An enhanced moment-based approach to time-dependent positional reliability analysis for robotic manipulators

Zhao, Qiangqiang; Guo, Junkang; Hong, Jun; Chirikjian, Gregory S.

doi:10.1016/j.mechmachtheory.2020.104167

Cited by 29 publications

(6 citation statements)

References 41 publications

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“…Compared with Cartesian robots, the controlling and programming of articulated robots are more complicated. For instance, redundancy can be exploited to improve manipulability and achieve more dexterous motions, but it may complicate the inverse kinematics[ 39 ]. Li et al .…”

Section: In Situ Bioprinting Modalitiesmentioning

confidence: 99%

Robotic-assisted automated in situ bioprinting

Dong¹,

Hu²,

Zhang³

et al. 2022

IJB

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In situ bioprinting has emerged as a promising technology for tissue and organ engineering based on the precise positioning of living cells, growth factors, and biomaterials. Rather than traditional in vitro reconstruction and recapitulation of tissue or organ models, the in situ technology can directly print on specific anatomical positions in living bodies. The requirements for biological activity, function, and mechanical property in an in vivo setting are more complex. By combining progressive innovations of biomaterials, tissue engineering, and digitalization, especially robotics, in situ bioprinting has gained significant interest from the academia and industry, demonstrating its prospect for clinical studies. This article reviews the progress of in situ bioprinting, with an emphasis on robotic-assisted studies. The main modalities for in situ three-dimensional bioprinting, which include extrusion-based printing, inkjet printing, laser-based printing, and their derivatives, are briefly introduced. These modalities have been integrated with various custom-tailored printers (i.e., end effectors) mounted on robotic arms for dexterous and precision biofabrication. The typical prototypes based on various robot configurations, including Cartesian, articulated, and parallel mechanisms, for in situ bioprinting are discussed and compared. The conventional and most recent applications of robotic-assisted methods for in situ fabrication of tissue and organ models, including cartilage, bone, and skin, are also elucidated, followed by a discussion on the existing challenges in this field with their corresponding suggestions.

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Section: In Situ Bioprinting Modalitiesmentioning

confidence: 99%

Robotic-assisted automated in situ bioprinting

Dong¹,

Hu²,

Zhang³

et al. 2022

IJB

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“…The purpose of reliability analysis is to estimate the failure probability of a structural system during normal operation under given conditions. At present, reliability analysis has received widespread attention in engineering applications, such as mechanical systems, 6 infrastructure engineering, 7 automobiles, 8 and industrial robots 9 …”

Section: Introductionmentioning

confidence: 99%

“…At present, reliability analysis has received widespread attention in engineering applications, such as mechanical systems, 6 infrastructure engineering, 7 automobiles, 8 and industrial robots. 9 Estimating failure probability (P f ) based on a mechanical model is the key to slope reliability analysis. [10][11][12] At present, several reliability methods are available, such as the first-order reliability method (FORM), second-order reliability method (SORM), and Monte Carlo simulation (MCS) method.…”

Section: Introductionmentioning

confidence: 99%

Copula‐based importance sampling method for efficient slope reliability analysis

Zhou

Xiao

Jiang

et al. 2023

Num Anal Meth Geomechanics

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Efficient slope reliability analysis that accounts for the interdependencies of random variables is challenging. In this study, an efficient reliability method is proposed based on importance sampling (IS) and copulas. First, the IS method based on the design point is introduced to construct the marginal IS density. Copulas are subsequently adopted to capture the interdependencies and establish both the initial joint distributions and the joint IS densities of random variable pairs. The IS algorithm is modified to consider the dependencies through not only the estimation of the probability distribution but also random sampling. Finally, a framework of the copula‐based IS method is constructed. The applicability, accuracy, efficiency, and robustness of the proposed method were validated by a classical slope case. The results showed that with the proposed method, slope reliability could be efficiently assessed with a relatively high accuracy. The dependencies of the variables significantly affected the IS and final slope reliability, whereas the IS density had a significantly lower effect. The proposed method combines the advantages of IS and copulas well. Interdependencies of the variables can be properly characterized during sampling. Moreover, the proposed method was shown to be robust regardless of the system failure probability level. It can be further applied to improve the random finite element method in the future.

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“…Identifying the root cause and correcting it can increase the system reliability which in this case the accuracy of the robot to reach a given location without errors. Several promising methods [ 36 , 37 , 38 ] were suggested to estimate the reliability of a system with moving joints. The authors in the paper [ 36 ] proposed an accurate method of computing the system reliability of robotic manipulators.…”

Section: Introductionmentioning

confidence: 99%

“…They validated the system by taking several samples and also a practical example. The authors in the paper [ 38 ] proposed an improved moment-based method integrated with Lie-group theory, series expansion simulation, sparse grid Gauss-Hermite integration technique and chi-square approximation to estimate the time-dependent positional reliability of a system. This reliability is nothing but the probability of the positional error falling within a safe region over a certain time interval.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning-Based Complete Area Coverage Path Planning for a Modified hTrihex Robot

Apuroop

Elara

et al. 2021

Sensors

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One of the essential attributes of a cleaning robot is to achieve complete area coverage. Current commercial indoor cleaning robots have fixed morphology and are restricted to clean only specific areas in a house. The results of maximum area coverage are sub-optimal in this case. Tiling robots are innovative solutions for such a coverage problem. These new kinds of robots can be deployed in the cases of cleaning, painting, maintenance, and inspection, which require complete area coverage. Tiling robots’ objective is to cover the entire area by reconfiguring to different shapes as per the area requirements. In this context, it is vital to have a framework that enables the robot to maximize the area coverage while minimizing energy consumption. That means it is necessary for the robot to cover the maximum area with the least number of shape reconfigurations possible. The current paper proposes a complete area coverage planning module for the modified hTrihex, a honeycomb-shaped tiling robot, based on the deep reinforcement learning technique. This framework simultaneously generates the tiling shapes and the trajectory with minimum overall cost. In this regard, a convolutional neural network (CNN) with long short term memory (LSTM) layer was trained using the actor-critic experience replay (ACER) reinforcement learning algorithm. The simulation results obtained from the current implementation were compared against the results that were generated through traditional tiling theory models that included zigzag, spiral, and greedy search schemes. The model presented in the current paper was also compared against other methods where this problem was considered as a traveling salesman problem (TSP) solved through genetic algorithm (GA) and ant colony optimization (ACO) approaches. Our proposed scheme generates a path with a minimized cost at a lesser time.

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An enhanced moment-based approach to time-dependent positional reliability analysis for robotic manipulators

Cited by 29 publications

References 41 publications

Robotic-assisted automated in situ bioprinting

Robotic-assisted automated in situ bioprinting

Copula‐based importance sampling method for efficient slope reliability analysis

Reinforcement Learning-Based Complete Area Coverage Path Planning for a Modified hTrihex Robot

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