258 research outputs found
Alignment control using visual servoing and mobilenet single-shot multi-box detection (SSD): a review
The concept is highly critical for robotic technologies that rely on visual feedback. In this context, robot systems tend to be unresponsive due to reliance on pre-programmed trajectory and path, meaning the occurrence of a change in the environment or the absence of an object. This review paper aims to provide comprehensive studies on the recent application of visual servoing and DNN. PBVS and Mobilenet-SSD were chosen algorithms for alignment control of the film handler mechanism of the portable x-ray system. It also discussed the theoretical framework features extraction and description, visual servoing, and Mobilenet-SSD. Likewise, the latest applications of visual servoing and DNN was summarized, including the comparison of Mobilenet-SSD with other sophisticated models. As a result of a previous study presented, visual servoing and MobileNet-SSD provide reliable tools and models for manipulating robotics systems, including where occlusion is present. Furthermore, effective alignment control relies significantly on visual servoing and deep neural reliability, shaped by different parameters such as the type of visual servoing, feature extraction and description, and DNNs used to construct a robust state estimator. Therefore, visual servoing and MobileNet-SSD are parameterized concepts that require enhanced optimization to achieve a specific purpose with distinct tools
Neural Potential Field for Obstacle-Aware Local Motion Planning
Model predictive control (MPC) may provide local motion planning for mobile
robotic platforms. The challenging aspect is the analytic representation of
collision cost for the case when both the obstacle map and robot footprint are
arbitrary. We propose a Neural Potential Field: a neural network model that
returns a differentiable collision cost based on robot pose, obstacle map, and
robot footprint. The differentiability of our model allows its usage within the
MPC solver. It is computationally hard to solve problems with a very high
number of parameters. Therefore, our architecture includes neural image
encoders, which transform obstacle maps and robot footprints into embeddings,
which reduce problem dimensionality by two orders of magnitude. The reference
data for network training are generated based on algorithmic calculation of a
signed distance function. Comparative experiments showed that the proposed
approach is comparable with existing local planners: it provides trajectories
with outperforming smoothness, comparable path length, and safe distance from
obstacles. Experiment on Husky UGV mobile robot showed that our approach allows
real-time and safe local planning. The code for our approach is presented at
https://github.com/cog-isa/NPField together with demo video
A novel robot calibration method with plane constraint based on dial indicator
In pace with the electronic technology development and the production
technology improvement, industrial robot Give Scope to the Advantage in social
services and industrial production. However, due to long-term mechanical wear
and structural deformation, the absolute positioning accuracy is low, which
greatly hinders the development of manufacturing industry. Calibrating the
kinematic parameters of the robot is an effective way to address it. However,
the main measuring equipment such as laser trackers and coordinate measuring
machines are expensive and need special personnel to operate. Additionally, in
the measurement process, due to the influence of many environmental factors,
measurement noises are generated, which will affect the calibration accuracy of
the robot. Basing on these, we have done the following work: a) developing a
robot calibration method based on plane constraint to simplify measurement
steps; b) employing Square-root Culture Kalman Filter (SCKF) algorithm for
reducing the influence of measurement noises; c) proposing a novel algorithm
for identifying kinematic parameters based on SCKF algorithm and Levenberg
Marquardt (LM) algorithm to achieve the high calibration accuracy; d) adopting
the dial indicator as the measuring equipment for slashing costs. The enough
experiments verify the effectiveness of the proposed calibration algorithm and
experimental platform
Visual Servoing in Robotics
Visual servoing is a well-known approach to guide robots using visual information. Image processing, robotics, and control theory are combined in order to control the motion of a robot depending on the visual information extracted from the images captured by one or several cameras. With respect to vision issues, a number of issues are currently being addressed by ongoing research, such as the use of different types of image features (or different types of cameras such as RGBD cameras), image processing at high velocity, and convergence properties. As shown in this book, the use of new control schemes allows the system to behave more robustly, efficiently, or compliantly, with fewer delays. Related issues such as optimal and robust approaches, direct control, path tracking, or sensor fusion are also addressed. Additionally, we can currently find visual servoing systems being applied in a number of different domains. This book considers various aspects of visual servoing systems, such as the design of new strategies for their application to parallel robots, mobile manipulators, teleoperation, and the application of this type of control system in new areas
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