## 3 Proposed Work
### State Space Model
A replan is necessary if the route is completely blocked or if crucial information has been uncovered. Planning and execution must go hand in hand. Replanning methods must be fast because the robot cannot move toward its goal while replanning. Their design prioritizes speed of operation, sometimes at the cost of optimality and completeness. #### 3.4.1 Path Planning for Exploring

An unmanned ground vehicle (UGV) can be beneficial for exploring and creating maps. Thanks to various algorithms, robots are able to explore uncharted territories. By computing the optimal route until a map discrepancy is found, updating the map, and then replanning the entire path, they can exhibit optimal behavior. However, this can be challenging on large maps, where replanning can be inefficient, especially if the map contains limited information and requires frequent replanning. To achieve speed, some algorithms prioritize coverage over optimality. The robot is guided [31] until it reaches its objective. To prevent repeated visits to the same location, the robot moves to the next location with the lowest cost, and the cost of that location is increased each time. Author [32]calculates the cost of reaching the goal for each state based on the initial data, and updates it with backtracking costs as the robot progresses. #### 3.4.2 Path Planning for Partially Known Environments

The goal is to provide more goal-directed behavior than can be achieved with exploration algorithms when working with partial maps, while still prioritizing planning speed. When there are no obstacles, [PERSON]'s bug algorithms [PERSON] (1986) steer the robot toward the objective. If an obstacle is encountered, it moves around it until it reaches the point closest to the goal, and then moves directly toward the goal. In the past, when incorrect information was discovered, some early approaches to planning in partially known environments required a complete replan (2018). However, since then, more efficient methods have been developed to expedite the replanning process. These typically involve refined graph search algorithms, such as those mentioned earlier. Using quad-trees (2018) to indicate traversability improves efficiency and speeds up the search by adjusting the heuristic using A* search and past terrain navigation experience. There are also additional methods for accelerating the search (2018). Some fundamental tools that speed up replanning include:

* Heuristics. * Using limited look-ahead to limit the planning area (Real-time Heuristic Search). * Replacing only the altered portion of the path (incremental search). * Only changing the portion of the path that is necessary to reach the objective (incremental search). In this research, a LiDAR sensor is combined with a camera, and the current study shows that no additional sensors are necessary in a LiDAR sensor-based environment. Well-established benchmarked methods are used to obtain accurate results. The current approach uses supervised data to handle image-based data, while unsupervised algorithms are being developed and researched to address the shortage of image-based data. Pre-training is suggested for SS as it provides the system with experience and prior knowledge of the real world. Figure 2 shows the PC data captured by the LiDAR sensor and the unstructured PC data. A PC is a set of data points in three-dimensional space that represent the shape and form of an object or environment. These data points are typically collected using 3D laser scanners or other surveying instruments, and they can be utilized to develop detailed 3D designs of objects and environments. Figure 2: LiDAR PC data from PCAP file. ### Pre-Learning

The pre-learning stage is critical for setting up the LiDAR sensor in a partially unknown environment. It focuses on using raw LiDAR data with mono-model sensors. To start, the Velodyne-FileReader function is used with parameters that take into account the file and device names. This creates a Velodyne PC object from the disorganized PC data.