基于种群编码和因子图的离散信念传播网络用于移动机器人的运动控制

Indar Sugiarto, J. Conradt
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引用次数: 8

摘要

本文通过对全向移动机器人的运动学计算,提出了一种因子图形式的概率图模型,用于进行分层概率推理。受神经元信息处理的启发,我们提出应用种群编码原理对因子图内传递的信息进行编码,以更新网络的内部信念。本文研究了两种推理场景:第一种是利用全向移动机器人的真实数据进行单轮电机控制;其次是机器人的速度和方向在现实世界的坐标使用模拟数据。第一种场景的实验结果表明,因子图几乎可以完美地学习输入输出关系,第二种场景的仿真结果表明,因子图中选择的模型对推理过程中由于噪声引起的干扰具有很强的鲁棒性。本研究的结果可以应用于建立在该基本运动学系统之上的更复杂的智能任务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Discrete belief propagation network using population coding and factor graph for kinematic control of a mobile robot
This paper presents a probabilistic graphical model in the form of a factor graph to perform hierarchical probabilistic inference by computing kinematics of an omnidirectional mobile robot. We propose applying population coding principles to encode messages transmitted within the factor graph to update the network's internal belief, as inspired by neuronal information processing. We examine two inference scenarios in this paper: first for single wheel motor control using real data from an omnidirectional mobile robot; and second for the robot's velocity and orientation in real-world coordinates using simulation data. The experimental results for the first scenario show that the factor graph can learn input-output relations almost perfectly and the simulation results for the second scenario demonstrate that the selected model in the factor graph is quite robust against disturbances due to noise during inference. The results of this study can be applied in more complex intelligence tasks, which build on top of this basic kinematics system.
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