1,159 research outputs found
Dynamic Walking of Bipedal Robots on Uneven Stepping Stones via Adaptive-frequency MPC
This paper presents a novel Adaptive-frequency MPC framework for bipedal
locomotion over terrain with uneven stepping stones. In detail, we intend to
achieve adaptive foot placement and gait period for bipedal periodic walking
gait with this MPC, in order to traverse terrain with discontinuities without
slowing down. We pair this adaptive-frequency MPC with a kino-dynamics
trajectory optimization for optimal gait periods, center of mass (CoM)
trajectory, and foot placements. We use whole-body control (WBC) along with
adaptive-frequency MPC to track the optimal trajectories from the offline
optimization. In numerical validations, our adaptive-frequency MPC framework
with optimization has shown advantages over fixed-frequency MPC. The proposed
framework can control the bipedal robot to traverse through uneven stepping
stone terrains with perturbed stone heights, widths, and surface shapes while
maintaining an average speed of 1.5 m/s.Comment: 6 pages, 7 figures, 1 tabl
Neuro-mechanical entrainment in a bipedal robotic walking platform
In this study, we investigated the use of van der Pol oscillators in a 4-dof embodied bipedal robotic platform for the purposes of planar walking. The oscillator controlled the hip and knee joints of the robot and was capable of generating waveforms with the correct frequency and phase so as to entrain with the mechanical system. Lowering its oscillation frequency resulted in an increase to the walking pace, indicating exploitation of the global natural dynamics. This is verified by its operation in absence of entrainment, where faster limb motion results in a slower overall walking pace
Neuro-mechanical entrainment in a bipedal robotic walking platform
In this study, we investigated the use of van der Pol oscillators in a 4-dof embodied bipedal robotic platform for the purposes of planar walking. The oscillator controlled the hip and knee joints of the robot and was capable of generating waveforms with the correct frequency and phase so as to entrain with the mechanical system. Lowering its oscillation frequency resulted in an increase to the walking pace, indicating exploitation of the global natural dynamics. This is verified by its operation in absence of entrainment, where faster limb motion results in a slower overall walking pace
Push recovery with stepping strategy based on time-projection control
In this paper, we present a simple control framework for on-line push
recovery with dynamic stepping properties. Due to relatively heavy legs in our
robot, we need to take swing dynamics into account and thus use a linear model
called 3LP which is composed of three pendulums to simulate swing and torso
dynamics. Based on 3LP equations, we formulate discrete LQR controllers and use
a particular time-projection method to adjust the next footstep location
on-line during the motion continuously. This adjustment, which is found based
on both pelvis and swing foot tracking errors, naturally takes the swing
dynamics into account. Suggested adjustments are added to the Cartesian 3LP
gaits and converted to joint-space trajectories through inverse kinematics.
Fixed and adaptive foot lift strategies also ensure enough ground clearance in
perturbed walking conditions. The proposed structure is robust, yet uses very
simple state estimation and basic position tracking. We rely on the physical
series elastic actuators to absorb impacts while introducing simple laws to
compensate their tracking bias. Extensive experiments demonstrate the
functionality of different control blocks and prove the effectiveness of
time-projection in extreme push recovery scenarios. We also show self-produced
and emergent walking gaits when the robot is subject to continuous dragging
forces. These gaits feature dynamic walking robustness due to relatively soft
springs in the ankles and avoiding any Zero Moment Point (ZMP) control in our
proposed architecture.Comment: 20 pages journal pape
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
Hybrid disturbance rejection control of dynamic bipedal robots
This paper presents a disturbance rejection control strategy for hybrid dynamic systems exposed to model uncertainties and external disturbances. The focus of this work is the gait control of dynamic bipedal robots. The proposed control strategy integrates continuous and discrete control actions. The continuous control action uses a novel model-based active disturbance rejection control (ADRC) approach to track gait trajectory references. The discrete control action resets the gait trajectory references after the impact produced by the robot’s support-leg exchange to maintain a zero tracking error. A Poincaré return map is used to search asymptotic stable periodic orbits in an extended hybrid zero dynamics (EHZD). The EHZD reflects a lower-dimensional representation of the full hybrid dynamics with uncertainties and disturbances. A physical bipedal robot testbed, referred to as Saurian, is fabricated for validation purposes. Numerical simulation and physical experiments show the robustness of the proposed control strategy against external disturbances and model uncertainties that affect both the swing motion phase and the support-leg exchange
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