.png){kind=icon}
.png){kind=icon}
Return
Computation Sharing in Distributed Robotic Systems: A Case Study on SLAM
.png){kind=icon}
PRE
AI
DOI：10.1109/TASE.2014.2357216.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Aiming at increasing team efficiency, mobile robots may act as a node of a Robotic Cluster to assist their teammates in computationally demanding tasks. Having this in mind, we propose two distributed architectures for the Simultaneous Localization And Mapping (SLAM) problem, our main case study. The analysis focuses especially on the efficiency gain that can be obtained. It is shown that the proposed architectures enable us to raise the workload up to values that would not be possible in a single robot solution, thus gaining in localization precision and map accuracy. Furthermore, we assess the impact of network bandwidth. All the results are extracted from frequently used SLAM datasets available in the robotics community and a real world testbed is described to show the potential of using the proposed philosophy. Note to Practitioners-Multirobot systems commonly suffer from limited computational resources, which interferes with the ability of each individual robot to finish the task underway. Generally in these scenarios, the use of the available computational power varies over time. Hence, based on the Robotic Clusters concept, we propose to allocate computational resources available in the multirobot system to solve computationally hard problems arising in the real world. As a case study, we propose two architectures for a solidary multirobot system engaged in a SLAM task. In this paper, we thoroughly discuss the tradeoffs of the architectures proposed in terms of efficiency, complexity, exchange of data, load balancing and SLAM performance.
Keywords:
Distributed computing
networked robots
robotic clusters
simultaneous localization and mapping
task sharing in multirobot systems
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.4
Papers:
4.9K
Citations:
1.6W