## 4 Mobile Robot Local Path Planning
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithms** & **Advantages** & **Disadvantages** \\ \hline \multirow{2}{*}{Dijkstra} & The calculation strategy is not complex and gives the shortest path & The increment of traversal nodes complicates the calculations \\ \hline \multirow{2}{*}{A*} & In static environments, the & Not appropriate for dynamic \\  & algorithm search efficiency is high & environments \\ \hline \multirow{2}{*}{D*} & Good for dynamic environment path & Planning longer paths via D* creates \\  & planning and more efficient than A* & challenges \\ \hline \multirow{2}{*}{RRT} & Fast convergence, high search capability & Algorithm efficiency is low in \\  & & unstructured environments \\ \hline \multirow{2}{*}{Genetic} & Appropriate for complex & Low algorithm convergence speed, \\  & environments, good for finding & low search ability in local paths \\  & optimal paths & \\ \hline \multirow{4}{*}{Ant colony} & Appropriate for complex & \\  & environments, can be combined & Slow convergence rate, easily \\  & with other heuristic-based path & trapped in local minima \\  & planners & \\ \hline \multirow{2}{*}{Particle swarm optimisation} & High convergence rate, good & Frequently, solutions converge into \\  & robustness & local optimal solutions \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Global path planning algorithms. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithms** & **Advantages** & **Disadvantages** \\ \hline \multirow{2}{*}{Artificial potential field} & Can be implemented for 3D & \multirow{2}{*}{Cannot guarantee the optimal} \\  & path planning, and can solve & solution \\  & the local minimum problem & \\ \hline \multirow{2}{*}{Simulated annealing} & Flexible and easy & \multirow{2}{*}{Can produce unstable results, the trade-off between accuracy} \\  & implementation, can deal with & \\  & noisy data and non-linear & \\  & models & \\ \hline \multirow{2}{*}{Fuzzy logic} & Strong robustness, decrease & Needs accurate prior \\  & the dependencies between & knowledge, poor learning \\  & environmental data & capabilities \\ \hline \multirow{2}{*}{Neural network} & Strong robustness, and & \multirow{2}{*}{Low path planning efficiency} \\  & learning ability from & \\  & experiences & \\ \hline \multirow{2}{*}{Dynamic window} & Good self-adaptation to & Not appropriate for \\  & environments & unstructured complex \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Local path planning algorithms. in mobile robotic applications because the data captured by sensors varies in real-time in response to the dynamics of environments. In comparison to global path planning strategies, local path planning is more critical for practical robot operation and usually serves as a bridge between global planning and the direct control of robots. However, local path planners have one notable drawback in that they often lead robots to local minimum points. Many classical local path planning algorithms can generate optimal paths in the local environments while avoiding local minimum problems. These methods include techniques such as the Fuzzy Logic algorithm, artificial potential field method, and simulated annealing algorithm. In general, these methods do not evaluate the relative velocities between robots and dynamic environment objects, however, which can lead to difficulties. In many worst-case scenarios, even the velocity profiles of these obstacles can be hard to acquire for the robots. Visual-inertial odometry methods show success in outdoor environment navigation scenarios but are not suitable for navigating in off-road conditions without pre-built maps or GPS assistance. Machine learning methods have been applied for mobile robot navigation to learn semantic information [139, 140, 141] and statistical patterns [142, 143] of environments. Several other research works [144, 145, 146, 147] have used machine learning to achieve robustness in path following.