Full Text:   <454>

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CLC number: TP182

On-line Access: 2019-01-30

Received: 2018-09-11

Revision Accepted: 2018-11-27

Crosschecked: 2019-01-08

Cited: 0

Clicked: 921

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Bin Xin

http://orcid.org/0000-0001-9989-0418

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Frontiers of Information Technology & Electronic Engineering  2019 Vol.20 No.1 P.18-31

http://doi.org/10.1631/FITEE.1800551


A-STC: auction-based spanning tree coverage algorithm for motion planning of cooperative robots


Author(s):  Guan-qiang Gao, Bin Xin

Affiliation(s):  School of Automation, Beijing Institute of Technology, Beijing 100081, China; more

Corresponding email(s):   3120170426@bit.edu.cn, brucebin@bit.edu.cn

Key Words:  Coverage motion planning, Multi-robot system, Auction algorithm, Spanning tree coverage algorithm


Guan-qiang Gao, Bin Xin. A-STC: auction-based spanning tree coverage algorithm for motion planning of cooperative robots[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(1): 18-31.

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Abstract: 
The multi-robot coverage motion planning (MCMP) problem in which every reachable area must be covered is common in multi-robot systems. To deal with the MCMP problem, we propose an efficient, complete, and off-line algorithm, named the “auction-based spanning tree coverage (A-STC)'' algorithm. First, the configuration space is divided into mega cells whose size is twice the minimum coverage range of a robot. Based on connection relationships among mega cells, a graph structure can be obtained. A robot that circumnavigates a spanning tree of the graph can generate a coverage trajectory. Then, the proposed algorithm adopts an auction mechanism to construct one spanning tree for each robot. In this mechanism, an auctioneer robot chooses a suitable vertex of the graph as an auction item from neighboring vertexes of its spanning tree by heuristic rules. A bidder robot submits a proper bid to the auctioneer according to the auction vertexes' relationships with the spanning tree of the robot and the estimated length of its trajectory. The estimated length is calculated based on vertexes and edges in the spanning tree. The bidder with the highest bid is selected as a winner to reduce the makespan of the coverage task. After auction processes, acceptable coverage trajectories can be planned rapidly. Computational experiments validate the effectiveness of the proposed MCMP algorithm and the method for estimating trajectory lengths. The proposed algorithm is also compared with the state-of-the-art algorithms. The comparative results show that the A-STC algorithm has apparent advantages in terms of the running time and the makespan for large crowded configuration spaces.

A-STC:一种基于拍卖的多机器人协同螺旋生成树覆盖运动规划方法

摘要:多机器人覆盖运动规划问题是多机器人领域常见问题,要求任务区域内每个点都被机器人传感器或者执行器覆盖一次。提出一种新的有效离线运动规划方法,即基于拍卖的螺旋生成树覆盖(A-STC)算法,解决多机器人覆盖运动规划问题。首先,将布局空间分解为机器人最小覆盖半径两倍的大栅格。基于大栅格之间的连通关系,生成一个无向连接图。机器人可利用巡航无向图中的生成树得到覆盖的运动轨迹。其次,A-STC算法采用拍卖机制构造每个机器人的生成树。在该拍卖机制下,每个机器人都可成为拍卖机器人和竞价机器人。拍卖机器人通过启发式规则从自身生成树的邻居节点中选择一个节点作为拍卖物品。竞价机器人根据拍卖节点的连通关系和运动轨迹的估计长度确定竞标价格。运动轨迹的估计长度主要取决于生成树中的节点和边的类型。最高出价的竞价机器人被选为中标机器人。拍卖过程后,可接受的覆盖运动轨迹可被快速规划生成。计算实验证明了A-STC算法和运动轨迹估计方法的有效性。将提出的算法与前沿算法进行比较,结果显示,所提运动规划方法运行时间和计算结果在大规模算例上具有明显优势。

关键词:覆盖运动规划;多机器人系统;拍卖算法;螺旋生成树覆盖算法

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