10,113 research outputs found
SPRK: A Low-Cost Stewart Platform For Motion Study In Surgical Robotics
To simulate body organ motion due to breathing, heart beats, or peristaltic
movements, we designed a low-cost, miniaturized SPRK (Stewart Platform Research
Kit) to translate and rotate phantom tissue. This platform is 20cm x 20cm x
10cm to fit in the workspace of a da Vinci Research Kit (DVRK) surgical robot
and costs $250, two orders of magnitude less than a commercial Stewart
platform. The platform has a range of motion of +/- 1.27 cm in translation
along x, y, and z directions and has motion modes for sinusoidal motion and
breathing-inspired motion. Modular platform mounts were also designed for
pattern cutting and debridement experiments. The platform's positional
controller has a time-constant of 0.2 seconds and the root-mean-square error is
1.22 mm, 1.07 mm, and 0.20 mm in x, y, and z directions respectively. All the
details, CAD models, and control software for the platform is available at
github.com/BerkeleyAutomation/sprk
Methods to improve the coping capacities of whole-body controllers for humanoid robots
Current applications for humanoid robotics require autonomy in an environment specifically
adapted to humans, and safe coexistence with people. Whole-body control is
promising in this sense, having shown to successfully achieve locomotion and manipulation
tasks. However, robustness remains an issue: whole-body controllers can still
hardly cope with unexpected disturbances, with changes in working conditions, or
with performing a variety of tasks, without human intervention. In this thesis, we
explore how whole-body control approaches can be designed to address these issues.
Based on whole-body control, contributions have been developed along three main
axes: joint limit avoidance, automatic parameter tuning, and generalizing whole-body
motions achieved by a controller. We first establish a whole-body torque-controller
for the iCub, based on the stack-of-tasks approach and proposed feedback control
laws in SE(3). From there, we develop a novel, theoretically guaranteed joint limit
avoidance technique for torque-control, through a parametrization of the feasible joint
space. This technique allows the robot to remain compliant, while resisting external
perturbations that push joints closer to their limits, as demonstrated with experiments
in simulation and with the real robot. Then, we focus on the issue of automatically
tuning parameters of the controller, in order to improve its behavior across different
situations. We show that our approach for learning task priorities, combining domain
randomization and carefully selected fitness functions, allows the successful transfer of
results between platforms subjected to different working conditions. Following these
results, we then propose a controller which allows for generic, complex whole-body
motions through real-time teleoperation. This approach is notably verified on the robot
to follow generic movements of the teleoperator while in double support, as well as to
follow the teleoperator\u2019s upper-body movements while walking with footsteps adapted
from the teleoperator\u2019s footsteps. The approaches proposed in this thesis therefore
improve the capability of whole-body controllers to cope with external disturbances,
different working conditions and generic whole-body motions
Human-in-the-Loop Methods for Data-Driven and Reinforcement Learning Systems
Recent successes combine reinforcement learning algorithms and deep neural
networks, despite reinforcement learning not being widely applied to robotics
and real world scenarios. This can be attributed to the fact that current
state-of-the-art, end-to-end reinforcement learning approaches still require
thousands or millions of data samples to converge to a satisfactory policy and
are subject to catastrophic failures during training. Conversely, in real world
scenarios and after just a few data samples, humans are able to either provide
demonstrations of the task, intervene to prevent catastrophic actions, or
simply evaluate if the policy is performing correctly. This research
investigates how to integrate these human interaction modalities to the
reinforcement learning loop, increasing sample efficiency and enabling
real-time reinforcement learning in robotics and real world scenarios. This
novel theoretical foundation is called Cycle-of-Learning, a reference to how
different human interaction modalities, namely, task demonstration,
intervention, and evaluation, are cycled and combined to reinforcement learning
algorithms. Results presented in this work show that the reward signal that is
learned based upon human interaction accelerates the rate of learning of
reinforcement learning algorithms and that learning from a combination of human
demonstrations and interventions is faster and more sample efficient when
compared to traditional supervised learning algorithms. Finally,
Cycle-of-Learning develops an effective transition between policies learned
using human demonstrations and interventions to reinforcement learning. The
theoretical foundation developed by this research opens new research paths to
human-agent teaming scenarios where autonomous agents are able to learn from
human teammates and adapt to mission performance metrics in real-time and in
real world scenarios.Comment: PhD thesis, Aerospace Engineering, Texas A&M (2020). For more
information, see https://vggoecks.com
Modeling, Control and Estimation of Reconfigurable Cable Driven Parallel Robots
The motivation for this thesis was to develop a cable-driven parallel robot (CDPR) as part of a two-part robotic device for concrete 3D printing. This research addresses specific research questions in this domain, chiefly, to present advantages offered by the addition of kinematic redundancies to CDPRs. Due to the natural actuation redundancy present in a fully constrained CDPR, the addition of internal mobility offers complex challenges in modeling and control that are not often encountered in literature.
This work presents a systematic analysis of modeling such kinematic redundancies through the application of reciprocal screw theory (RST) and Lie algebra while further introducing specific challenges and drawbacks presented by cable driven actuators. It further re-contextualizes well-known performance indices such as manipulability, wrench closure quality, and the available wrench set for application with reconfigurable CDPRs.
The existence of both internal redundancy and static redundancy in the joint space offers a large subspace of valid solutions that can be condensed through the selection of appropriate objective priorities, constraints or cost functions. Traditional approaches to such redundancy resolution necessitate computationally expensive numerical optimization. The control of both kinematic and actuation redundancies requires cascaded control frameworks that cannot easily be applied towards real-time control.
The selected cost functions for numerical optimization of rCDPRs can be globally (and sometimes locally) non-convex. In this work we present two applied examples of redundancy resolution control that are unique to rCDPRs. In the first example, we maximize the directional wrench ability at the end-effector while minimizing the joint torque requirement by utilizing the fitness of the available wrench set as a constraint over wrench feasibility. The second example focuses on directional stiffness maximization at the end-effector through a variable stiffness module (VSM) that partially decouples the tension and stiffness. The VSM introduces an additional degrees of freedom to the system in order to manipulate both reconfigurability and cable stiffness independently.
The controllers in the above examples were designed with kinematic models, but most CDPRs are highly dynamic systems which can require challenging feedback control frameworks. An approach to real-time dynamic control was implemented in this thesis by incorporating a learning-based frameworks through deep reinforcement learning. Three approaches to rCDPR training were attempted utilizing model-free TD3 networks.
Robustness and safety are critical features for robot development. One of the main causes of robot failure in CDPRs is due to cable breakage. This not only causes dangerous dynamic oscillations in the workspace, but also leads to total robot failure if the controllability (due to lack of cables) is lost. Fortunately, rCDPRs can be utilized towards failure tolerant control for task recovery. The kinematically redundant joints can be utilized to help recover the lost degrees of freedom due to cable failure. This work applies a Multi-Model Adaptive Estimation (MMAE) framework to enable online and automatic objective reprioritization and actuator retasking. The likelihood of cable failure(s) from the estimator informs the mixing of the control inputs from a bank of feedforward controllers.
In traditional rigid body robots, safety procedures generally involve a standard emergency stop procedure such as actuator locking. Due to the flexibility of cable links, the dynamic oscillations of the end-effector due to cable failure must be actively dampened. This work incorporates a Linear Quadratic Regulator (LQR) based feedback stabilizer into the failure tolerant control framework that works to stabilize the non-linear system and dampen out these oscillations.
This research contributes to a growing, but hitherto niche body of work in reconfigurable cable driven parallel manipulators. Some outcomes of the multiple engineering design, control and estimation challenges addressed in this research warrant further exploration and study that are beyond the scope of this thesis. This thesis concludes with a thorough discussion of the advantages and limitations of the presented work and avenues for further research that may be of interest to continuing scholars in the community
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