论文部分内容阅读
当多机器人探索系统接受人类操作者的遥控时,其一方面要遵从操作者的意图,另一方面要满足网络连通性约束和提高通信效率等自身性能指标,如何协调两方面的关系,是本文研究的关键问题.在实时任务分配器中,针对实时性和人机决策协调问题,采用边界栅格采样率衰减和面向遥操作者兴趣的任务分配算法,使任务分配决策与人的意图相协调;在拓扑控制器中,对以Steiner点最少且非Steiner点的点权之和最大为优化目标的Steiner树优化问题进行求解,进而控制拓扑,使连通性约束得以保证;在地图融合中心选择器中,通过广度优先搜索选择地图融合中心,从而减小传输带宽和传输能量消耗.在典型室内环境中,与全自主系统Possible Moves Sampling进行了对比仿真,与未使用地图融合中心的探测系统进行了对比实验,结果表明,本系统具有更高、更稳定的探索效率和更小的数据传输量,体现了本文人机共享控制方法的有效性.
When the multi-robot exploration system accepts the human operator’s remote control, it must comply with the operator’s intention on the one hand and satisfy its performance index such as network connectivity constraints and communication efficiency on the other hand. How to coordinate the relationship between the two is In the real-time task allocator, aiming at the problems of real-time and man-machine decision coordination, this paper adopts the boundary raster sampling rate attenuation and the task assigning algorithm which is aimed at the remote operator to coordinate the task assignment decision with the human intention In the topology controller, we solve the Steiner tree optimization problem that maximizes the weighted sum of the weighted points with the least Steiner point and the non-Steiner point, and then controls the topology to ensure the connectivity constraint. In the Map Fusion Center Selector , We selected the map fusion center by breadth-first search to reduce the transmission bandwidth and transmission energy consumption.Compared with Possible Moves Sampling in a typical indoor environment, the simulation system was compared with the detection system that did not use the map fusion center Contrast experiments show that the system has higher and more stable exploration efficiency and smaller data transmission Volume, reflects the effectiveness of this method of human-machine sharing control.