## 4 Mobile Robot Local Path Planning  
Approaches for path planning of robots in local environments can be broadly classified into either classical methods or learning-based methods that attempt to modify the path-planning based on factors such as environmental conditions and prior experience [131; 132]. The classical methods follow a modular architecture with environmental perception, planning of paths relative to generated global maps, and trajectory following. Many classical techniques are applicable for robot navigation in static environments. However, their suitability can substantially diminish in unstructured or dynamic environments [133]. In general, these methods can be used effectively in indoor mobile robot applications but may not be suitable for outdoor off-road navigation conditions (e.g., terrain with grass might be traversable despite appearing blocked, and ground with mud or sand may not be suitable for pathing, despite appearing clear), hence the requirement to investigate machine learning-based navigation approaches. Several global and local motion planning methods for ground robot navigation are compared in Tables 4 and 5. The A* and Dijkstra algorithms have been well-researched in the past decades, and these methods have shown their potential by extensively being applied with the Robot Operating System (ROS) in many real-world robotics applications. Combining these two path-planning methods with heuristic searching is effective in relatively low-complexity 2D environments. However, these methods require heavy computational capabilities in high-dimensional environments or may struggle in unstructured and dynamic working environments. Random sampling-based path planning algorithms generally consist of BITs and RRTs. Regionally Accelerated Batch Informed Trees (RABIT) are more widely used and perform better in high dimensional and dynamic environments [134] in comparison to graph search-based path planning algorithms. Bionic-based intelligent robot path planning methods simulate the behaviours of insects to generate evolving paths. These evolutionary methods include the ant colony, particle swarm optimisation, and genetic and artificial bee colony algorithms. Many other optimised versions of these algorithms have been proposed to improve calculation efficiency and avoid local minimum problems. A welding robot system developed in [135] had a combination of genetic and particle swarm optimisation algorithms to solve for the shortest route while avoiding obstacles. The artificial potential field method, ant colony optimisation, and geometry optimisation path methods were combined in another research study [136] to search the globally optimal path in 2D scenarios using computer simulations. This method showed fast solutions and reduced the risk of trapping robots in local minimum points. A multi-objective hierarchical particle swarm optimisation method has been proposed by [137] to plan global optimal path trajectories in cluttered environments. This method utilises three layers to generate the robot navigation trajectories. The triangular decomposition method [138] is applied in the first layer, the Dijkstra algorithm is used in the second, and a modified particle swarm optimisation algorithm is applied in the last layer. In general, the local path planning strategies that have been discussed use the available sensor data of robots regarding their surroundings to map, understand, and generate local paths while avoiding obstacles. These local path planning methods are effective