# Path Optimization Using Metaheuristic Techniques for a Surveillance Robot
## 1 Introduction  
It is undeniable that along with any type of industrial application there is an associated monitoring task. This is essential to avoid accidents, sabotage, or theft that could disrupt normal operation and, in the worst case, lead the industry to bankruptcy. For this reason, for decades a part of the personnel of companies has been dedicated to surveillance and control tasks. However, with recent advances in mobile robotics and optimization algorithms, it is possible to increase the efficiency of such tasks, reducing human errors and improving performance, quality, and monitoring. In order to design a procedure capable of improving the quality of these tasks beyond human capabilities, it is imperative to address all the challenges that this implies. Specifically, within the scope of trajectory optimization, efficient route planning for autonomous vehicles is essential to minimize labor time while providing comprehensive spatial surveillance in the most efficient way possible. In the current market there are robots that receive orders to navigate freely to a specific point (usually called free navigation) or to navigate following predefined trajectories. Both approaches allow the creation of paths that run through a given space, facilitating the implementation of surveillance applications. However, waypoints must be chosen judiciously to maximize efficiency, that is, to cover the maximum area in the shortest possible time [1]. To address this important issue, this paper proposes the utilization of different heuristic techniques, such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), andPattern Search (PS), as tools to optimize these route waypoints and develop a complete coverage path planning (CCPP) approach. These approaches were chosen due to their capability to solve complex and dynamic challenges in trajectory planning [2]. In particular, this research explores how each evolutive approach influences the quality and efficiency of surveillance coverage, considering the characteristics of a Light Detection and Ranging (LiDAR) sensor, and a commercial mobile robot. A LiDAR is a remote sensing technology that uses laser light to measure distances, typically employed in applications such as mapping, surveying, and object detection. The proposed methodology aims to minimize robot displacements while achieving complete spatial coverage in the shortest time possible for different maps. The effectiveness of the three aforementioned optimization methods (GAs, PSO, and PS) has been studied and compared in rooms of different sizes and with obstacles, for the devices available in these use cases. As the methodology proposed here is general, with minor and simple adjustments it can be applied to any environment, with different types of sensors and for any differential robot. It could even be extended to other surveillance sensors, such as a smoke or gas detector [3]. To replace the sensor, it would be sufficient to adjust the range, pitch angle, and sweep angle parameters in the surveillance parameter calculation code. On the other hand, to use another robot it would be necessary to adjust the parameters of the length and angular acceleration ramps in the trajectory simulation code. All of these changes can be made quickly and easily. The main contributions of this work can be summarized as follows:  
* Development of a new methodology to solve the CCPP problem based on polyline optimization. An innovative methodology is introduced that optimizes the route planning of a mobile robot. The approach considers obstacles in the environment, maximizes coverage, and minimizes time. GA, PS, and PSO were applied as optimization techniques to solve this problem efficiently. * Application of the CCPP methodology to an autonomous surveillance robot with LiDAR technology. * A complete set of simulation experiments are carried out to evaluate the performance of the proposed methodology.