基于改进型自主发育网络的机器人场景识别方法

场景识别是移动机器人在陌生动态环境中完成任务的前提. 考虑到现有方法的不足, 本文提出了一种基于改进型自主发育网络的场景识别方法, 它通过引入基于多优胜神经元的Top-k竞争机制、基于负向学习的权值更新、基于连续性样本的加强型学习等步骤实现对场景的快速识别, 并使该方法具有更好的适应能力. 对于这种基于改进型自主发育网络的场景识别方法, 通过实验进行了对比测试. 结果表明, 这种改进型自主发育神经网络节点利用率高, 场景识别准确可靠, 可以较好地满足机器人作业的实际需求.

A Robot Scene Recognition Method Based on Improved Autonomous Developmental Network

Scene recognition is a prerequisite for mobile robots to complete tasks in unfamiliar dynamic environments. Considering the drawbacks of existing methods, this paper proposes a scene recognition method based on improved autonomous developmental network, which introduces important steps, such as Top-k competition mechanism based on multi-winning neurons, weight update based on negative learning, and reinforcement learning with continuous sample images, into the basic algorithm, so as to efficiently achieve scene recognition tasks with good adaptability to various environment. To test the performance of the scene recognition method based on improved autonomous developmental network, numerous comparative experiments are implemented, with the obtained results showing that the improved autonomous developmental neural network model presents such advantages as high node utilization rate, reliable scene recognition results, and it thus better meets the practical needs of robot operations.