模式识别与人工智能
模式識彆與人工智能
모식식별여인공지능
Moshi Shibie yu Rengong Zhineng
2015年
4期
335-343
,共9页
多机器人%多目标观测%贡献模型%平均观测率
多機器人%多目標觀測%貢獻模型%平均觀測率
다궤기인%다목표관측%공헌모형%평균관측솔
Multi-robot%Observation of Multiple Moving Targets%Contribution Model%Average Observation Rate
如何在减少重叠观测现象的同时提高平均观测率是多机器人多目标观测的一个难题。文中提出基于贡献模型的多机器人多目标观测方法( C-CMOMMT),将机器人所观测的目标数记为贡献值,增加贡献值低的机器人所受的排斥力,扩大排斥力的作用距离,减小权重小的目标对贡献值高的机器人的吸引力,从而减少重叠观测现象。同时降低贡献值高的机器人所受到的排斥力,减轻排斥力的副作用,减少目标丢失现象,因此提高整体的平均观测率。为更系统地评价观测性能,建立由平均观测率、位置标准差和位置熵这3个因素构成的综合评价体系。仿真实验表明,相比A-CMOMMT和B-CMOMMT,C-CMOMMT可提高平均观测率,减少重叠观测现象,体现出较好的可行性和高效性。
如何在減少重疊觀測現象的同時提高平均觀測率是多機器人多目標觀測的一箇難題。文中提齣基于貢獻模型的多機器人多目標觀測方法( C-CMOMMT),將機器人所觀測的目標數記為貢獻值,增加貢獻值低的機器人所受的排斥力,擴大排斥力的作用距離,減小權重小的目標對貢獻值高的機器人的吸引力,從而減少重疊觀測現象。同時降低貢獻值高的機器人所受到的排斥力,減輕排斥力的副作用,減少目標丟失現象,因此提高整體的平均觀測率。為更繫統地評價觀測性能,建立由平均觀測率、位置標準差和位置熵這3箇因素構成的綜閤評價體繫。倣真實驗錶明,相比A-CMOMMT和B-CMOMMT,C-CMOMMT可提高平均觀測率,減少重疊觀測現象,體現齣較好的可行性和高效性。
여하재감소중첩관측현상적동시제고평균관측솔시다궤기인다목표관측적일개난제。문중제출기우공헌모형적다궤기인다목표관측방법( C-CMOMMT),장궤기인소관측적목표수기위공헌치,증가공헌치저적궤기인소수적배척력,확대배척력적작용거리,감소권중소적목표대공헌치고적궤기인적흡인력,종이감소중첩관측현상。동시강저공헌치고적궤기인소수도적배척력,감경배척력적부작용,감소목표주실현상,인차제고정체적평균관측솔。위경계통지평개관측성능,건립유평균관측솔、위치표준차화위치적저3개인소구성적종합평개체계。방진실험표명,상비A-CMOMMT화B-CMOMMT,C-CMOMMT가제고평균관측솔,감소중첩관측현상,체현출교호적가행성화고효성。
How to reduce the overlap observation phenomena and improve the average observation rate at the same time is a complicated problem of cooperative multi-robot observation of multiple moving targets. An approach based on contribution for cooperative multi-robot observation of multiple moving targets ( C-CMOMMT) is proposed. Each robot is endowed by the C-CMOMMT algorithm with a contribution value derived from the number of assigned targets to it. Robots with low contribution receive strengthened repulsive forces from all others. Besides, the operating distances of all repulsive forces are expanded, and robots with high contribution receive weakened attractive forces from low-weighted targets. With these three methods the overlap observation phenomena are reduced. To decrease the target loss, robots with high contribution receive feeble repulsive forces, and thus the side effects become weak. Consequently, the robots are decentralized and the overlap observing phenomena are dwindled. The average observation rate, the standard deviation and entropy of the positions of mobile robots are introduced to systematically evaluate the performance and the degree of overlap observing. Results show that C-CMOMMT improves the average observation rate and dwindles the overlap observing phenomena and it is more effective than A-CMOMMT and B-CMOMMT.