Robotics in Life Science Automation

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: closed (30 May 2023) | Viewed by 9659

Special Issue Editor


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Guest Editor
Center for Life Science Automation, University Rostock, 18119 Rostock, Germany
Interests: life science automation and engineering; process automation; robotic systems; mobile robotics; high throughput technologies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Automation solutions have been established in numerous industrial areas. By contrast, classic life sciences laboratories are often still characterized by predominantly manual activities. The establishment of robot-based solutions is only progressing slowly in this area due to the high diversity of the processes. With regard to a further increase in throughput, but also a reduction in the exposure of laboratory personnel to toxic and infectious materials, further development of this research area is essential.

This Special Issue on “Robotics in Life Science Applications” aims to reflect recent developments and applications of robot-based systems for classical laboratory applications. Submissions are expected to focus on the development of suitable robotic technology as well as on the application of robot-based systems. New ideas for further development of this research area are very welcome.

Topics of interest include but are not limited to the following areas:

  • Recent robotic developments for laboratory applications;
  • Advances in robot technology;
  • Automated robot-based systems for analytical and medical applications;
  • Mobile robotics in life science laboratories;
  • Robots in life science applications;
  • Innovative control systems in laboratory automation;
  • Intelligent scheduling systems for life science applications.

We hope this Special Issue works as a roadmap for all developers and users of robotic-based equipment in life science laboratories.

Prof. Dr. Kerstin Thurow
Guest Editor

Manuscript Submission Information

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Published Papers (5 papers)

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Editorial

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6 pages, 180 KiB  
Editorial
System Concepts for Robots in Life Science Applications
by Kerstin Thurow
Appl. Sci. 2022, 12(7), 3257; https://doi.org/10.3390/app12073257 - 23 Mar 2022
Cited by 2 | Viewed by 1387
Abstract
For a long time, robot-based automation solutions have found their way into industrial production and manufacturing [...] Full article
(This article belongs to the Special Issue Robotics in Life Science Automation)

Research

Jump to: Editorial

18 pages, 5174 KiB  
Article
Time-Varying Topology Formation Reconfiguration Control of the Multi-Agent System Based on the Improved Hungarian Algorithm
by Yingxue Zhang, Meng Chen, Jinbao Chen, Chuanzhi Chen, Hongzhi Yu, Yunxiao Zhang and Xiaokang Deng
Appl. Sci. 2023, 13(20), 11581; https://doi.org/10.3390/app132011581 - 23 Oct 2023
Cited by 1 | Viewed by 629
Abstract
Distributed time-varying formation technology for multi-agent systems is recently become a research hotspot in formation control field. However, the formation reconfiguration control technology for agents that randomly appeared to fail during maneuvers is rarely studied. In this paper, the topological relations between intelligence [...] Read more.
Distributed time-varying formation technology for multi-agent systems is recently become a research hotspot in formation control field. However, the formation reconfiguration control technology for agents that randomly appeared to fail during maneuvers is rarely studied. In this paper, the topological relations between intelligence are designed by graph theory to simplify the cooperative interaction between multi-agent systems. Moreover, this paper constructs the time-varying configuration of the target formation based on the rigidity graph theory and leader–follower strategy. Drawing on the establishment of the expert experience database in a collaborative process, we innovatively propose the establishment of a graphic library to help the multi-agent system quickly form an affine transformation as soon as it is disabled. Secondly, the improved Hungarian algorithm is adopted to allocate the target point when the first failure occurs. This algorithm incorporates a gradient weighting factor from the auction algorithm to improve the speed of system reconfiguration with minimum path cost. On this basis, a distributed multi-agent control law based on consistency theory is established, and the system’s stability can be guaranteed via Lyapunov functions. Finally, the simulation results demonstrate the feasibility and effectiveness of the proposed formation reconfiguration control algorithm in a collaborative environment. Full article
(This article belongs to the Special Issue Robotics in Life Science Automation)
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29 pages, 2311 KiB  
Article
Effect of Formation Size on Flocking Formation Performance for the Goal Reach Problem
by Sarab AlMuhaideb, Ameur Touir, Reem Alshraihi, Najwa Altwaijry and Safwan Qasem
Appl. Sci. 2022, 12(7), 3630; https://doi.org/10.3390/app12073630 - 03 Apr 2022
Viewed by 1411
Abstract
Flocking is one of the swarm tasks inspired by animal behavior. A flock involves multiple agents aiming to achieve a goal while maintaining certain characteristics of their formation. In nature, flocks vary in size. Although several studies have focused on the flock controller [...] Read more.
Flocking is one of the swarm tasks inspired by animal behavior. A flock involves multiple agents aiming to achieve a goal while maintaining certain characteristics of their formation. In nature, flocks vary in size. Although several studies have focused on the flock controller itself, less research has focused on how the flock size affects flock formation and performance. In this study, we address this problem and develop a simple flock controller for goal-zone-reaching tasks. The developed controller is intended for a two-dimensional environment and can handle obstacles as well as integrate an additional invented feature, called sensing power, in order to simulate the natural dynamics of migratory birds. This controller is simulated using the NetLogo simulation tool. Several experiments were conducted with and without obstacles, accompanied by changes in the flock size. The simulation results demonstrate that the flock controller is able to successfully deliver the flock to the goal zone. In addition, changes in the flock size affect multiple metrics, such as the time required to reach the goal (and, consequently, the time required to complete the flocking task), as well as the number of collisions that occur. Full article
(This article belongs to the Special Issue Robotics in Life Science Automation)
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19 pages, 2983 KiB  
Article
Automation System for the Flexible Sample Preparation for Quantification of Δ9-THC-D3, THC-OH and THC-COOH from Serum, Saliva and Urine
by Anna Bach, Heidi Fleischer, Bhagya Wijayawardena and Kerstin Thurow
Appl. Sci. 2022, 12(6), 2838; https://doi.org/10.3390/app12062838 - 10 Mar 2022
Cited by 7 | Viewed by 2467
Abstract
In the life sciences, automation solutions are primarily established in the field of drug discovery. However, there is also an increasing need for automated solutions in the field of medical diagnostics, e.g., for the determination of vitamins, medication or drug abuse. While the [...] Read more.
In the life sciences, automation solutions are primarily established in the field of drug discovery. However, there is also an increasing need for automated solutions in the field of medical diagnostics, e.g., for the determination of vitamins, medication or drug abuse. While the actual metrological determination is highly automated today, the necessary sample preparation processes are still mainly carried out manually. In the laboratory, flexible solutions are required that can be used to determine different target substances in different matrices. A suitable system based on an automated liquid handler was implemented. It has been tested and validated for the determination of three cannabinoid metabolites in blood, urine and saliva. To extract Δ9-tetrahydrocannabinol-D3 (Δ9-THC-D3), 11-hydroxy-Δ9-tetrahydrocannabinol (THC-OH) and 11-nor-9-carboxy-Δ9-tetrahydrocannabinol (THC-COOH) from serum, urine and saliva both rapidly and cost-effectively, three sample preparation methods automated with a liquid handling robot are presented in this article, the basic framework of which is an identical SPE method so that they can be quickly exchanged against each other when the matrix is changed. If necessary, the three matrices could also be prepared in parallel. For the sensitive detection of analytes, protein precipitation is used when preparing serum before SPE and basic hydrolysis is used for urine to cleave the glucuronide conjugate. Recoveries of developed methods are >77%. Coefficients of variation are <4%. LODs are below 1 ng/mL and a comparison with the manual process shows a significant cost reduction. Full article
(This article belongs to the Special Issue Robotics in Life Science Automation)
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20 pages, 6950 KiB  
Article
A Robot Dynamic Target Grasping Method Based on Affine Group Improved Gaussian Resampling Particle Filter
by Yong Tao, Fan Ren, He Gao, Tianmiao Wang, Shan Jiang, Yufang Wen and Jiangbo Lan
Appl. Sci. 2021, 11(21), 10270; https://doi.org/10.3390/app112110270 - 01 Nov 2021
Cited by 2 | Viewed by 1958
Abstract
Tracking and grasping a moving target is currently a challenging topic in the field of robotics. The current visual servo grasping method is still inadequate, as the real-time performance and robustness of target tracking both need to be improved. A target tracking method [...] Read more.
Tracking and grasping a moving target is currently a challenging topic in the field of robotics. The current visual servo grasping method is still inadequate, as the real-time performance and robustness of target tracking both need to be improved. A target tracking method is proposed based on improved geometric particle filtering (IGPF). Following the geometric particle filtering (GPF) tracking framework, affine groups are proposed as state particles. Resampling is improved by incorporating an improved conventional Gaussian resampling algorithm. It addresses the problem of particle diversity loss and improves tracking performance. Additionally, the OTB2015 dataset and typical evaluation indicators in target tracking are adopted. Comparative experiments are performed using PF, GPF and the proposed IGPF algorithm. A dynamic target tracking and grasping method for the robot is proposed. It combines an improved Gaussian resampling particle filter algorithm based on affine groups and the positional visual servo control of the robot. Finally, the robot conducts simulation and experiments on capturing dynamic targets in the simulation environment and actual environment. It verifies the effectiveness of the method proposed in this paper. Full article
(This article belongs to the Special Issue Robotics in Life Science Automation)
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