El-Shenawy Ahmed K, ElSaharty M A, Zakzouk Ezz Eldin
Arab Academy for Science, Technology and Maritime Transport, Electric and Control Department, College of Engineering and Technology, Alexandria, Egypt.
PLoS One. 2017 Jan 3;12(1):e0169036. doi: 10.1371/journal.pone.0169036. eCollection 2017.
Trajectory tracking of mobile wheeled chairs using internal shaft encoder and inertia measurement unit(IMU), exhibits several complications and accumulated errors in the tracking process due to wheel slippage, offset drift and integration approximations. These errors can be realized when comparing localization results from such sensors with a camera tracking system. In long trajectory tracking, such errors can accumulate and result in significant deviations which make data from these sensors unreliable for tracking. Meanwhile the utilization of an external camera tracking system is not always a feasible solution depending on the implementation environment. This paper presents a novel sensor fusion method that combines the measurements of internal sensors to accurately predict the location of the wheeled chair in an environment. The method introduces a new analogical OR gate structured with tuned parameters using multi-layer feedforward neural network denoted as "Neuro-Analogical Gate" (NAG). The resulting system minimize any deviation error caused by the sensors, thus accurately tracking the wheeled chair location without the requirement of an external camera tracking system. The fusion methodology has been tested with a prototype Mecanum wheel-based chair, and significant improvement over tracking response, error and performance has been observed.
使用内轴编码器和惯性测量单元（IMU）对移动轮椅进行轨迹跟踪时，由于车轮打滑、偏移漂移和积分近似，在跟踪过程中会出现一些复杂情况和累积误差。将此类传感器的定位结果与摄像头跟踪系统进行比较时，这些误差就会显现出来。在长轨迹跟踪中，此类误差会累积并导致显著偏差，从而使这些传感器的数据在跟踪时不可靠。同时，根据实现环境，使用外部摄像头跟踪系统并不总是可行的解决方案。本文提出了一种新颖的传感器融合方法，该方法结合内部传感器的测量结果，以准确预测轮椅在环境中的位置。该方法引入了一种新的类比或门，其结构采用了使用多层前馈神经网络调谐的参数，称为“神经类比门”（NAG）。由此产生的系统将传感器引起的任何偏差误差降至最低，从而无需外部摄像头跟踪系统即可准确跟踪轮椅位置。该融合方法已在基于麦克纳姆轮的原型轮椅上进行了测试，并观察到在跟踪响应、误差和性能方面有显著改进。
