## 3 Operational Requirements
### Navigation and Mobility
#### 3.2.2 Route Planning and Navigation  
Route planning for the navigation of a harvesting robot is a crucial process that involves determining the best trajectory for the robot to move efficiently through the crop field and harvest the fruits optimally, this process is done autonomously by the robot. * **Mapping the environment:** Before starting any harvesting operation, the robot uses its sensors, such as cameras and perception systems, to create a detailed map of the agricultural environment. This map includes information on the arrangement of the rows of plants, the location of ripe fruits, obstacles such as trees or agricultural structures, and other relevant elements [43]. * **Route planning:** Based on the generated map and the harvesting objectives, the robot plans the best route to move efficiently between the rows of plants and collect the identified fruits. The robot decides the order in which it will pick the fruits based on criteria such as proximity, accessibility, and picking efficiency. For example, you can prioritize fruits that are closer or in a more accessible position. Using route planning algorithms, such as the algorithm, the robot calculates the shortest and safest route to reach each point of interest. During route planning, the robot also considers how to avoid obstacles detected in the environment [44]. * **Autonomous navigation:** During operation, the robot uses navigation algorithms that combine information from the map of the environment, the robot's current position, and sensor data to make real-time decisions about direction and speed of movement. In short, route planning for the navigation of a harvesting robot combines the ability to perceive the environment with advanced trajectory planning algorithms to achieve efficient and accurate harvesting in dynamic and varied agricultural environments. Autonomous movement in the agricultural environment involves GPS systems and LIDAR sensors for precise navigation. * **GPS (Global Positioning System):** GPS works by receiving signals from satellites in orbit. A GPS receiver in the robot picks up these signals and uses them to calculate the robot's position in terms of geographic coordinates (latitude, longitude, and altitude) [45]. Using the trilateration technique, the GPS receiver calculates its exact position by measuring the time it takes for signals to travel from various satellites to the receiver. The GPS provides the robot with information about its location in the crop field. This is crucial for planning efficient harvesting routes and ensuring that the robot covers the entire growing area without skipping any sections. In large fields where multiple robots may be operating simultaneously, GPS helps coordinate movements between robots to avoid collisions and optimize field coverage. The data collected, such as the location of ripe fruits, can be georeferenced, allowing a subsequent analysis on the distribution and conditions of the crop [46]. * **LiDAR (Light Detection and Ranging):** LiDAR works by emitting pulses of laser light into the environment and measuring the time it takes for light to reflect and return to the sensor. By measuring these distances in multiple directions, LiDAR creates a three-dimensional \"point cloud\" that accurately represents the structure of the environment [47]. LiDAR allows the robot to detect obstacles in its immediate environment, such as plants, trees, and agricultural structures, in real-time. This is essential to avoid collisions and damage to both the robot and the crop. By generating a detailed three-dimensional map of the environment, LiDAR helps the robot navigate precisely between rows of plants and locate ripe fruits for picking. The detailed environmental information provided by the LiDAR allows the robot to position its harvesting arms and tools with pinpoint accuracy, ensuring effective harvesting without damaging surrounding fruits or plants [48].