.png){kind=icon}
Return
Modular Reconfigurable Robot Distributed Computing System for Tracking Multiple Objects
.png){kind=icon}
PRE
AI
DOI：10.1109/JSYST.2020.2990921.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
A modular reconfigurable robot (MRR) is considered as a system of systems; that is, a robot control system consists of modular systems. For an MRR, onboard tracking of multiple objects is one of the most challenging tasks. This is because the task requires lots of computation resources, but an MRR system is constrained by several physical limitations. To enhance the computing performance of an MRR control system, a novel approach to organizing a distributed computing system is proposed. The key components of the proposed approach are FPGA-based specialized computational cores (SCCs). An SCC for tracking objects is designed to utilize spare resources of the MRR module's control devices. Analysis and experiments on a real testbench are performed to illustrate that the proposed approach can enhance overall computing performance of an MRR control system without adding additional components. As such, weight and dimensional parameters are saved; unnecessary power consumption and heat generation are reduced. One experimental example shows that the proposed SCC enables a Kalman filter to track objects up to 100 times faster than a comparable solution implemented with microcontrollers, while the whole distributed computing system can outperform a similar one built on high-performance CPUs.
Keywords:
Task analysis
Robot sensing systems
Field programmable gate arrays
Cloud computing
Performance evaluation
Distributed computing
Ethernet POWERLINK
FPGA
Kalman filter
matrix computation
modular reconfigurable robot (MRR)
object tracking
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
I
IF:
2.4
Papers:
4.5K
Citations:
387
Organization
Cited Papers
Factors influencing decision making for healthcare services outsourcing: A review and Delphi study
OAAI
OAAIShareSaveShareSaveShareSaveShareSaveShareSaveShareSaveMOANA: An Online Learned Adaptive Appearance Model for Robust Multiple Object Tracking in 3D.png)
IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAIShareSaveShareSave
View more