摘要
针对具有松散和紧密耦合型任务的大规模多机器人系统,研究了基于群体智能的任务分配方法。系统采用层次结构,高层用蚁群算法实现松散耦合型任务分配的寻优,提出逆转分配思想让蚂蚁代表任务,为每个任务选择任务的承担者。底层分别提出了基于蚁群、粒子群蚁群和量子蚁群实现机器人联盟的形成——产生紧耦合型任务解,并进行仿真。仿真结果表明,基本蚁群算法得到的解质量最差;粒子群蚁群算法得到的分配解最好,但是运算时间最长;量子蚁群算法得到的解稍次于粒子群蚁群算法,但分配时间比另两种算法减少了一半。因此,在大规模的多机器人任务分配中,量子蚁群算法具有更强的适用性。
The task allocation was studied based on the swarm inteeligence for the large-scale multi- robot system with loose-and tight-coupled tasks adopting the hierarchial architecture. In the high level, the ant colony algorithm was employed to find the optimal allocation of the loose-coupled tasks, namely, based on the reverse distribution idea, taking each ant to form a task, an undertaker was chosen for every task. In the low level, the coalition formation algorithms based on the ant colony optimization(ACO), the particle swarm and ant colomy optimization(PSACO), or the quantum-inspised ant colony optimization (QACO) was proposed respectively for performin a tight-coupled task. Simulations were performed and results showed that PSACO provides the best solution, but its running time is the largest; QACO is a little inferior in solution quality to PSACO, however,it needs only a half time of the 2 other methods. Therefore, QACO appears more suitable for the task allocation of the large-scale multi-robot system.
出处
《吉林大学学报(工学版)》
EI
CAS
CSCD
北大核心
2010年第1期123-129,共7页
Journal of Jilin University:Engineering and Technology Edition
基金
国家自然科学基金项目(60573067)
关键词
自动控制技术
任务分配
机器人联盟形成
蚁群优化
粒子群蚁群优化
量子蚁群优化
automatic control technology
task allocation
robot coalition formation
ant colony optimization
particle swarm and ant colony optimization
quantum-inspired ant colony optimization