scholarly journals Behavior-Based Formation Control of Swarm Robots

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Dongdong Xu ◽  
Xingnan Zhang ◽  
Zhangqing Zhu ◽  
Chunlin Chen ◽  
Pei Yang
Keyword(s):  
Formation Control ◽  
Large Scale ◽  
Research Field ◽  
Initial Formation ◽  
Swarm Robots ◽  
Robot Systems ◽  
Potential Applications ◽  
Behavior Based ◽  
Swarm Robot ◽  
Searching Method

Swarm robotics is a specific research field of multirobotics where a large number of mobile robots are controlled in a coordinated way. Formation control is one of the most challenging goals for the coordination control of swarm robots. In this paper, a behavior-based control design approach is proposed for two kinds of important formation control problems: efficient initial formation and formation control while avoiding obstacles. In this approach, a classification-based searching method for generating large-scale robot formation is presented to reduce the computational complexity and speed up the initial formation process for any desired formation. The behavior-based method is applied for the formation control of swarm robot systems while navigating in an unknown environment with obstacles. Several groups of experimental results demonstrate the success of the proposed approach. These methods have potential applications for various swarm robot systems in both the simulation and the practical environments.

Sharing Experience for Behavior Generation of Real Swarm Robot Systems Using Deep Reinforcement Learning

2019 ◽  
Vol 31 (4) ◽  
pp. 520-525 ◽  
Author(s):  
Toshiyuki Yasuda ◽  
Kazuhiro Ohkura ◽  
◽  
Keyword(s):  
Reinforcement Learning ◽  
Collective Behavior ◽  
Design Methodology ◽  
Learning Performance ◽  
Centralized Control ◽  
Robot System ◽  
Swarm Robots ◽  
Robot Systems ◽  
Swarm Robot ◽  
Typical Design

Swarm robotic systems (SRSs) are a type of multi-robot system in which robots operate without any form of centralized control. The typical design methodology for SRSs comprises a behavior-based approach, where the desired collective behavior is obtained manually by designing the behavior of individual robots in advance. In contrast, in an automatic design approach, a certain general methodology is adopted. This paper presents a deep reinforcement learning approach for collective behavior acquisition of SRSs. The swarm robots are expected to collect information in parallel and share their experience for accelerating their learning. We conducted real swarm robot experiments and evaluated the learning performance of the swarm in a scenario where the robots consecutively traveled between two landmarks.

APPLYING A TAXONOMY OF FORMATION CONTROL IN DEVELOPING A ROBOTIC SYSTEM

2007 ◽  
Vol 16 (04) ◽  
pp. 565-582 ◽  
Author(s):  
HARRY CHIA-HUNG HSU ◽  
ALAN LIU
Keyword(s):  
Formation Control ◽  
Expert Knowledge ◽  
Research Field ◽  
Robotic Control ◽  
Know How ◽  
Reference Type ◽  
Robot Systems ◽  
Formation Systems ◽  
Formation System

Designing cooperative multi-robot systems (MRS) requires expert knowledge both in control and artificial intelligence. Formation control is an important research within the research field of MRS. Since many researchers use different ways in approaching formation control, we try to give a taxonomy in order to help researchers design formation systems in a systematical way. We can analyze formation structures in two categories: control abstraction and robot distinguishability. The control abstraction can be divided into three layers: formation shape, reference type, and robotic control. Furthermore, robots can be classified as anonymous robots or identification robots depending on whether robots are distinguishable according to their inner states. We use this taxonomy to analyze some ground-based formation systems and to state current challenges of formation control. Such information becomes the design know-how in developing a formation system, and a case study of designing a multi-team formation system is introduced to demonstrate the usefulness of the taxonomy.

scholarly journals Generating Legible and Glanceable Swarm Robot Motion through Trajectory, Collective Behavior, and Pre-attentive Processing Features

2021 ◽  
Vol 10 (3) ◽  
pp. 1-25
Author(s):  
Lawrence H. Kim ◽  
Sean Follmer
Keyword(s):  
Swarm Intelligence ◽  
Collective Behavior ◽  
Optimal Solution ◽  
Robot Motion ◽  
Vision Science ◽  
Swarm Robots ◽  
Different Types ◽  
Behavior Based ◽  
Swarm Robot

As swarm robots begin to share the same space with people, it is critical to design legible swarm robot motion that clearly and rapidly communicates the intent of the robots to nearby users. To address this, we apply concepts from intent-expressive robotics, swarm intelligence, and vision science. Specifically, we leverage the trajectory, collective behavior, and density of swarm robots to generate motion that implicitly guides people’s attention toward the goal of the robots. Through online evaluations, we compared different types of intent-expressive motions both in terms of legibility as well as glanceability, a measure we introduce to gauge an observer’s ability to predict robots’ intent pre-attentively. The results show that the collective behavior-based motion has the best legibility performance overall, whereas, for glanceability, trajectory-based legible motion is most effective. These results suggest that the optimal solution may involve a combination of these legibility cues based on the scenario and the desired properties of the motion.

scholarly journals The NSB control: a behavior-based approach for multi-robot systems

2010 ◽  
Vol 1 (1) ◽  
Author(s):  
Gianluca Antonelli ◽  
Filippo Arrichiello ◽  
Stefano Chiaverini
Keyword(s):  
Formation Control ◽  
Ad Hoc ◽  
Null Space ◽  
Robotic Systems ◽  
Lower Priority ◽  
Robot Systems ◽  
Mobile Ad Hoc ◽  
Behavior Based ◽  
Hoc Networks ◽  
Multi Robot

AbstractThe paper presents an overview on the use of a behavior-based approach, namely the Null-Space-based Behavioral (NSB) approach, to control multi-robot systems in a wide application domain. The NSB approach has been recently developed to control the motion of generic robotic systems; it uses a projection mechanism to combine the multiple, prioritized, behaviors that compose the robotic mission so that the lower priority behaviors do not effect the higher priority ones. In this paper we describe how the NSB approach has been used to control different multi-robot systems (e.g., composed of wheeled and marine robots) to achieve missions such as formation control, entrapping/escorting of targets, control of mobile ad-hoc networks, flocking, border patrol and cooperative caging.

scholarly journals Immunomodulatory Nanomedicine for the Treatment of Atherosclerosis

2021 ◽  
Vol 10 (14) ◽  
pp. 3185
Author(s):  
Linsey J. F. Peters ◽  
Alexander Jans ◽  
Matthias Bartneck ◽  
Emiel P. C. van der Vorst
Keyword(s):  
Cell Proliferation ◽  
Drug Carriers ◽  
Research Field ◽  
General Introduction ◽  
Cellular Receptors ◽  
Cellular Processes ◽  
Potential Applications ◽  
Current Therapies ◽  
Based Medicine ◽  
Physiological Systems

Atherosclerosis is the main underlying cause of cardiovascular diseases (CVDs), which remain the number one contributor to mortality worldwide. Although current therapies can slow down disease progression, no treatment is available that can fully cure or reverse atherosclerosis. Nanomedicine, which is the application of nanotechnology in medicine, is an emerging field in the treatment of many pathologies, including CVDs. It enables the production of drugs that interact with cellular receptors, and allows for controlling cellular processes after entering these cells. Nanomedicine aims to repair, control and monitor biological and physiological systems via nanoparticles (NPs), which have been shown to be efficient drug carriers. In this review we will, after a general introduction, highlight the advantages and limitations of the use of such nano-based medicine, the potential applications and targeting strategies via NPs. For example, we will provide a detailed discussion on NPs that can target relevant cellular receptors, such as integrins, or cellular processes related to atherogenesis, such as vascular smooth muscle cell proliferation. Furthermore, we will underline the (ongoing) clinical trials focusing on NPs in CVDs, which might bring new insights into this research field.

scholarly journals C4 Bacterial Volatiles Improve Plant Health

Pathogens ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 682
Author(s):  
Bruno Henrique Silva Dias ◽  
Sung-Hee Jung ◽  
Juliana Velasco de Castro Oliveira ◽  
Choong-Min Ryu
Keyword(s):  
Plant Growth ◽  
Volatile Compounds ◽  
Large Scale ◽  
Growth Promotion ◽  
Plant Growth Promoting Rhizobacteria ◽  
Plant Health ◽  
Systemic Resistance ◽  
Potential Applications ◽  
Plant Growth Promoting ◽  
Bacterial Volatiles

Plant growth-promoting rhizobacteria (PGPR) associated with plant roots can trigger plant growth promotion and induced systemic resistance. Several bacterial determinants including cell-wall components and secreted compounds have been identified to date. Here, we review a group of low-molecular-weight volatile compounds released by PGPR, which improve plant health, mostly by protecting plants against pathogen attack under greenhouse and field conditions. We particularly focus on C4 bacterial volatile compounds (BVCs), such as 2,3-butanediol and acetoin, which have been shown to activate the plant immune response and to promote plant growth at the molecular level as well as in large-scale field applications. We also disc/ uss the potential applications, metabolic engineering, and large-scale fermentation of C4 BVCs. The C4 bacterial volatiles act as airborne signals and therefore represent a new type of biocontrol agent. Further advances in the encapsulation procedure, together with the development of standards and guidelines, will promote the application of C4 volatiles in the field.

scholarly journals Review of Cryogenic Carbon Capture Innovations and Their Potential Applications

2021 ◽  
Vol 7 (3) ◽  
pp. 58
Author(s):  
Carolina Font-Palma ◽  
David Cann ◽  
Chinonyelum Udemu
Keyword(s):  
Carbon Dioxide Emissions ◽  
Carbon Capture ◽  
Large Scale ◽  
Carbon Capture And Storage ◽  
Technological Readiness ◽  
Cryogenic Carbon Capture ◽  
Potential Applications ◽  
Air Capture ◽  
Reduce Carbon Dioxide ◽  
Co2 Recovery

Our ever-increasing interest in economic growth is leading the way to the decline of natural resources, the detriment of air quality, and is fostering climate change. One potential solution to reduce carbon dioxide emissions from industrial emitters is the exploitation of carbon capture and storage (CCS). Among the various CO2 separation technologies, cryogenic carbon capture (CCC) could emerge by offering high CO2 recovery rates and purity levels. This review covers the different CCC methods that are being developed, their benefits, and the current challenges deterring their commercialisation. It also offers an appraisal for selected feasible small- and large-scale CCC applications, including blue hydrogen production and direct air capture. This work considers their technological readiness for CCC deployment and acknowledges competing technologies and ends by providing some insights into future directions related to the R&D for CCC systems.

scholarly journals Transformer Meets Convolution: A Bilateral Awareness Network for Semantic Segmentation of Very Fine Resolution Urban Scene Images

2021 ◽  
Vol 13 (16) ◽  
pp. 3065
Author(s):  
Libo Wang ◽  
Rui Li ◽  
Dongzhi Wang ◽  
Chenxi Duan ◽  
Teng Wang ◽  
...  
Keyword(s):  
Large Scale ◽  
Texture Features ◽  
Semantic Segmentation ◽  
Autonomous Driving ◽  
Research Field ◽  
Learning Approaches ◽  
Fine Grained ◽  
Urban Scene ◽  
Fine Resolution ◽  
With Memory

Semantic segmentation from very fine resolution (VFR) urban scene images plays a significant role in several application scenarios including autonomous driving, land cover classification, urban planning, etc. However, the tremendous details contained in the VFR image, especially the considerable variations in scale and appearance of objects, severely limit the potential of the existing deep learning approaches. Addressing such issues represents a promising research field in the remote sensing community, which paves the way for scene-level landscape pattern analysis and decision making. In this paper, we propose a Bilateral Awareness Network which contains a dependency path and a texture path to fully capture the long-range relationships and fine-grained details in VFR images. Specifically, the dependency path is conducted based on the ResT, a novel Transformer backbone with memory-efficient multi-head self-attention, while the texture path is built on the stacked convolution operation. In addition, using the linear attention mechanism, a feature aggregation module is designed to effectively fuse the dependency features and texture features. Extensive experiments conducted on the three large-scale urban scene image segmentation datasets, i.e., ISPRS Vaihingen dataset, ISPRS Potsdam dataset, and UAVid dataset, demonstrate the effectiveness of our BANet. Specifically, a 64.6% mIoU is achieved on the UAVid dataset.

scholarly journals Quantifying the effects of passenger-level heterogeneity on transit journey times

2020 ◽  
Vol 1 ◽  
Author(s):  
Ramandeep Singh ◽  
Daniel J. Graham ◽  
Richard J. Anderson
Keyword(s):  
Large Scale ◽  
Semiparametric Regression ◽  
Related Factors ◽  
Network Characteristics ◽  
Journey Time ◽  
Time Variance ◽  
Assignment Algorithm ◽  
Rail Systems ◽  
Urban Rail ◽  
Potential Applications

Abstract In this paper, we apply flexible data-driven analysis methods on large-scale mass transit data to identify areas for improvement in the engineering and operation of urban rail systems. Specifically, we use data from automated fare collection (AFC) and automated vehicle location (AVL) systems to obtain a more precise characterisation of the drivers of journey time variance on the London Underground, and thus an improved understanding of delay. Total journey times are decomposed via a probabilistic assignment algorithm, and semiparametric regression is undertaken to disentangle the effects of passenger-specific travel characteristics from network-related factors. For total journey times, we find that network characteristics, primarily train speeds and headways, represent the majority of journey time variance. However, within the typically twice as onerous access and egress time components, passenger-level heterogeneity is more influential. On average, we find that intra-passenger heterogeneity represents 6% and 19% of variance in access and egress times, respectively, and that inter-passenger effects have a similar or greater degree of influence than static network characteristics. The analysis shows that while network-specific characteristics are the primary drivers of journey time variance in absolute terms, a nontrivial proportion of passenger-perceived variance would be influenced by passenger-specific characteristics. The findings have potential applications related to improving the understanding of passenger movements within stations, for example, the analysis can be used to assess the relative way-finding complexity of stations, which can in turn guide transit operators in the targeting of potential interventions.

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