112 research outputs found
Planning and Control Strategies for Motion and Interaction of the Humanoid Robot COMAN+
Despite the majority of robotic platforms are still confined in controlled environments such as factories, thanks to the ever-increasing level of autonomy and the progress on human-robot interaction, robots are starting to be employed for different operations, expanding their focus from uniquely industrial to more diversified scenarios.
Humanoid research seeks to obtain the versatility and dexterity of robots capable of mimicking human motion in any environment. With the aim of operating side-to-side with humans, they should be able to carry out complex tasks without posing a threat during operations.
In this regard, locomotion, physical interaction with the environment and safety are three essential skills to develop for a biped.
Concerning the higher behavioural level of a humanoid, this thesis addresses both ad-hoc movements generated for specific physical interaction tasks and cyclic movements for locomotion. While belonging to the same category and sharing some of the theoretical obstacles, these actions require different approaches: a general high-level task is composed of specific movements that depend on the environment and the nature of the task itself, while regular locomotion involves the generation of periodic trajectories of the limbs.
Separate planning and control architectures targeting these aspects of biped motion are designed and developed both from a theoretical and a practical standpoint, demonstrating their efficacy on the new humanoid robot COMAN+, built at Istituto Italiano di Tecnologia.
The problem of interaction has been tackled by mimicking the intrinsic elasticity of human muscles, integrating active compliant controllers. However, while state-of-the-art robots may be endowed with compliant architectures, not many can withstand potential system failures that could compromise the safety of a human interacting with the robot. This thesis proposes an implementation of such low-level controller that guarantees a fail-safe behaviour, removing the threat that a humanoid robot could pose if a system failure occurred
ExoRecovery: Push Recovery with a Lower-Limb Exoskeleton based on Stepping Strategy
Balance loss is a significant challenge in lower-limb exoskeleton
applications, as it can lead to potential falls, thereby impacting user safety
and confidence. We introduce a control framework for omnidirectional recovery
step planning by online optimization of step duration and position in response
to external forces. We map the step duration and position to a human-like foot
trajectory, which is then translated into joint trajectories using inverse
kinematics. These trajectories are executed via an impedance controller,
promoting cooperation between the exoskeleton and the user.
Moreover, our framework is based on the concept of the divergent component of
motion, also known as the Extrapolated Center of Mass, which has been
established as a consistent dynamic for describing human movement. This
real-time online optimization framework enhances the adaptability of
exoskeleton users under unforeseen forces thereby improving the overall user
stability and safety. To validate the effectiveness of our approach,
simulations, and experiments were conducted. Our push recovery experiments
employing the exoskeleton in zero-torque mode (without assistance) exhibit an
alignment with the exoskeleton's recovery assistance mode, that shows the
consistency of the control framework with human intention. To the best of our
knowledge, this is the first cooperative push recovery framework for the
lower-limb human exoskeleton that relies on the simultaneous adaptation of
intra-stride parameters in both frontal and sagittal directions. The proposed
control scheme has been validated with human subject experiments.Comment: Submitted for a conference. 8 pages including references, 8 figure
Multi-expert learning of adaptive legged locomotion
Achieving versatile robot locomotion requires motor skills which can adapt to
previously unseen situations. We propose a Multi-Expert Learning Architecture
(MELA) that learns to generate adaptive skills from a group of representative
expert skills. During training, MELA is first initialised by a distinct set of
pre-trained experts, each in a separate deep neural network (DNN). Then by
learning the combination of these DNNs using a Gating Neural Network (GNN),
MELA can acquire more specialised experts and transitional skills across
various locomotion modes. During runtime, MELA constantly blends multiple DNNs
and dynamically synthesises a new DNN to produce adaptive behaviours in
response to changing situations. This approach leverages the advantages of
trained expert skills and the fast online synthesis of adaptive policies to
generate responsive motor skills during the changing tasks. Using a unified
MELA framework, we demonstrated successful multi-skill locomotion on a real
quadruped robot that performed coherent trotting, steering, and fall recovery
autonomously, and showed the merit of multi-expert learning generating
behaviours which can adapt to unseen scenarios
Real-time pursuit-evasion with humanoid robots
We consider a pursuit-evasion problem between humanoids. In our scenario, the pursuer enters the safety area of the evader headed for collision, while the latter executes a fast evasive motion. Control schemes are designed for both the pursuer and the evader. They are structurally identical, although the objectives are different: the pursuer tries to align its direction of motion with the line-of-sight to the evader, whereas the evader tries to move in a direction orthogonal to the line-of-sight to the pursuer. At the core of the control scheme is a maneuver planning module which makes use of closed- form expressions exclusively. This allows its use in a replanning framework, where each robot updates its motion plan upon completion of a step to account for the perceived motion of the other. Simulation and experimental results on NAO humanoids reveal an interesting asymptotic behavior which was predicted using unicycle as template models for trajectory generation
Reachability Aware Capture Regions with Time Adjustment and Cross-Over for Step Recovery
For humanoid robots to live up to their potential utility, they must be able
to robustly recover from instabilities. In this work, we propose a number of
balance enhancements to enable the robot to both achieve specific, desired
footholds in the world and adjusting the step positions and times as necessary
while leveraging ankle and hip. This includes improving the calculation of
capture regions for bipedal locomotion to better consider how step constraints
affect the ability to recover. We then explore a new strategy for performing
cross-over steps to maintain stability, which greatly enhances the variety of
tracking error from which the robot may recover. Our last contribution is a
strategy for time adaptation during the transfer phase for recovery. We then
present these results on our humanoid robot, Nadia, in both simulation and
hardware, showing the robot walking over rough terrain, recovering from
external disturbances, and taking cross-over steps to maintain balance.Comment: Submitted to 2023 IEEE International Conference on Humanoid Robotics
(Humanoids
From walking to running: robust and 3D humanoid gait generation via MPC
Humanoid robots are platforms that can succeed in tasks conceived for humans. From locomotion in unstructured environments, to driving cars, or working in industrial plants,
these robots have a potential that is yet to be disclosed in systematic every-day-life applications. Such a perspective, however, is opposed by the need of solving complex
engineering problems under the hardware and software point of view. In this thesis, we focus on the software side of the problem, and in particular on locomotion control. The operativity of a legged humanoid is subordinate to its capability of realizing a reliable locomotion. In many settings, perturbations may undermine the balance and make the robot fall. Moreover, complex and dynamic motions might be required by the context, as for instance it could be needed to start running or climbing stairs to achieve a certain location in the shortest time. We present gait generation schemes based on Model Predictive Control (MPC) that tackle both the problem of robustness and tridimensional dynamic motions. The proposed control schemes adopt the typical paradigm of centroidal MPC for reference motion generation, enforcing dynamic balance through the Zero Moment Point condition, plus a whole-body controller that maps the generated trajectories to joint commands. Each of the described predictive controllers also feature a so-called stability constraint, preventing the generation of diverging Center of Mass trajectories with respect to the Zero Moment Point. Robustness is addressed by modeling the humanoid as a Linear Inverted Pendulum and devising two types of strategies. For persistent perturbations, a way to use a disturbance observer and a technique for constraint tightening (to ensure robust constraint satisfaction) are presented. In the case of impulsive pushes instead, techniques for footstep and timing adaptation are introduced. The underlying approach is to interpret robustness as a MPC feasibility problem, thus aiming at ensuring the existence of a solution for the constrained optimization problem to be solved at each iteration in spite of the perturbations. This perspective allows to devise simple solutions to complex problems, favoring a reliable real-time implementation.
For the tridimensional locomotion, on the other hand, the humanoid is modeled as a Variable Height Inverted Pendulum. Based on it, a two stage MPC is introduced with particular emphasis on the implementation of the stability constraint. The overall result is a gait generation scheme that allows the robot to overcome relatively complex
environments constituted by a non-flat terrain, with also the capability of realizing running gaits. The proposed methods are validated in different settings: from conceptual simulations in Matlab to validations in the DART dynamic environment, up to experimental tests on the NAO and the OP3 platforms
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