12,336 research outputs found
UMSL Bulletin 2023-2024
The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp
Vision-enhanced Peg-in-Hole for automotive body parts using semantic image segmentation and object detection
Artificial Intelligence (AI) is an enabling technology in the context of Industry 4.0. In particular, the automotive sector is among those who can benefit most of the use of AI in conjunction with advanced vision techniques. The scope of this work is to integrate deep learning algorithms in an industrial scenario involving a robotic Peg-in-Hole task. More in detail, we focus on a scenario where a human operator manually positions a carbon fiber automotive part in the workspace of a 7 Degrees of Freedom (DOF) manipulator. To cope with the uncertainty on the relative position between the robot and the workpiece, we adopt a three stage strategy. The first stage concerns the Three-Dimensional (3D) reconstruction of the workpiece using a registration algorithm based on the Iterative Closest Point (ICP) paradigm. Such a procedure is integrated with a semantic image segmentation neural network, which is in charge of removing the background of the scene to improve the registration. The adoption of such network allows to reduce the registration time of about 28.8%. In the second stage, the reconstructed surface is compared with a Computer Aided Design (CAD) model of the workpiece to locate the holes and their axes. In this stage, the adoption of a Convolutional Neural Network (CNN) allows to improve the holes’ position estimation of about 57.3%. The third stage concerns the insertion of the peg by implementing a search phase to handle the remaining estimation errors. Also in this case, the use of the CNN reduces the search phase duration of about 71.3%. Quantitative experiments, including a comparison with a previous approach without both the segmentation network and the CNN, have been conducted in a realistic scenario. The results show the effectiveness of the proposed approach and how the integration of AI techniques improves the success rate from 84.5% to 99.0%
A novel adaptive PD-type iterative learning control of the PMSM servo system with the friction uncertainty in low speeds
High precision demands in a large number of emerging robotic applications strengthened the role of the modern control laws in the position control of the Permanent Magnet Synchronous Motor (PMSM) servo system. This paper proposes a learning-based adaptive control approach to improve the PMSM position tracking in the presence of the friction uncertainty. In contrast to most of the reported works considering the servos operating at high speeds, this paper focuses on low speeds in which the friction stemmed deteriorations become more obvious. In this paper firstly, a servo model involving the Stribeck friction dynamics is formulated, and the unknown friction parameters are identified by a genetic algorithm from the offline data. Then, a feedforward controller is designed to inject the friction information into the loop and eliminate it before causing performance degradations. Since the friction is a kind of disturbance and leads to uncertainties having time-varying characters, an Adaptive Proportional Derivative (APD) type Iterative Learning Controller (ILC) named as the APD-ILC is designed to mitigate the friction effects. Finally, the proposed control approach is simulated in MATLAB/Simulink environment and it is compared with the conventional Proportional Integral Derivative (PID) controller, Proportional ILC (P-ILC), and Proportional Derivative ILC (PD-ILC) algorithms. The results confirm that the proposed APD-ILC significantly lessens the effects of the friction and thus noticeably improves the control performance in the low speeds of the PMSM
State-wise Constrained Policy Optimization
Reinforcement Learning (RL) algorithms have shown tremendous success in
simulation environments, but their application to real-world problems faces
significant challenges, with safety being a major concern. In particular,
enforcing state-wise constraints is essential for many challenging tasks such
as autonomous driving and robot manipulation. However, existing safe RL
algorithms under the framework of Constrained Markov Decision Process (CMDP) do
not consider state-wise constraints. To address this gap, we propose State-wise
Constrained Policy Optimization (SCPO), the first general-purpose policy search
algorithm for state-wise constrained reinforcement learning. SCPO provides
guarantees for state-wise constraint satisfaction in expectation. In
particular, we introduce the framework of Maximum Markov Decision Process, and
prove that the worst-case safety violation is bounded under SCPO. We
demonstrate the effectiveness of our approach on training neural network
policies for extensive robot locomotion tasks, where the agent must satisfy a
variety of state-wise safety constraints. Our results show that SCPO
significantly outperforms existing methods and can handle state-wise
constraints in high-dimensional robotics tasks.Comment: arXiv admin note: text overlap with arXiv:2305.1368
Initial Value Problem Enhanced Sampling for Closed-Loop Optimal Control Design with Deep Neural Networks
Closed-loop optimal control design for high-dimensional nonlinear systems has
been a long-standing challenge. Traditional methods, such as solving the
associated Hamilton-Jacobi-Bellman equation, suffer from the curse of
dimensionality. Recent literature proposed a new promising approach based on
supervised learning, by leveraging powerful open-loop optimal control solvers
to generate training data and neural networks as efficient high-dimensional
function approximators to fit the closed-loop optimal control. This approach
successfully handles certain high-dimensional optimal control problems but
still performs poorly on more challenging problems. One of the crucial reasons
for the failure is the so-called distribution mismatch phenomenon brought by
the controlled dynamics. In this paper, we investigate this phenomenon and
propose the initial value problem enhanced sampling method to mitigate this
problem. We theoretically prove that this sampling strategy improves over the
vanilla strategy on the classical linear-quadratic regulator by a factor
proportional to the total time duration. We further numerically demonstrate
that the proposed sampling strategy significantly improves the performance on
tested control problems, including the optimal landing problem of a quadrotor
and the optimal reaching problem of a 7 DoF manipulator
Safety-Aware Human-Robot Collaborative Transportation and Manipulation with Multiple MAVs
Human-robot interaction will play an essential role in various industries and
daily tasks, enabling robots to effectively collaborate with humans and reduce
their physical workload. Most of the existing approaches for physical
human-robot interaction focus on collaboration between a human and a single
ground robot. In recent years, very little progress has been made in this
research area when considering aerial robots, which offer increased versatility
and mobility compared to their grounded counterparts. This paper proposes a
novel approach for safe human-robot collaborative transportation and
manipulation of a cable-suspended payload with multiple aerial robots. We
leverage the proposed method to enable smooth and intuitive interaction between
the transported objects and a human worker while considering safety constraints
during operations by exploiting the redundancy of the internal transportation
system. The key elements of our system are (a) a distributed payload external
wrench estimator that does not rely on any force sensor; (b) a 6D admittance
controller for human-aerial-robot collaborative transportation and
manipulation; (c) a safety-aware controller that exploits the internal system
redundancy to guarantee the execution of additional tasks devoted to preserving
the human or robot safety without affecting the payload trajectory tracking or
quality of interaction. We validate the approach through extensive simulation
and real-world experiments. These include as well the robot team assisting the
human in transporting and manipulating a load or the human helping the robot
team navigate the environment. To the best of our knowledge, this work is the
first to create an interactive and safety-aware approach for quadrotor teams
that physically collaborate with a human operator during transportation and
manipulation tasks.Comment: Guanrui Li and Xinyang Liu contributed equally to this pape
Technical Report on: Tripedal Dynamic Gaits for a Quadruped Robot
A vast number of applications for legged robots entail tasks in complex,
dynamic environments. But these environments put legged robots at high risk for
limb damage. This paper presents an empirical study of fault tolerant dynamic
gaits designed for a quadrupedal robot suffering from a single, known
``missing'' limb. Preliminary data suggests that the featured gait controller
successfully anchors a previously developed planar monopedal hopping template
in the three-legged spatial machine. This compositional approach offers a
useful and generalizable guide to the development of a wider range of tripedal
recovery gaits for damaged quadrupedal machines.Comment: Updated *increased font size on figures 2-6 *added a legend, replaced
text with colors in figure 5a and 6a *made variables representing vectors
boldface in equations 8-10 *expanded on calculations in equations 8-10 by
adding additional lines *added a missing "2" to equation 8 (typo) *added mass
of the robot to tables II and III *increased the width of figures 1 and
Unconventional Cognitive Intelligent Robotic Control: Quantum Soft Computing Approach in Human Being Emotion Estimation -- QCOptKB Toolkit Application
Strategy of intelligent cognitive control systems based on quantum and soft
computing presented. Quantum self-organization knowledge base synergetic effect
extracted from intelligent fuzzy controllers imperfect knowledge bases
described. That technology improved of robustness of intelligent cognitive
control systems in hazard control situations described with the cognitive
neuro-interface and different types of robot cooperation. Examples demonstrated
the introduction of quantum fuzzy inference gate design as prepared
programmable algorithmic solution for board embedded control systems. The
possibility of neuro-interface application based on cognitive helmet with
quantum fuzzy controller for driving of the vehicle is shown
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