A Distributed Robot Garden System

Sanneman, Lindsay; Ajilo, Deborah; DelPreto, Joseph; Mehta, Ankur; Miyashita, Shuhei; Poorheravi, Negin Abdolrahim; Yim, Sehyuk; Kim, Sangbae; Rus, Daniela

doi:10.1109/icra.2015.7140058

Cited by 5 publications

(5 citation statements)

References 17 publications

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“…L. Sanneman et al, in their paper, presents a multi-robot system capable of running autonomously or under user control from a simple graphical interface. Over 100 origami flowers are actuated with LEDs and printed pouch motors and are deployed in a modular array around additional swimming and crawling folded robots [12]. M. Johnson et al have developed a framework to provide a dynamic regulatory system for supporting coordination in human-robot teamwork [13].…”

Section: Related Workmentioning

confidence: 99%

ABB IRB 120-30.6 Build Procedure in RoboDK

Chakraborty

Aithal

2021

IJMTS

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Purpose: Research on robotics needs a robot to experiment on it. The actual industrial robot is costly. So, the only resort is to use a Robot simulator. The RoboDK is one of the best robot simulators now. It has covered most of the popular industrial robots. Its interface is straightforward. Just open the software, download the robot as we need, and start experiments. Up to that, no issue was found anywhere. However, the problem begins when we want to build the simulated robot by own. Lots of complexity arises like coordinate assignment, rotation not aligned, length mismatch, robot not synced with DH parameter. We begin to find some documents for making the robots. A few bits of the document are present. That is why we research it. After doing that, we prepared this paper for the researcher who wants to develop the simulated robot independently. This paper can be referenced for them. To minimize the complexity of our research, we study an industrial robot, ABB IRB 120-30.6. It is a good and popular robot. It is six degrees of freedom robot. We will use the specification and STEP file from their respective website and build a simulated robot from the STEP file for our research purpose. Design/Methodology/Approach: We will create a simulated robot from ABB IRB 120-30.6 STEP file. To create a robot by own, we took the help of the IRB 120 robot model. To demonstrate as simple as possible, we start with that robot whose default design is already present. We match and tune the joint coordinate based on robot parameters through this experiment. Findings/results: Here, we see how to create a custom robot. Using the IRB 120 robot model, we will create a robot model step by step. Furthermore, it will move it around its axis. Originality/Value: Using this experiment, the new researcher can get valuable information to create their custom robot. Paper Type: Simulation-based Research.

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Section: Related Workmentioning

confidence: 99%

ABB IRB 120-30.6 Build Procedure in RoboDK

Chakraborty

Aithal

2021

IJMTS

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“…Swarms of edge devices are increasing in number and size [13,17,25,30,31,32,34,35,42,43,65,67,68,68,78,81,104,116,122,125,129,131]. Swarms enable new applications, often with intermittent activity [?, 52,53,54,55,56,57,59,61,62,63,64,76,77,96,97,102,122,125,133], spanning accounting for people in disaster zones, to monitoring crops, and navigating self-driving vehicles.…”

Section: Introductionmentioning

confidence: 99%

A Hardware-Software Stack for Serverless Edge Swarms

Patterson¹,

Pigorovsky²,

Dempsey³

et al. 2021

Preprint

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Swarms of autonomous devices are increasing in ubiquity and size, making the need for rethinking their hardwaresoftware system stack critical.We present HiveMind, the first swarm coordination platform that enables programmable execution of complex task workflows between cloud and edge resources in a performant and scalable manner. HiveMind is a software-hardware platform that includes a domain-specific language to simplify programmability of cloud-edge applications, a program synthesis tool to automatically explore task placement strategies, a centralized controller that leverages serverless computing to elastically scale cloud resources, and a reconfigurable hardware acceleration fabric for network and remote memory accesses.We design and build the full end-to-end HiveMind system on two real edge swarms comprised of drones and robotic cars. We quantify the opportunities and challenges serverless introduces to edge applications, as well as the trade-offs between centralized and distributed coordination. We show that Hive-Mind achieves significantly better performance predictability and battery efficiency compared to existing centralized and decentralized platforms, while also incurring lower network traffic. Using both real systems and a validated simulator we show that HiveMind can scale to thousands of edge devices without sacrificing performance or efficiency, demonstrating that centralized platforms can be both scalable and performant.

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“…Swarms of autonomous edge devices are increasing in number, size, and popularity [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21]. From UAVs to self-driving cars and supply-chain robots, swarms are enabling new distributed applications, which often experience intermittent activity, and are interactive and latency-sensitive [1,2,10,11,22,23,24].…”

Section: Introductionmentioning

confidence: 99%

HiveMind: A Scalable and Serverless Coordination Control Platform for UAV Swarms

Hu,

Bruno,

Ritchken

et al. 2020

Preprint

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Swarms of autonomous devices are increasing in ubiquity and size. There are two main trains of thought for controlling devices in such swarms; centralized and distributed control. Centralized platforms achieve higher output quality but result in high network traffic and limited scalability, while decentralized systems are more scalable, but less sophisticated.In this work we present HiveMind, a centralized coordination control platform for IoT swarms that is both scalable and performant. HiveMind leverages a centralized cluster for all resource-intensive computation, deferring lightweight and timecritical operations, such as obstacle avoidance to the edge devices to reduce network traffic. HiveMind employs an event-driven serverless framework to run tasks on the cluster, guarantees fault tolerance both in the edge devices and serverless functions, and handles straggler tasks and underperforming devices. We evaluate HiveMind on a swarm of 16 programmable drones on two scenarios; searching for given items, and counting unique people in an area. We show that HiveMind achieves better performance and battery efficiency compared to fully centralized and fully decentralized platforms, while also handling load imbalances and failures gracefully, and allowing edge devices to leverage the cluster to collectively improve their output quality.

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A Distributed Robot Garden System

Cited by 5 publications

References 17 publications

ABB IRB 120-30.6 Build Procedure in RoboDK

ABB IRB 120-30.6 Build Procedure in RoboDK

A Hardware-Software Stack for Serverless Edge Swarms

HiveMind: A Scalable and Serverless Coordination Control Platform for UAV Swarms

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