# Smooth Optimised A*-Guided DWA for Mobile Robot Path Planning
## 5 Simulation Experiments and Analyses
### Ablation Study  
To evaluate the contribution of each improvement in the proposed SOA-DWA algorithm, we conducted an ablation study in a consistent experimental setting. This study aims to isolate and quantify the impact of key modules including path pruning, path smoothing, and additional cost terms introduced in the DWA evaluation function. All experiments were conducted on the same static and dynamic map scenarios to ensure effective comparison.  
The baseline model includes the traditional A* and DWA algorithms without any modification. Subsequently, pruning is implemented to eliminate redundant nodes and reduce unnecessary turns. Following this, B-spline smoothing is applied to the pruned path to enhance curvature continuity and ensure the trajectory better aligns with the robot's kinematic constraints. For the DWA evaluation function, the impact of each additional cost term is evaluated separately.  
To ensure the statistical reliability of the experimental results, each algorithm combination is run 30 times independently in each scenario, with each key performance indicator recorded, including path length, number of turning points, total turning angles, and running time. The results are summarised as \"mean \(\pm\) standard deviation\" and presented in Tables 3 and 4. The results show that pruning alone can significantly reduce both the path length and turning points. Following B-spline smoothing, the total turning angle is further reduced, indicating that the trajectory smoothness is significantly improved. On the other hand, adding the global path constraint to the original DWA significantly improves the consistency of the trajectory with the global path. And adding the obstacle avoidance strategy significantly reduces the search time.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Number of**} & \multicolumn{2}{c}{**Number of**} \\  & & \multicolumn{2}{c}{**Turning**} & \multicolumn{2}{c}{**Nonsmooth**} & \multicolumn{2}{c}{**Total Turning**} & \multicolumn{2}{c}{**Path Length**} \\
**Environment** & **Algorithm** & **Points (Mean** & **Points (Mean** & **Online \(\pm\) Standard & **Points (Mean)** & **Online \(\pm\) Standard \\  & & & **Deviation**) & **Deviation**) & **Deviation**)/m** \\ \hline  & & Baseline A* & 7.1 \(\pm\) 0.94 & 7 \(\pm\) 0.62 & 643.5 \(\pm\) 21.1 & 57.2 \(\pm\) 0.76 \\  & Scenario 1 & A* with & 5.9 \(\pm\) 0.53 & 6.1 \(\pm\) 0.48 & 589.6 \(\pm\) 15.4 & 55.7 \(\pm\) 0.42 \\  & & A* with & 5.5 \(\pm\) 0.46 & 0 \(\pm\) 0 & 553.7 \(\pm\) 10.6 & 56.4 \(\pm\) 0.53 \\ Static Known & & smoothing & & & & \\ Scenario & Baseline A* & 13.4 \(\pm\) 0.84 & 13.1 \(\pm\) 0.68 & 1152.4 \(\pm\) 25.9 & 59 \(\pm\) 0.55 \\  & Scenario 2 & A* with & 10.5 \(\pm\) 0.62 & 11.4 \(\pm\) 0.56 & 1024.7 \(\pm\) 16.3 & 56 \(\pm\) 0.5 \\  & & A* with & 8.7 \(\pm\) 0.51 & 0 \(\pm\) 0 & 934.5 \(\pm\) 12.5 & 57.3 \(\pm\) 0.55 \\  & & smoothing & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation experiments with improved A* algorithm.  
These results clearly demonstrate the effectiveness of each proposed module and validate the necessity of their integration. The ablation study provides compelling evidence that each design decision contributes meaningfully to the overall performance gains achieved by the SOA-DWA algorithm.