A Graph Theoretic-Based Approach for Deploying Heterogeneous Multi-agent Systems with Application in Precision Agriculture

Davoodi, Mohammadreza; Faryadi, Saba; Velni, Javad Mohammadpour

doi:10.1007/s10846-020-01263-4

Cited by 15 publications

(5 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many system entities need to collectively coordinate with each other to make decisions online and collaboratively in this paradigm. Some examples of multi-agent robots are simultaneous localization and mapping (SLAM) [143], [144], [145], warehouse robotics [146], [147], [148], surgical robotics [149], [150], autonomous driving [151], [152], [153], agricultural robotics [154], [155], [156] etc. In these applications, each of the agents in a multi-agent system may be equipped with sensors like LiDAR, RGB and IR cameras, GPS receiver etc., which enable them to function autonomously.…”

Section: E Multi-agent Robotics Applicationsmentioning

confidence: 99%

Going Beyond RF: How AI-enabled Multimodal Beamforming will Shape the NextG Standard

Roy¹,

Salehi²,

Banou³

et al. 2022

Preprint

View full text Add to dashboard Cite

Incorporating artificial intelligence and machine learning (AI/ML) methods within the 5G wireless standard promises autonomous network behavior and ultra-low-latency reconfiguration. However, the effort so far has purely focused on learning from radio frequency (RF) signals. Future standards and next-generation (nextG) networks beyond 5G will have two significant evolutions over the state-of-the-art 5G implementations: (i) massive number of antenna elements, scaling up to hundreds-tothousands in number, and (ii) inclusion of AI/ML in the critical path of the network reconfiguration process that can access sensor feeds from a variety of RF and non-RF sources. While the former allows unprecedented flexibility in 'beamforming', where signals combine constructively at a target receiver, the latter enables the network with enhanced situation awareness not captured by a single and isolated data modality. This survey presents a thorough analysis of the different approaches used for beamforming today, focusing on mmWave bands, and then proceeds to make a compelling case for considering non-RF sensor data from multiple modalities, such as LiDAR, Radar, GPS for increasing beamforming directional accuracy and reducing processing time. This so called idea of multimodal beamforming will require deep learning based fusion techniques, which will serve to augment the current RF-only and classical signal processing methods that do not scale well for massive antenna arrays. The survey describes relevant deep learning architectures for multimodal beamforming, identifies computational challenges and the role of edge computing in this process, dataset generation tools, and finally, lists open challenges that the community should tackle to realize this transformative vision of the future of beamforming.

show abstract

Section: E Multi-agent Robotics Applicationsmentioning

confidence: 99%

Going Beyond RF: How AI-enabled Multimodal Beamforming will Shape the NextG Standard

Roy¹,

Salehi²,

Banou³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In [70], a monitoring application for precision agriculture using heterogeneous ground robots was presented. The approach followed was to use a weighted directed graph to represent the robot team.…”

Section: Ugv Robot Teams (Multi-ugv)mentioning

confidence: 99%

An Overview of Cooperative Robotics in Agriculture

et al. 2021

View full text Add to dashboard Cite

Agricultural robotics has been a popular subject in recent years from an academic as well as a commercial point of view. This is because agricultural robotics addresses critical issues such as seasonal shortages in manual labor, e.g., during harvest, as well as the increasing concern regarding environmentally friendly practices. On one hand, several individual agricultural robots have already been developed for specific tasks (e.g., for monitoring, spraying, harvesting, transport, etc.) with varying degrees of effectiveness. On the other hand, the use of cooperative teams of agricultural robots in farming tasks is not as widespread; yet, it is an emerging trend. This paper presents a comprehensive overview of the work carried out so far in the area of cooperative agricultural robotics and identifies the state-of-the-art. This paper also outlines challenges to be addressed in fully automating agricultural production; the latter is promising for sustaining an increasingly vast human population, especially in cases of pandemics such as the recent COVID-19 pandemic.

show abstract

“…In this case, the two combines harvest the field concurrently, yet independently, by following independent paths in counter-clockwise spiral shapes at some distance apart. On the other hand, in [ 26 ], a graphical theoretic approach was considered to plan and optimize the paths for robots in a heterogeneous robot team, where larger areas of the field were assigned to the more capable robots in the team for coverage during a monitoring task. Other approaches focus on the determination of the most efficient field coverage by multiple robots [ 27 , 28 , 29 , 30 , 31 ].…”

Section: Introductionmentioning

confidence: 99%

Coordinated Navigation of Two Agricultural Robots in a Vineyard: A Simulation Study

Lytridis

Bazinas

Pachidis

et al. 2022

Sensors

View full text Add to dashboard Cite

The development of an effective agricultural robot presents various challenges in actuation, localization, navigation, sensing, etc., depending on the prescribed task. Moreover, when multiple robots are engaged in an agricultural task, this requires appropriate coordination strategies to be developed to ensure safe, effective, and efficient operation. This paper presents a simulation study that demonstrates a robust coordination strategy for the navigation of two heterogeneous robots, where one robot is the expert and the second robot is the helper in a vineyard. The robots are equipped with localization and navigation capabilities so that they can navigate the environment and appropriately position themselves in the work area. A modular collaborative algorithm is proposed for the coordinated navigation of the two robots in the field via a communications module. Furthermore, the robots are also able to position themselves accurately relative to each other using a vision module in order to effectively perform their cooperative tasks. For the experiments, a realistic simulation environment is considered, and the various control mechanisms are described. Experiments were carried out to investigate the robustness of the various algorithms and provide preliminary results before real-life implementation.

show abstract

A Graph Theoretic-Based Approach for Deploying Heterogeneous Multi-agent Systems with Application in Precision Agriculture

Cited by 15 publications

References 40 publications

Going Beyond RF: How AI-enabled Multimodal Beamforming will Shape the NextG Standard

Going Beyond RF: How AI-enabled Multimodal Beamforming will Shape the NextG Standard

An Overview of Cooperative Robotics in Agriculture

Coordinated Navigation of Two Agricultural Robots in a Vineyard: A Simulation Study

Contact Info

Product

Resources

About