5,808 research outputs found
Dual Design PID Controller for Robotic Manipulator Application
This research introduces a dual design proportional–integral–derivative (PID) controller architecture process that aims to improve system performance by reducing overshoot and conserving electrical energy. The dual design PID controller uses real-time error and one-time step delay to adjust the confidence weights of the controller, leading to improved performance in reducing overshoot and saving electrical energy. To evaluate the effectiveness of the dual design PID controller, experiments were conducted to compare it with the PID controller using least overshoot tuning by Chien–Hrones–Reswick (CHR) technique. The results showed that the dual design PID controller was more effective at reducing overshoot and saving electrical energy. A case study was also conducted as part of this research, and it demonstrated that the system performed better when using the dual design PID controller. Overshoot and electrical energy consumption are common issues in systems that can impact performance, and the dual design PID controller architecture process provides a solution to these issues by reducing overshoot and saving electrical energy. The dual design PID controller offers a new technique for addressing these issues and improving system performance. In summary, this research presents a new technique for addressing overshoot and electrical energy consumption in systems through the use of a dual design PID controller. The dual design PID controller architecture process was found to be an effective solution for reducing overshoot and saving electrical energy in systems, as demonstrated by the experiments and case study conducted as part of this research. The dual design PID controller presents a promising solution for improving system performance by addressing the issues of overshoot and electrical energy consumption
Learning Contact-Rich Manipulation Skills with Guided Policy Search
Autonomous learning of object manipulation skills can enable robots to
acquire rich behavioral repertoires that scale to the variety of objects found
in the real world. However, current motion skill learning methods typically
restrict the behavior to a compact, low-dimensional representation, limiting
its expressiveness and generality. In this paper, we extend a recently
developed policy search method \cite{la-lnnpg-14} and use it to learn a range
of dynamic manipulation behaviors with highly general policy representations,
without using known models or example demonstrations. Our approach learns a set
of trajectories for the desired motion skill by using iteratively refitted
time-varying linear models, and then unifies these trajectories into a single
control policy that can generalize to new situations. To enable this method to
run on a real robot, we introduce several improvements that reduce the sample
count and automate parameter selection. We show that our method can acquire
fast, fluent behaviors after only minutes of interaction time, and can learn
robust controllers for complex tasks, including putting together a toy
airplane, stacking tight-fitting lego blocks, placing wooden rings onto
tight-fitting pegs, inserting a shoe tree into a shoe, and screwing bottle caps
onto bottles
Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning
Reinforcement learning (RL) algorithms for real-world robotic applications
need a data-efficient learning process and the ability to handle complex,
unknown dynamical systems. These requirements are handled well by model-based
and model-free RL approaches, respectively. In this work, we aim to combine the
advantages of these two types of methods in a principled manner. By focusing on
time-varying linear-Gaussian policies, we enable a model-based algorithm based
on the linear quadratic regulator (LQR) that can be integrated into the
model-free framework of path integral policy improvement (PI2). We can further
combine our method with guided policy search (GPS) to train arbitrary
parameterized policies such as deep neural networks. Our simulation and
real-world experiments demonstrate that this method can solve challenging
manipulation tasks with comparable or better performance than model-free
methods while maintaining the sample efficiency of model-based methods. A video
presenting our results is available at
https://sites.google.com/site/icml17pilqrComment: Paper accepted to the International Conference on Machine Learning
(ICML) 201
Automated pick-up of suturing needles for robotic surgical assistance
Robot-assisted laparoscopic prostatectomy (RALP) is a treatment for prostate
cancer that involves complete or nerve sparing removal prostate tissue that
contains cancer. After removal the bladder neck is successively sutured
directly with the urethra. The procedure is called urethrovesical anastomosis
and is one of the most dexterity demanding tasks during RALP. Two suturing
instruments and a pair of needles are used in combination to perform a running
stitch during urethrovesical anastomosis. While robotic instruments provide
enhanced dexterity to perform the anastomosis, it is still highly challenging
and difficult to learn. In this paper, we presents a vision-guided needle
grasping method for automatically grasping the needle that has been inserted
into the patient prior to anastomosis. We aim to automatically grasp the
suturing needle in a position that avoids hand-offs and immediately enables the
start of suturing. The full grasping process can be broken down into: a needle
detection algorithm; an approach phase where the surgical tool moves closer to
the needle based on visual feedback; and a grasping phase through path planning
based on observed surgical practice. Our experimental results show examples of
successful autonomous grasping that has the potential to simplify and decrease
the operational time in RALP by assisting a small component of urethrovesical
anastomosis
Single-Shot Clothing Category Recognition in Free-Configurations with Application to Autonomous Clothes Sorting
This paper proposes a single-shot approach for recognising clothing
categories from 2.5D features. We propose two visual features, BSP (B-Spline
Patch) and TSD (Topology Spatial Distances) for this task. The local BSP
features are encoded by LLC (Locality-constrained Linear Coding) and fused with
three different global features. Our visual feature is robust to deformable
shapes and our approach is able to recognise the category of unknown clothing
in unconstrained and random configurations. We integrated the category
recognition pipeline with a stereo vision system, clothing instance detection,
and dual-arm manipulators to achieve an autonomous sorting system. To verify
the performance of our proposed method, we build a high-resolution RGBD
clothing dataset of 50 clothing items of 5 categories sampled in random
configurations (a total of 2,100 clothing samples). Experimental results show
that our approach is able to reach 83.2\% accuracy while classifying clothing
items which were previously unseen during training. This advances beyond the
previous state-of-the-art by 36.2\%. Finally, we evaluate the proposed approach
in an autonomous robot sorting system, in which the robot recognises a clothing
item from an unconstrained pile, grasps it, and sorts it into a box according
to its category. Our proposed sorting system achieves reasonable sorting
success rates with single-shot perception.Comment: 9 pages, accepted by IROS201
Manipulating Highly Deformable Materials Using a Visual Feedback Dictionary
The complex physical properties of highly deformable materials such as
clothes pose significant challenges fanipulation systems. We present a novel
visual feedback dictionary-based method for manipulating defoor autonomous
robotic mrmable objects towards a desired configuration. Our approach is based
on visual servoing and we use an efficient technique to extract key features
from the RGB sensor stream in the form of a histogram of deformable model
features. These histogram features serve as high-level representations of the
state of the deformable material. Next, we collect manipulation data and use a
visual feedback dictionary that maps the velocity in the high-dimensional
feature space to the velocity of the robotic end-effectors for manipulation. We
have evaluated our approach on a set of complex manipulation tasks and
human-robot manipulation tasks on different cloth pieces with varying material
characteristics.Comment: The video is available at goo.gl/mDSC4
Multidimensional Capacitive Sensing for Robot-Assisted Dressing and Bathing
Robotic assistance presents an opportunity to benefit the lives of many
people with physical disabilities, yet accurately sensing the human body and
tracking human motion remain difficult for robots. We present a
multidimensional capacitive sensing technique that estimates the local pose of
a human limb in real time. A key benefit of this sensing method is that it can
sense the limb through opaque materials, including fabrics and wet cloth. Our
method uses a multielectrode capacitive sensor mounted to a robot's end
effector. A neural network model estimates the position of the closest point on
a person's limb and the orientation of the limb's central axis relative to the
sensor's frame of reference. These pose estimates enable the robot to move its
end effector with respect to the limb using feedback control. We demonstrate
that a PR2 robot can use this approach with a custom six electrode capacitive
sensor to assist with two activities of daily living-dressing and bathing. The
robot pulled the sleeve of a hospital gown onto able-bodied participants' right
arms, while tracking human motion. When assisting with bathing, the robot moved
a soft wet washcloth to follow the contours of able-bodied participants' limbs,
cleaning their surfaces. Overall, we found that multidimensional capacitive
sensing presents a promising approach for robots to sense and track the human
body during assistive tasks that require physical human-robot interaction.Comment: 8 pages, 16 figures, International Conference on Rehabilitation
Robotics 201
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