119 research outputs found
Analytic Model for Quadruped Locomotion Task-Space Planning
Despite the extensive presence of the legged locomotion in animals, it is
extremely challenging to be reproduced with robots. Legged locomotion is an
dynamic task which benefits from a planning that takes advantage of the
gravitational pull on the system. However, the computational cost of such
optimization rapidly increases with the complexity of kinematic structures,
rendering impossible real-time deployment in unstructured environments. This
paper proposes a simplified method that can generate desired centre of mass and
feet trajectory for quadrupeds. The model describes a quadruped as two bipeds
connected via their centres of mass, and it is based on the extension of an
algebraic bipedal model that uses the topology of the gravitational attractor
to describe bipedal locomotion strategies. The results show that the model
generates trajectories that agrees with previous studies. The model will be
deployed in the future as seed solution for whole-body trajectory optimization
in the attempt to reduce the computational cost and obtain real-time planning
of complex action in challenging environments.Comment: Accepted to be Published in 2019, 41th Annual International
Conference of the IEEE Engineering in Medicine and Biology Society (EMBC),
Berlin German
Motor Control Insights on Walking Planner and its Stability
The application of biomechanic and motor control models in the control of
bidedal robots (humanoids, and exoskeletons) has revealed limitations of our
understanding of human locomotion. A recently proposed model uses the potential
energy for bipedal structures to model the bipedal dynamics, and it allows to
predict the system dynamics from its kinematics. This work proposes a
task-space planner for human-like straight locomotion that target application
of in rehabilitation robotics and computational neuroscience. The proposed
architecture is based on the potential energy model and employs locomotor
strategies from human data as a reference for human behaviour. The model
generates Centre of Mass (CoM) trajectories, foot swing trajectories and the
Base of Support (BoS) over time. The data show that the proposed architecture
can generate behaviour in line with human walking strategies for both the CoM
and the foot swing. Despite the CoM vertical trajectory being not as smooth as
a human trajectory, yet the proposed model significantly reduces the error in
the estimation of the CoM vertical trajectory compared to the inverted pendulum
models. The proposed model is also able to asses the stability based on the
body kinematics embedding in currently used in the clinical practice. However,
the model also implies a shift in the interpretation of the spatiotemporal
parameters of the gait, which are now determined by the conditions for the
equilibrium and not \textit{vice versa}. In other words, locomotion is a
dynamic reaching where the motor primitives are also determined by gravity
Safe and Compliant Control of Redundant Robots Using Superimposition of Passive Task-Space Controllers
Safe and compliant control of dynamic systems in interaction with the
environment, e.g., in shared workspaces, continues to represent a major
challenge. Mismatches in the dynamic model of the robots, numerical
singularities, and the intrinsic environmental unpredictability are all
contributing factors. Online optimization of impedance controllers has recently
shown great promise in addressing this challenge, however, their performance is
not sufficiently robust to be deployed in challenging environments. This work
proposes a compliant control method for redundant manipulators based on a
superimposition of multiple passive task-space controllers in a hierarchy. Our
control framework of passive controllers is inherently stable, numerically
well-conditioned (as no matrix inversions are required), and computationally
inexpensive (as no optimization is used). We leverage and introduce a novel
stiffness profile for a recently proposed passive controller with smooth
transitions between the divergence and convergence phases making it
particularly suitable when multiple passive controllers are combined through
superimposition. Our experimental results demonstrate that the proposed method
achieves sub-centimeter tracking performance during demanding dynamic tasks
with fast-changing references, while remaining safe to interact with and robust
to singularities. he proposed framework achieves such results without knowledge
of the robot dynamics and thanks to its passivity is intrinsically stable. The
data further show that the robot can fully take advantage of the redundancy to
maintain the primary task accuracy while compensating for unknown environmental
interactions, which is not possible from current frameworks that require
accurate contact information
Optimisation of Body-ground Contact for Augmenting Whole-Body Loco-manipulation of Quadruped Robots
Legged robots have great potential to perform loco-manipulation tasks, yet it
is challenging to keep the robot balanced while it interacts with the
environment. In this paper we study the use of additional contact points for
maximising the robustness of loco-manipulation motions. Specifically,
body-ground contact is studied for enhancing robustness and manipulation
capabilities of quadrupedal robots. We propose to equip the robot with prongs:
small legs rigidly attached to the body which ensure body-ground contact occurs
in controllable point-contacts. The effect of these prongs on robustness is
quantified by computing the Smallest Unrejectable Force (SUF), a measure of
robustness related to Feasible Wrench Polytopes. We apply the SUF to assess the
robustness of the system, and propose an effective approximation of the SUF
that can be computed at near-real-time speed. We design a hierarchical
quadratic programming based whole-body controller that controls stable
interaction when the prongs are in contact with the ground. This novel concept
of using prongs and the resulting control framework are all implemented on
hardware to validate the effectiveness of the increased robustness and newly
enabled loco-manipulation tasks, such as obstacle clearance and manipulation of
a large object
Geometrical postural optimisation of 7-DoF limb-like manipulators
Robots are moving towards applications in less structured environments, but their model-based controllers are challenged by the tasks’ complexity and intrinsic environmental unpredictability. Studying biological motor control can provide insights into overcoming these limitations due to the high dexterity and stability observable in humans and animals. This work presents a geometrical solution to the postural optimisation of 7-DoF limbs-like mechanisms, which are robust to singularities and computationally efficient. The theoretical formulation identified two separate decoupled optimisation strategies. The shoulder and elbow strategy align the plane of motion with the expected plane of motion and guarantee the reachability of the end-posture. The wrist strategy ensures the end-effector orientation, which is essential to retain manipulability when nearing a singular configuration. The numerical results confirmed the theoretical observations and allowed us to identify the effect of different grasp strategies on system manipulability. The geometrical method was numerically tested in thousands of configurations proving to be both robust and accurate. The tested scenarios include left and right arm postures, singular configurations, and walking scenarios. The proposed geometrical approach can find application in developing efficient and robust interaction controllers that could be applied in computational neuroscience and robotics
Bio-mimetic Adaptive Force/Position Control Using Fractal Impedance
The ability of animals to interact with complex dynamics is unmatched in
robots. Especially important to the interaction performances is the online
adaptation of body dynamics, which can be modeled as an impedance behaviour.
However, the variable impedance controller still possesses a challenge in the
current control frameworks due to the difficulties of retaining stability when
adapting the controller gains. The fractal impedance controller has been
recently proposed to solve this issue. However, it still has limitations such
as sudden jumps in force when it starts to converge to the desired position and
the lack of a force feedback loop. In this manuscript, two improvements are
made to the control framework to solve these limitations. The force
discontinuity has been addressed introducing a modulation of the impedance via
a virtual antagonist that modulates the output force. The force tracking has
been modeled after the parallel force/position controller architecture. In
contrast to traditional methods, the fractal impedance controller enables the
implementation of a search algorithm on the force feedback to adapt its
behaviour on the external environment instead of on relying on \textit{a
priori} knowledge of the external dynamics. Preliminary simulation results
presented in this paper show the feasibility of the proposed approach, and it
allows to evaluate the trade-off that needs to be made when relying on the
proposed controller for interaction. In conclusion, the proposed method mimics
the behaviour of an agonist/antagonist system adapting to unknown external
dynamics, and it may find application in computational neuroscience, haptics,
and interaction control.Comment: \c{opyright} 2020 IEEE. Personal use of this material is permitted.
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