An Optimal Task Allocation Approach for Large-Scale Multiple Robotic Systems With Hierarchical Framework and Resource Constraints

2018 ◽  
Vol 12 (4) ◽  
pp. 3877-3880 ◽  
Author(s):  
Liang Ren ◽  
Yingying Yu ◽  
Zhiqiang Cao ◽  
Zhiyong Wu ◽  
Junzhi Yu ◽  
...  
Keyword(s):  
Task Allocation ◽  
Large Scale ◽  
Resource Constraints ◽  
Robotic Systems ◽  
Hierarchical Framework ◽  
Allocation Approach

Kinematics of a Cable-Driven Robotic Platform for Large-Scale Additive Manufacturing

2021 ◽  
pp. 1-17
Author(s):  
Phillip Chesser ◽  
Peter Wang ◽  
Joshua Vaughan ◽  
Randall Lind ◽  
Brian Post
Keyword(s):  
Additive Manufacturing ◽  
Large Scale ◽  
Robotic Systems ◽  
Robotic Platform ◽  
Intended Application ◽  
Rigid Link ◽  
Physical Implementation

Abstract Concrete additive manufacturing (AM) is a growing field of research. However, on-site, large-scale concrete additive manufacturing requires motion platforms that are difficult to implement with conventional rigid-link robotic systems. This paper presents a new kinematic arrangement for a deployable cable-driven robot intended for on-site AM. The kinematics of this robot are examined to determine if they meet the requirements for this application, the wrench feasible workspace (WFW) is examined, and the physical implementation of a prototype is also presented. Data collected from the physical implementation of the proposed system is analyzed, and the results support its suitability for the intended application. The success of this system demonstrates that this kinematic arrangement is promising for future deployable AM systems.

Context-aware Adaptive Surgery

2021 ◽  
Vol 5 (3) ◽  
pp. 1-22
Author(s):  
Hongli Wang ◽  
Bin Guo ◽  
Jiaqi Liu ◽  
Sicong Liu ◽  
Yungang Wu ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Resource Constraints ◽  
Search Algorithm ◽  
Search Time ◽  
State Graph ◽  
Context Aware ◽  
Optimal Partition ◽  
Research Attention ◽  
Neighbor Effect

Deep Neural Networks (DNNs) have made massive progress in many fields and deploying DNNs on end devices has become an emerging trend to make intelligence closer to users. However, it is challenging to deploy large-scale and computation-intensive DNNs on resource-constrained end devices due to their small size and lightweight. To this end, model partition, which aims to partition DNNs into multiple parts to realize the collaborative computing of multiple devices, has received extensive research attention. To find the optimal partition, most existing approaches need to run from scratch under given resource constraints. However, they ignore that resources of devices (e.g., storage, battery power), and performance requirements (e.g., inference latency), are often continuously changing, making the optimal partition solution change constantly during processing. Therefore, it is very important to reduce the tuning latency of model partition to realize the real-time adaption under the changing processing context. To address these problems, we propose the Context-aware Adaptive Surgery (CAS) framework to actively perceive the changing processing context, and adaptively find the appropriate partition solution in real-time. Specifically, we construct the partition state graph to comprehensively model different partition solutions of DNNs by import context resources. Then "the neighbor effect" is proposed, which provides the heuristic rule for the search process. When the processing context changes, CAS adopts the runtime search algorithm, Graph-based Adaptive DNN Surgery (GADS), to quickly find the appropriate partition that satisfies resource constraints under the guidance of the neighbor effect. The experimental results show that CAS realizes adaptively rapid tuning of the model partition solutions in 10ms scale even for large DNNs (2.25x to 221.7x search time improvement than the state-of-the-art researches), and the total inference latency still keeps the same level with baselines.

scholarly journals Improving COVID-19 Testing Efficiency using Guided Agglomerative Sampling

2020 ◽  
Author(s):  
Fayyaz Minhas ◽  
Dimitris Grammatopoulos ◽  
Lawrence Young ◽  
Imran Amin ◽  
David Snead ◽  
...  
Keyword(s):  
Large Scale ◽  
Resource Constraints ◽  
Large Population ◽  
Sampling Strategy ◽  
Population Surveillance ◽  
Large Scale Testing ◽  
Scale Population ◽  
Simulation Results ◽  
The Given ◽  
Test Outcomes

AbstractOne of the challenges in the current COVID-19 crisis is the time and cost of performing tests especially for large-scale population surveillance. Since, the probability of testing positive in large population studies is expected to be small (<15%), therefore, most of the test outcomes will be negative. Here, we propose the use of agglomerative sampling which can prune out multiple negative cases in a single test by intelligently combining samples from different individuals. The proposed scheme builds on the assumption that samples from the population may not be independent of each other. Our simulation results show that the proposed sampling strategy can significantly increase testing capacity under resource constraints: on average, a saving of ~40% tests can be expected assuming a positive test probability of 10% across the given samples. The proposed scheme can also be used in conjunction with heuristic or Machine Learning guided clustering for improving the efficiency of large-scale testing further. The code for generating the simulation results for this work is available here: https://github.com/foxtrotmike/AS.

scholarly journals Reliability Based Task Allocation Analysis for Multi-Robot Systems by Using Fuzzy Logic

2018 ◽  
pp. 8-12
Author(s):  
Muhammed Oguz Tas ◽  
Ugur Yayan ◽  
Hasan Serhan Yavuz ◽  
Ahmet Yazici
Keyword(s):  
Fuzzy Logic ◽  
Task Allocation ◽  
System Reliability ◽  
Fuzzy Inference ◽  
Autonomous Robots ◽  
Robotic Systems ◽  
Inference System ◽  
Load Carrying ◽  
Robot Systems

Robotic systems are used many areas where it is dangerous or difficult for people to do. The importance of autonomous robots increased with the Industry 4.0, and the concept of reliability needed more attention for long term operability of robotic systems. In this study, reliability based task allocation analysis is performed for robots by using fuzzy logic. With the help of fuzzy inference system, the result of reliability based task allocation are obtained using the amount of carried load and load carrying distances. In the study, cases of task allocation based on nearest and reliability were analyzed and compared. Experimental results showed that, the system reliability that occurs with reliability based task allocation is higher than the system reliability that occurs with nearest based task allocation.

Proximate Mechanisms of Individual Decision-Making Behavior

2016 ◽  
Author(s):  
Paul W. Glimcher
Keyword(s):  
Twentieth Century ◽  
Large Scale ◽  
Resource Constraints ◽  
Empirical Work ◽  
Decision Makers ◽  
Decision Variable ◽  
Computational Resource ◽  
Human Decision ◽  
Holistic Theory ◽  
Decision Making Behavior

In the early twentieth century, neoclassical economic theorists began to explore mathematical models of maximization. The theories of human behavior that they produced explored how optimal human agents, who were subject to no internal computational resource constraints of any kind, should make choices. During the second half of the twentieth century, empirical work laid bare the limitations of this approach. Human decision makers were often observed to fail to achieve maximization in domains ranging from health to happiness to wealth. Psychologists responded to these failures by largely abandoning holistic theory in favor of large-scale multi-parameter models that retained many of the key features of the earlier models. Over the last two decades, scholars combining neurobiology, psychology, economics, and evolutionary approaches have begun to examine alternative theoretical approaches. Their data suggest explanations for some of the failures of neoclassical approaches and revealed new theoretical avenues for exploration. While neurobiologists have largely validated the economic and psychological assumption that decision makers compute and represent a single-decision variable for every option considered during choice, their data also make clear that the human brain faces severe computational resource constraints which force it to rely on very specific modular approaches to the processes of valuation and choice.

Distributed Coordination Architecture for Cooperative Task Planning and Execution of Intelligent Multi-Robot Systems

2015 ◽  
pp. 407-426 ◽  
Author(s):  
Gen'ichi Yasuda
Keyword(s):  
Large Scale ◽  
Discrete Event ◽  
Robotic Systems ◽  
Task Planning ◽  
Distributed Coordination ◽  
Robot Systems ◽  
Concurrent Processes ◽  
Manufacturing Applications ◽  
Robot Cooperation ◽  
Multi Robot

This chapter provides a practical and intuitive way of cooperative task planning and execution for complex robotic systems using multiple robots in automated manufacturing applications. In large-scale complex robotic systems, because individual robots can autonomously execute their tasks, robotic activities are viewed as discrete event-driven asynchronous, concurrent processes. Further, since robotic activities are hierarchically defined, place/transition Petri nets can be properly used as specification tools on different levels of control abstraction. Net models representing inter-robot cooperation with synchronized interaction are presented to achieve distributed autonomous coordinated activities. An implementation of control software on hierarchical and distributed architecture is presented in an example multi-robot cell, where the higher level controller executes an activity-based global net model of task plan representing cooperative behaviors performed by the robots, and the parallel activities of the associated robots are synchronized without the coordinator through the transmission of requests and the reception of status.

scholarly journals Responding to measles outbreaks in underserved Roma and Romanian populations in England: the critical role of community understanding and engagement

2020 ◽  
Vol 148 ◽  
Author(s):  
Sadie Bell ◽  
Vanessa Saliba ◽  
Gail Evans ◽  
Stephen Flanagan ◽  
Sam Ghebrehewet ◽  
...  
Keyword(s):  
Community Engagement ◽  
Large Scale ◽  
Resource Constraints ◽  
Critical Role ◽  
Health Spending ◽  
Disease Outbreaks ◽  
Underserved Communities ◽  
Outbreak Management ◽  
Qualitative Interview Study ◽  
Measles Outbreaks

Abstract Since 2016, the European Region has experienced large-scale measles outbreaks. Several measles outbreaks in England during 2017/18 specifically affected Romanian and Romanian Roma communities. In this qualitative interview study, we looked at the effectiveness of outbreak responses and efforts to promote vaccination uptake amongst these underserved communities in three English cities: Birmingham, Leeds and Liverpool. Semi-structured in-depth interviews were conducted with 33 providers involved in vaccination delivery and outbreak management in these cities. Interviews were analysed thematically and factors that influenced the effectiveness of responses were categorised into five themes: (1) the ability to identify the communities, (2) provider knowledge and understanding of the communities, (3) the co-ordination of response efforts and partnership working, (4) links to communities and approaches to community engagement and (5) resource constraints. We found that effective partnership working and community engagement were key to the prevention and management of vaccine-preventable disease outbreaks in the communities. Effective engagement was found to be compromised by cuts to public health spending and services for underserved communities. To increase uptake in under-vaccinated communities, local knowledge and engagement are vital to build trust and relationships. Local partners must work proactively to identify, understand and build connections with communities.

Sign in / Sign up

Export Citation Format

Share Document