.png){kind=icon}
.png){kind=icon}
Return
Grouping-Based Optimization Method for Multirobot System Pattern Formation
.png){kind=icon}
PRE
AI
DOI：10.1109/JSYST.2021.3122548.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This article presents a novel optimization method for multirobot formation in an obstacle environment. For this challenge, we proposed an iterative optimization approach in previous work, in which all robots as a whole obtain the optimal goal pattern, and then, move to the goal without collision to form the pattern. However, this approach results in high consumption in terms of time and the path that robots travel as the number of robots increases. To improve efficiency, we propose a grouping-based optimization method. First, a specific grouping strategy (the number of groups and the number of data points in each group are fixed) is utilized to group the multiple robots. And then in an obstacle environment, each group of robots completes its optimal pattern formation in parallel without collision through coordination within and between groups. The simulation results of the multiletter pattern formation validate the effectiveness of the method proposed in this article compared to the method without grouping.
Keywords:
Robot kinematics
Pattern formation
Collision avoidance
Optimization methods
Clustering methods
Task analysis
Swarm robotics
Grouping
mixed integer convex quadratic programming
multirobot system
pattern formation
Journal
I
IF:
2.4
Papers:
4.5K
Citations:
387