## 3 Multi-Sensor-Based Remote Autonomous Navigation and Close-Range
### Automatic Redarging Path Planning Algorithm
#### 3.2.2 Improved Dynamic Window Algorithm  
The traditional dynamic window approach lacks global planning guidance, making it prone to local optima. To address this drawback, this paper improves the evaluation function of the dynamic window method to incorporate global path information, ensuring that the final local planning adheres to the global optimal path and alleviates the issue of becoming stuck in local optima. The dynamic window method first determines the range of robot motion speeds and then simulates driving trajectories using this speed range within the robot motion model. The evaluation function scores several simulated trajectories, and the trajectory with the highest score is selected as the optimal driving route for the robot. The corresponding angular and linear speeds of this trajectory are the robot's best driving speeds. After simulating several trajectories based on the speed range and robot motion model, the evaluation function selects the optimal trajectory. To resolve the issue of local optima, this paper improves the evaluation function to combine global path information, ensuring that the final local path is based on the global optimal path. The improved evaluation function is given by:  
\[G(\
u,\omega)=\sigma(\alpha P_{\text{Head}}(\
u,\omega)+\beta\text{dist}(\
u, \omega)+\gamma\text{vel}(\
u,\omega)) \tag{6}\]  
where \(P_{\text{Head}}(\
u,\omega)\) is the angle difference between the trajectory endpoint direction and the current target point, \(\text{dist}(\
u,\omega)\) is the distance between the trajectory and the nearest obstacle, \(\text{vel}(\
u,\omega)\) is the current velocity evaluation function, \(\sigma\) is a smoothing function, and \(\alpha\), \(\beta\), and \(\gamma\) are weighting coefficients. This evaluation function ensures that the planned route avoids random obstacles and adheres to the global optimal path. This paper integrates the improved A* algorithm with the dynamic window method, achieving both global path optimality and random obstacle avoidance. The improved A* algorithm is used to plan the global path, and after obtaining the global optimal node sequence, the dynamic window method performs local path planning between each pair of adjacent nodes. The algorithm flow is as shown in Figure 5.  
The simulation environment is configured as a \(30\times 30\) two-dimensional grid map with an obstacle coverage of \(P=25\%\). The robot's parameters are defined as follows: a maximum speed of \(2\,\text{m}/\text{s}\); maximum rotational speed of \(20\,\text{rad}/\text{s}\); acceleration of \(0.2\,\text{m}/\text{s}^{2}\) \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Metric** & **Traditional A* (Experiment 1)** & **Traditional A* (Experiment 2)** & **Traditional A* (Experiment 3)** & **Improved A* (Experiment 1)** & **Improved A* (Experiment 2)** & **Improved A* (Experiment 3)** \\ \hline Planning time (s) & 0.020610 & 0.007665 & 0.004626 & 0.027963 & 0.020292 & 0.014692 \\ \hline Turn angle & 45.0000 & 180.0000 & 135.0000 & 0.0000 & 80.3964 & 72.6313 \\ \hline Turn count & 1.0000 & 4.0000 & 3.0000 & 0.0000 & 2.0000 & 2.0000 \\ \hline Path length & 13.8995 & 21.2426 & 28.6569 & 13.0384 & 21.0105 & 28.2904 \\ \hline Node count & 81 & 84 & 123 & 53 & 73 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of algorithms in three experimental groups. rotational acceleration of \(50\) rad/s\({}^{2}\); and velocity resolution of \(0.01\) m/s. The mobile obstacle is set to start at coordinates \((12,14)\) and move to \((8,21)\) with a specified speed of \(0.01\) m/s. The integrated algorithm successfully plans a path from the starting departure points located at \((12,14)\), \((8,21)\), and \((9,28)\) to the mobile target for the AGV set at coordinates \((5,2)\). After incorporating the DWA algorithm into the improved A* framework, the robot not only follows a globally optimal path but also demonstrates enhanced obstacle avoidance capabilities.