# Path Optimization Using Metaheuristic Techniques for a Surveillance Robot
## 3 Description of the Experimental Setup and Simulation Scenarios  
Effective surveillance is crucial in security and monitoring applications. In robotics, a common challenge is to determine the optimal trajectory that ensures complete coverage of the target area while minimizing redundancy and maximizing detection time. It is essential to design routes that are efficient in terms of distance traveled and time invested, as well as to identify the path that consumes less energy, with less wear and, at the same time, that performs more work in the minimum time, in order to provide the greatest value to the system. This study explores how to apply advanced surveillance trajectory optimization techniques, such as GA, PSO, and PS, to improve the surveillance trajectories of a Hussar mobile robot [28] equipped with a 6 m range 3D 360 LiDAR [29]. The Hussar robot is a square-bodied delivery robot with extensive mobility, positioning, and remote trajectory tracking capabilities. It is also compact and versatile, allowing it to effortlessly navigate through tight spaces and obstacle-filled environments, making it ideal for areas with narrow passages. It has dimensions (\(L\cdot W\cdot H\)) of 450 mm \(\times\) 450 mm:317 mm. It has an important autonomy of 6 h and an automated recharging system. As for the robot's mobility, it is capable of moving with speeds in a range from 0.2 to 0.8 m/s. However, the version available in the laboratory is limited to 0.5 m/s. Therefore, in the experiments its speed is considered constant and has been set at the maximum of 0.5 m/s. The robot is equipped with an Android 5.1 operating system and can establish communication with the on-board computer via a PC, Raspberry Pi, or Arduino using ROS (Robot Operating System, [[http://www.reemanrobot.com/robot-chassis/square-robot-chassis/automatic-square-robot-chassis.html](http://www.reemanrobot.com/robot-chassis/square-robot-chassis/automatic-square-robot-chassis.html)]([http://www.reemanrobot.com/robot-chassis/square-robot-chassis/automatic-square-robot-chassis.html](http://www.reemanrobot.com/robot-chassis/square-robot-chassis/automatic-square-robot-chassis.html)) (accessed on 27 September 2023)). Given the need to detect and distinguish obstacles and people from other types of potentially malicious objects, a 3D LiDAR type sensor is necessary. The detection data from the LiDAR are intersected with the static map of the environment stored inside the robot. This process helps identify if an object is an obstacle or a potential alert. The 3D multiScan100 from SICK (SICK, Waldkirch, Germany) [30] was chosen, which offers a 360-degree range, operates in three dimensions, and has a distance range of up to 10 m with 10% reflectance. For security reasons, the distance that has been programmed in the simulation environment is 4 m. This ensures that the distance between two consecutives sensing samples in the same sensing sweep is below 1 cm. For data transmission, this device has an Ethernet communication interface. The system architecture (Figure 1) is composed of three elements: the LiDAR multiScan100 (from SICK, Waldrick, Germany), a Raspberry Pi 4 (from Raspberry Pi Foundation, Cambridge, UK), and [PERSON]'s Hussar robot (from Reeman Robotics, Shenzen, China). The process by which objects are detected is as follows: the LiDAR, powered by the same source as the Raspberry Pi, collects positional data of objects in the environment and transmits them to the Raspberry Pi as a point cloud. The Raspberry Pi compares the positions collected by the multiScan100 with the map. If an object not present in the map is detected, the system issues an alarm. On the other hand, the navigation algorithm is also integrated in the Raspberry, and it works as follows: the autonomous robot communicates its current position, and the control device recognizes it and commands the next position in the navigation path.