基于结构自适应的多机器人协作机制研究

Strategy of multi-robot coordination based on structural self-organization

未知环境下的多机器人合作是一个复杂的控制问题。它的解决方案要求在机器人内部任务目标与机器人之间的任务目标间保证有适当的均衡,而机器人间的协作是多机器人系统高效工作的关键。证明了一种分布式的结构自适应的组织模型在MAS中是鲁棒的和高效的。基于自组织的原则,在执行任务时机器人可以通过任务分配实时改变组织结构。各机器人之间通过任务分配的关系记录来确定下一步的关系。实验证明:此方法能接近到集中式控制方法的上界,优于静态的和随机的任务分配方法。

It is a complex control problem for multi-robot coordination in unknown environment. The solution requires an appropriate balance between the task object inside robot and task object between robots, while the collaboration in the multi-robots systems is the most important problem for its efficient work. A robust, decentralized approach for structural adaptation in MAS is demonstrated. Based on self-organization principles,the method enables robots to modify their structural relations. And the robots adopt their next-step relationship by tasks allocation which has finished. It is shown that, the performance of self-organization is close to that of an upper bound centralized allocation methods and better than a static method and a random adaptation method.