Guffanti Diego, Mora Murillo Moisés Filiberto, Hinojosa Marco Alejandro, Bustamante Sanchez Santiago, Obregón Gutiérrez Javier Oswaldo, Gutiérrez Nelson, Sánchez Miguel
Universidad UTE, Av. Mariscal Sucre, Quito 170129, Ecuador.
Departamento de Mecánica y Ciencias Exactas, Instituto Superior Universitario Japón, Santo Domingo 230102, Ecuador.
Sensors (Basel). 2025 Jun 5;25(11):3553. doi: 10.3390/s25113553.
Precise modeling of differential drive robots is crucial for effective control and trajectory planning in autonomous systems. A comparative analysis of two modeling approaches for a four-wheel differential drive robot is presented in this paper. The first approach, named Motor-Based Model (MBM), identifies four transfer functions, one for each motor, while the second approach, named Simplified Model (SM), uses only two transfer functions, one for linear velocity and another for angular velocity. Both models were validated by comparing their predicted trajectories against real odometry data obtained from a SLAM system implemented on a differential-drive robot. This provided a practical assessment of each model's accuracy and underscored the importance of model selection in control design and navigation tasks. The results showed that the Motor-Based Model (MBM) consistently outperformed the Simplified Model (SM) in terms of odometry accuracy, both in position and orientation. Across all trajectories, the average RMSE for position using MBM was 0.309 m, while the SM recorded a higher average RMSE of 0.414 m. Similarly, the maximum position error averaged 0.522 m for MBM and 0.710 m for SM, confirming that MBM is more accurate and consistent in position tracking. Regarding the results of orientation estimation, when averaged across all experiments, the MBM maintained a lower angular RMSE of 0.170 rad in contrast to SM, which achieves an RMSE of 0.239 rad. The maximum angular error was also higher for the MBM at 0.316 rad, compared to 0.447 rad for the SM. Moreover, the computational performance evaluation indicated that the SM consistently outperformed MBM, achieving a 30% reduction in simulation time and substantially lower memory usage. These results demonstrate the relationship between model complexity and accuracy and suggest that the motor-specific model is more appropriate for applications requiring precise mapping or localization, such as SLAM, while the simplified model may be suitable for simpler use cases with lower computational requirements, such as embedded systems with limited resources. This paper provides a practical evaluation of the accuracy and computational performance of two modeling approaches, highlighting the implications of model selection for the design of navigation tasks.
精确建模差动驱动机器人对于自主系统中的有效控制和轨迹规划至关重要。本文对四轮差动驱动机器人的两种建模方法进行了比较分析。第一种方法称为基于电机的模型（MBM），识别四个传递函数，每个电机一个，而第二种方法称为简化模型（SM），仅使用两个传递函数，一个用于线速度，另一个用于角速度。通过将预测轨迹与从差动驱动机器人上实现的SLAM系统获得的实际里程计数据进行比较，对这两种模型进行了验证。这为每个模型的准确性提供了实际评估，并强调了模型选择在控制设计和导航任务中的重要性。结果表明，基于电机的模型（MBM）在里程计精度方面，无论是位置还是方向，始终优于简化模型（SM）。在所有轨迹上，使用MBM的位置平均均方根误差（RMSE）为0.309米，而SM的平均RMSE更高，为0.414米。同样，MBM的最大位置误差平均为0.522米，SM为0.710米，证实了MBM在位置跟踪方面更准确和一致。关于方向估计结果，在所有实验中平均计算时，MBM的角RMSE保持在较低的0.170弧度，而SM的RMSE为0.239弧度。MBM的最大角误差也更高，为0.316弧度，而SM为0.447弧度。此外，计算性能评估表明，SM始终优于MBM，模拟时间减少了30%，内存使用量也大幅降低。这些结果证明了模型复杂性与准确性之间的关系，并表明特定于电机的模型更适合需要精确映射或定位的应用，如SLAM，而简化模型可能适用于计算要求较低的简单用例，如资源有限的嵌入式系统。本文对两种建模方法的准确性和计算性能进行了实际评估，突出了模型选择对导航任务设计的影响。
