6,656 research outputs found
Multi-robot-based nanoassembly planning with automated path generation
In this paper, a novel approach of automated multirobot nanoassembly planning is presented. This approach uses an improved self-organizing map to coordinate assembly tasks of nanorobots while generating optimized motion paths at run time with a modified shunting neural network. It is capable of synchronizing multiple nanorobots working simultaneously and efficiently on the assembly of swarms of objects in the presence of obstacles and environmental uncertainty. Operation of the presented approach is demonstrated with experiments at the end of the paper
Virtual assembly with biologically inspired intelligence
This paper investigates the introduction of biologically inspired intelligence into virtual assembly. It develops a approach to assist product engineers making assembly-related manufacturing decisions without actually realizing the physical products. This approach extracts the knowledge of mechanical assembly by allowing human operators to perform assembly operations directly in the virtual environment. The incorporation of a biologically inspired neural network into an interactive assembly planner further leads to the improvement of flexible product manufacturing, i.e., automatically producing alternative assembly sequences with robot-level instructions for evaluation and optimization. Complexity analysis and simulation study demonstrate the effectiveness and efficiency of this approach
Intelligent Feature Extraction, Data Fusion and Detection of Concrete Bridge Cracks: Current Development and Challenges
As a common appearance defect of concrete bridges, cracks are important
indices for bridge structure health assessment. Although there has been much
research on crack identification, research on the evolution mechanism of bridge
cracks is still far from practical applications. In this paper, the
state-of-the-art research on intelligent theories and methodologies for
intelligent feature extraction, data fusion and crack detection based on
data-driven approaches is comprehensively reviewed. The research is discussed
from three aspects: the feature extraction level of the multimodal parameters
of bridge cracks, the description level and the diagnosis level of the bridge
crack damage states. We focus on previous research concerning the quantitative
characterization problems of multimodal parameters of bridge cracks and their
implementation in crack identification, while highlighting some of their major
drawbacks. In addition, the current challenges and potential future research
directions are discussed.Comment: Published at Intelligence & Robotics; Its copyright belongs to
author
Intelligent Escape of Robotic Systems: A Survey of Methodologies, Applications, and Challenges
Intelligent escape is an interdisciplinary field that employs artificial
intelligence (AI) techniques to enable robots with the capacity to
intelligently react to potential dangers in dynamic, intricate, and
unpredictable scenarios. As the emphasis on safety becomes increasingly
paramount and advancements in robotic technologies continue to advance, a wide
range of intelligent escape methodologies has been developed in recent years.
This paper presents a comprehensive survey of state-of-the-art research work on
intelligent escape of robotic systems. Four main methods of intelligent escape
are reviewed, including planning-based methodologies, partitioning-based
methodologies, learning-based methodologies, and bio-inspired methodologies.
The strengths and limitations of existing methods are summarized. In addition,
potential applications of intelligent escape are discussed in various domains,
such as search and rescue, evacuation, military security, and healthcare. In an
effort to develop new approaches to intelligent escape, this survey identifies
current research challenges and provides insights into future research trends
in intelligent escape.Comment: This paper is accepted by Journal of Intelligent and Robotic System
A Real-Time De-Noising Algorithm for E-Noses in a Wireless Sensor Network
A wireless e-nose network system is developed for the special purpose of monitoring odorant gases and accurately estimating odor strength in and around livestock farms. This system is to simultaneously acquire accurate odor strength values remotely at various locations, where each node is an e-nose that includes four metal-oxide semiconductor (MOS) gas sensors. A modified Kalman filtering technique is proposed for collecting raw data and de-noising based on the output noise characteristics of those gas sensors. The measurement noise variance is obtained in real time by data analysis using the proposed slip windows average method. The optimal system noise variance of the filter is obtained by using the experiments data. The Kalman filter theory on how to acquire MOS gas sensors data is discussed. Simulation results demonstrate that the proposed method can adjust the Kalman filter parameters and significantly reduce the noise from the gas sensors
Distributed Robust Learning-Based Backstepping Control Aided with Neurodynamics for Consensus Formation Tracking of Underwater Vessels
This paper addresses distributed robust learning-based control for consensus
formation tracking of multiple underwater vessels, in which the system
parameters of the marine vessels are assumed to be entirely unknown and subject
to the modeling mismatch, oceanic disturbances, and noises. Towards this end,
graph theory is used to allow us to synthesize the distributed controller with
a stability guarantee. Due to the fact that the parameter uncertainties only
arise in the vessels' dynamic model, the backstepping control technique is then
employed. Subsequently, to overcome the difficulties in handling time-varying
and unknown systems, an online learning procedure is developed in the proposed
distributed formation control protocol. Moreover, modeling errors,
environmental disturbances, and measurement noises are considered and tackled
by introducing a neurodynamics model in the controller design to obtain a
robust solution. Then, the stability analysis of the overall closed-loop system
under the proposed scheme is provided to ensure the robust adaptive performance
at the theoretical level. Finally, extensive simulation experiments are
conducted to further verify the efficacy of the presented distributed control
protocol
A Fuzzy Logic-based Cascade Control without Actuator Saturation for the Unmanned Underwater Vehicle Trajectory Tracking
An intelligent control strategy is proposed to eliminate the actuator
saturation problem that exists in the trajectory tracking process of unmanned
underwater vehicles (UUV). The control strategy consists of two parts: for the
kinematic modeling part, a fuzzy logic-refined backstepping control is
developed to achieve control velocities within acceptable ranges and errors of
small fluctuations; on the basis of the velocities deducted by the improved
kinematic control, the sliding mode control (SMC) is introduced in the dynamic
modeling to obtain corresponding torques and forces that should be applied to
the vehicle body. With the control velocities computed by the kinematic model
and applied forces derived by the dynamic model, the robustness and accuracy of
the UUV trajectory without actuator saturation can be achieved
A Dynamic Bioinspired Neural Network Based Real-Time Path Planning Method for Autonomous Underwater Vehicles
Real-time path planning for autonomous underwater vehicle (AUV) is a very difficult and challenging task. Bioinspired neural network (BINN) has been used to deal with this problem for its many distinct advantages: that is, no learning process is needed and realization is also easy. However, there are some shortcomings when BINN is applied to AUV path planning in a three-dimensional (3D) unknown environment, including complex computing problem when the environment is very large and repeated path problem when the size of obstacles is bigger than the detection range of sensors. To deal with these problems, an improved dynamic BINN is proposed in this paper. In this proposed method, the AUV is regarded as the core of the BINN and the size of the BINN is based on the detection range of sensors. Then the BINN will move with the AUV and the computing could be reduced. A virtual target is proposed in the path planning method to ensure that the AUV can move to the real target effectively and avoid big-size obstacles automatically. Furthermore, a target attractor concept is introduced to improve the computing efficiency of neural activities. Finally, some experiments are conducted under various 3D underwater environments. The experimental results show that the proposed BINN based method can deal with the real-time path planning problem for AUV efficiently
Synthesis and characterization of photoaffinity labelling reagents towards the Hsp90 C-terminal domain
Glucosyl-novobiocin-based diazirine photoaffinity labelling reagents (PALs) were designed and synthesized to probe the Hsp90 C-terminal domain unknown binding pocket and the structure-activity relationship. Five PALs were successfully synthesized from novobiocin in six consecutive steps employing phase transfer catalytic glycosylation. Reactions were monitored and guided by analytical LC/MS which led to different strategies of adding either a PAL precursor or a sugar moiety first. The structures and bonding linkages of these compounds were characterised by various 2D-NMR spectroscopy and MS techniques. Synthetic techniques provide powerful probes for unknown protein binding pockets
An Intelligent Trust Cloud Management Method for Secure Clustering in 5G enabled Internet of Medical Things
5G edge computing enabled Internet of Medical Things (IoMT) is an efficient
technology to provide decentralized medical services while Device-to-device
(D2D) communication is a promising paradigm for future 5G networks. To assure
secure and reliable communication in 5G edge computing and D2D enabled IoMT
systems, this paper presents an intelligent trust cloud management method.
Firstly, an active training mechanism is proposed to construct the standard
trust clouds. Secondly, individual trust clouds of the IoMT devices can be
established through fuzzy trust inferring and recommending. Thirdly, a trust
classification scheme is proposed to determine whether an IoMT device is
malicious. Finally, a trust cloud update mechanism is presented to make the
proposed trust management method adaptive and intelligent under an open
wireless medium. Simulation results demonstrate that the proposed method can
effectively address the trust uncertainty issue and improve the detection
accuracy of malicious devices
- …