2016
DOI: 10.47839/ijc.15.3.849
|View full text |Cite
|
Sign up to set email alerts
|

Ub Swarm: Hardware Implementation of Heterogeneous Swarm Robot With Fault Detection and Power Management

Abstract: In this work we present the hardware architecture of a mobile heterogeneous robot swarm, designed and implemented at the Interdisciplinary Robotics, Intelligent Sensing and Control (RISC) Laboratory, University of Bridgeport. Most of the recent advances in swarm robotics have mainly focused on homogeneous robot swarms and their applications. Developing and coordinating a multi-agent robot system with heterogeneity and a larger behavioral repertoire is a great challenge. To give swarm hardware heterogeneity we … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1

Citation Types

0
3
0

Year Published

2018
2018
2024
2024

Publication Types

Select...
4
2
1

Relationship

0
7

Authors

Journals

citations
Cited by 14 publications
(3 citation statements)
references
References 22 publications
0
3
0
Order By: Relevance
“…The focus was to build heterogeneous swarms. The UB-swarm project [180] also built a swarm of heterogeneous robots. Thus, in our opinion we suggest some software requirements include the implementation of:…”
Section: A Hardware and Software Issuesmentioning
confidence: 99%
“…The focus was to build heterogeneous swarms. The UB-swarm project [180] also built a swarm of heterogeneous robots. Thus, in our opinion we suggest some software requirements include the implementation of:…”
Section: A Hardware and Software Issuesmentioning
confidence: 99%
“…The use of a dedicated sensor is shown in [7][8][9]. These decisions are difficult to implement for RP because of the lack of natural optical templates in fields during low flying, and a large amount of power supply for the test sites lighting.…”
Section: Relative Workmentioning
confidence: 99%
“…Flow chart of the process of microplasma spraying by an intelligent robotic system Thus, the trajectory plan must be generated for the thermal spraying process without any initial knowledge of the products shape or orientation. The closest to the main ideas of this study are the different algorithms developed by Kondratenko et al [16,17], Tkachenko et al [18] and Patil et al [19] for the operation of an intelligent robot in an environment with uncertainty. In this case, the trajectory is formed by the robot control system based on information about the current state of the external environment, that is, according to the 3D model of the processed surface reconstructed by the robot, which is a point cloud (coordinates of the object surface).…”
Section: Introductionmentioning
confidence: 99%