# Design and Control of an Omnidirectional Mobile Wall-Climbing Robot
## 6 Discussion and Future Work  
As the omnidirectional wheels rotate, the alternating forces on the rollers create a cyclic shock. The shock causes a periodic change in the positive pressure between each robot wheel and the wall. Therefore, when the robot is in a critical slip state, this shock eventually leads to unstable friction between the robot's wheels and the wall, thus causing the oscillation after 500 ms in Figure 9. According to the description in [35], this shock can be reduced by adjusting the omnidirectional wheel form. As a result, the motion performance of the robot can be more fully exploited. The next step can be to improve the design of the omnidirectional wheel based on this requirement and complete the real robot.  
In this paper, an NMPC algorithm is designed that is more accurate than the linear MPC algorithm model but still suffers from the model mismatch. In the experiments, the angle information in this model directly selects the angle of the reference trajectory instead of the prediction angle. If the angle of the reference trajectory is used, the prediction model mismatch will be aggravated when the robot's orientation tracking is inaccurate; if the prediction angle is used, the computation of the prediction model will increase significantly as the number of predictions steps increases. Making the prediction model more accurate while ensuring the efficiency of the algorithm is an important research element to enhance this control algorithm. One possible solution to the problem of model mismatch is to use a data-driven nonlinear model. Parameter adaption techniques are often integrated into the MPC algorithm. Model predictive control based on the data-driven model as the neural network-based model predictive control(NNMPC) [36] and the Bayesian neural networks model predictive control(BNN-MPC) [37] are now widely researched and used.  
Because of the favorable results of this simulation, the next step would be to implement this NMPC-based control algorithm on a real robot to test and compare the efficacy with the simulation results. Further theoretical research and engineering implementation are proceeding in parallel now. However, the conditions for experiments on a real robot are not available yet. There are many differences between real-life implementation and simulation that need to be addressed. For example, in real-life, the delay of the robot is unstable due to many factors, such as the hardware and environment. In addition, the accuracy of motion control is reduced because of perturbations in the robot's attachment and drive forces. These problems make it challenging for the disturbance rejection of the control algorithm.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Program**} & **RMSE for X-** & **RMSE for Y-** & **RMSE for** \\  & **Directional Position** & **Directional Position** & **Orientation** \\ \hline PID experiment results & \(3.661\times 10^{-2}\) & \(3.684\times 10^{-2}\) & \(1.219\) \\ NMPC experiment results & \(4.946\times 10^{-4}\) & \(3.839\times 10^{-3}\) & \(4.844\times 10^{-1}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the RMSE of PID experiment and NMPC experiment.