面向无人环境的机器人生态圈自主任务决策方法

Autonomous task decision-making method of robot ecosystem for unmanned scenes

借鉴自然界生态系统的典型特征,提出机器人生态圈概念。通过使集群机器人进行智能协同与复杂演化,涌现自我维持、自我复制与自我进化等生命特征,实现无人条件下的长期生存、繁衍与进化,并执行特定的任务。针对机器人生态圈典型任务场景的自主任务决策需求,分析不同机器学习任务决策方法的特点,建立机器人生态圈自主任务决策的决策树模型和神经网络模型。分析表明,两种模型的正确率均在80%~90%,且均具有良好的稳定性。这说明,机器人生态圈自主任务决策问题可以通过决策树、神经网络等机器学习方法来很好地加以解决,从而为面向无人化场景的任务应用提供技术支持。

Based on the typical characteristics of natural ecosystems,the concept of robot ecosystem was proposed.Through the intelligent coordination and complex evolution of cluster robots,life features such as self-sustaining,self-replication and self-evolution emerged,enabling them to achieve long-term survival,reproduction and evolution under unmanned conditions,and perform specific tasks.According to the requirements of autonomous task decision-making in typical task scenarios of robot ecosystem,the characteristics of different machine learning task decision-making methods were analyzed,and the decision tree model and neural network model of autonomous task decision-making in robot ecosystem were established.The analysis shows that the accuracy of the two models is 80%~90%,and both have good stability.The results show that the autonomous task decision-making problem of robot ecosystem can be well solved by machine learning methods such as decision tree and neural network,so as to provide technical support for task application in unmanned scenes.