknown and rough terrains. For such tasks, visual feedback is a
fundamental asset to provide robots with terrain-awareness. However, robust
dynamic locomotion on difficult terrains with real-time performance guarantees
remains a challenge. We present here a real-time, dynamic foothold adaptation
strategy based on visual feedback. Our method adjusts the landing position of
the feet in a fully reactive manner, using only on-board computers and sensors.
The correction is computed and executed continuously along the swing phase
trajectory of each leg. To efficiently adapt the landing position, we implement
a self-supervised foothold classifier based on a Convolutional Neural Network
(CNN). Our method results in an up to 200 times faster computation with respect
to the full-blown heuristics. Our goal is to react to visual stimuli from the
environment, bridging the gap between blind reactive locomotion and purely
vision-based planning strategies. We assess the performance of our method on
the dynamic quadruped robot HyQ, executing static and dynamic gaits (at speeds
up to 0.5 m/s) in both simulated and real scenarios; the benefit of safe
foothold adaptation is clearly demonstrated by the overall robot behavior.Comment: 9 pages, 11 figures. Accepted to RA-L + ICRA 2019, January 201
Legged locomotion over irregular terrains: State of the art of human and robot performance
Legged robotic technologies have moved out of the lab to operate in real environments, characterized by a wide variety of unpredictable irregularities and disturbances, all this in close proximity with humans. Demonstrating the ability of current robots to move robustly and reliably in these conditions is becoming essential to prove their safe operation. Here, we report an in-depth literature review aimed at verifying the existence of common or agreed protocols and metrics to test the performance of legged system in realistic environments. We primarily focused on three types of robotic technologies, i.e., hexapods, quadrupeds and bipeds. We also included a comprehensive overview on human locomotion studies, being it often considered the gold standard for performance, and one of the most important sources of bioinspiration for legged machines. We discovered that very few papers have rigorously studied robotic locomotion under irregular terrain conditions. On the contrary, numerous studies have addressed this problem on human gait, being nonetheless of highly heterogeneous nature in terms of experimental design. This lack of agreed methodology makes it challenging for the community to properly assess, compare and predict the performance of existing legged systems in real environments. On the one hand, this work provides a library of methods, metrics and experimental protocols, with a critical analysis on the limitations of the current approaches and future promising directions. On the other hand, it demonstrates the existence of an important lack of benchmarks in the literature, and the possibility of bridging different disciplines, e.g., the human and robotic, towards the definition of standardized procedure that will boost not only the scientific development of better bioinspired solutions, but also their market uptake
State Estimation for Hybrid Locomotion of Driving-Stepping Quadrupeds
Fast and versatile locomotion can be achieved with wheeled quadruped robots
that drive quickly on flat terrain, but are also able to overcome challenging
terrain by adapting their body pose and by making steps. In this paper, we
present a state estimation approach for four-legged robots with non-steerable
wheels that enables hybrid driving-stepping locomotion capabilities. We
formulate a Kalman Filter (KF) for state estimation that integrates driven
wheels into the filter equations and estimates the robot state (position and
velocity) as well as the contribution of driving with wheels to the above
state. Our estimation approach allows us to use the control framework of the
Mini Cheetah quadruped robot with minor modifications. We tested our approach
on this robot that we augmented with actively driven wheels in simulation and
in the real world. The experimental results are available at
https://www.ais.uni-bonn.de/%7Ehosseini/se-dsq .Comment: Accepted final version. IEEE International Robotic Computing (IRC),
Naples, Italy, December 202
Model Predictive Control With Environment Adaptation for Legged Locomotion
Re-planning in legged locomotion is crucial to track the desired user
velocity while adapting to the terrain and rejecting external disturbances. In
this work, we propose and test in experiments a real-time Nonlinear Model
Predictive Control (NMPC) tailored to a legged robot for achieving dynamic
locomotion on a variety of terrains. We introduce a mobility-based criterion to
define an NMPC cost that enhances the locomotion of quadruped robots while
maximizing leg mobility and improves adaptation to the terrain features. Our
NMPC is based on the real-time iteration scheme that allows us to re-plan
online at with a prediction horizon of seconds. We use
the single rigid body dynamic model defined in the center of mass frame in
order to increase the computational efficiency. In simulations, the NMPC is
tested to traverse a set of pallets of different sizes, to walk into a V-shaped
chimney,and to locomote over rough terrain. In real experiments, we demonstrate
the effectiveness of our NMPC with the mobility feature that allowed IIT's
quadruped robot HyQ to achieve an omni-directional walk on
flat terrain, to traverse a static pallet, and to adapt to a repositioned
pallet during a walk.Comment: Video available on: https://youtu.be/r0-KIiw0eW
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
Simplifying robotic locomotion by escaping traps via an active tail
Legged systems offer the ability to negotiate and climb heterogeneous terrains, more so than their wheeled counterparts \cite{freedberg_2012}. However, in certain complex environments, these systems are susceptible to failure conditions. These scenarios are caused by the interplay between the locomotor's kinematic state and the local terrain configuration, thus making them challenging to predict and overcome. These failures can cause catastrophic damage to the system and thus, methods to avoid such scenarios have been developed. These strategies typically take the form of environmental sensing or passive mechanical elements that adapt to the terrain. Such methods come at an increased control and mechanical design complexity for the system, often still being susceptible to imperceptible hazards. In this study, we investigated whether a tail could serve to offload this complexity by acting as a mechanism to generate new terradynamic interactions and mitigate failure via substrate contact. To do so, we developed a quadrupedal C-leg robophysical model (length and width = 27 cm, limb radius = 8 cm) capable of walking over rough terrain with an attachable actuated tail (length = 17 cm). We investigated three distinct tail strategies: static pose, periodic tapping, and load-triggered (power) tapping, while varying the angle of the tail relative to the body. We challenged the system to traverse a terrain (length = 160 cm, width = 80 cm) of randomized blocks (length and width = 10 cm, height = 0 to 12 cm) whose dimensions were scaled to the robot. Over this terrain, the robot exhibited trapping failures independent of gait pattern. Using the tail, the robot could free itself from trapping with a probability of 0 to 0.5, with the load-driven behaviors having comparable performance to low frequency periodic tapping across all tested tail angles. Along with increasing this likelihood of freeing, the robot displayed a longer survival distance over the rough terrain with these tail behaviors. In summary, we present the beginning of a framework that leverages mechanics via tail-ground interactions to offload limb control and design complexity to mitigate failure and improve legged system performance in heterogeneous environments.M.S
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