Deep multilayer neural framework for dynamic slip mitigation in omnidirectional mobile robots

Background and Problem: Omnidirectional mobile robots are a very diverse class of autonomous systems due to their holonomic motion capability in confined environments. However, wheel slip and surface roughness phenomena significantly reduce the accuracy of path tracking. Classical models often assume “pure rolling” conditions and neglect the effects of slip. Current approaches incorporate longitudinal and lateral slip coefficients into the dynamic equations, but designing accurate and robust controllers under slip conditions remains a practical challenge. 

Research Objective: This study presents a novel slip compensation and control framework for omnidirectional mobile robots that uses a multilayer neural network (MLNN) trained on high-fidelity simulation data (MSC ADAMS). The main goal is to achieve a lightweight, accurate and hardware-limited controller that can provide robust path tracking under variable friction conditions with minimal control energy and slip fluctuations. 

Methodology: Kinematics and dynamics modeling: Inverse and forward kinematic equations were derived considering longitudinal ((S_L)) and lateral ((S_T)) slip coefficients. Multilayer neural network: An MLNN with two hidden layers (16 and 12 neurons) and a linear output layer was designed. Training data: About 60,000 simulation examples of standard and complex paths (circle, zigzag, spiral, random wave) were generated under three different friction regimes. Training: The network was trained with the Levenberg–Marquardt algorithm and weight regularization and dropout techniques were used to improve generalizability. Evaluation: The network performance was measured on the test set with MSE and (R^2) metrics and compared with PD controllers, analytical slip-aware controllers, and other state-of-the-art methods. 

Key Results Prediction Accuracy: The network achieved MSE = 2.4 x 10^-4 and (R^2 > 0.995) for all outputs. Tracking Error Reduction: Compared to analytical slip-aware controllers, a reduction of up to 35% in the cumulative tracking error was observed. Slip Control: The slip ratio was stabilized within. Energy Consumption: The lowest total control energy consumption was recorded among all the compared methods. Correlation with Simulation Objectives: The network was able to reproduce the longitudinal and lateral slip dynamics with near-perfect correlation with the simulation objectives. Path Tracking: Even with sudden changes in direction, pose tracking was performed smoothly and accurately. 

Key Innovation Analytical Model Integration with Simulation Data: Explicit slip coefficients derived from the physical model were directly incorporated into the network training pipeline. No Need for Real Data: The network was trained solely on high-fidelity simulation data, eliminating the need for costly field data collection. Re-training-free portability: The trained controller can be deployed directly on the real system, maintaining stability and performance. Lightweight and suitable for limited hardware: The network architecture is designed to be cost-effective for robotic platforms with limited computing resources. 

Conclusion: The proposed framework demonstrates that a multilayer neural network trained on high-fidelity simulation data can act as a robust and efficient slip compensation controller in omnidirectional mobile robots. This approach not only significantly improves the path tracking accuracy, but also eliminates the need for field adjustments and real data. The results confirm the feasibility of deploying this controller in slip-prone industrial, service, and field scenarios, and provide a transferable, model-aware, and data-compatible solution.