# A Study on the Design and Control of the Overhead Hoist Railway-Based Transportation System
## 4 Control Algorithms  
The use of artificial intelligence and robotics in the field of robot path planning is significant in the domain of mobile security. The task at hand entails the identification and determination of pathways that are collision-free, enabling mobile robots to successfully navigate towards their respective objectives. Path planning is divided into two distinct components: global planning and local planning. The former pertains to situations when the environment is well-known, while the latter deals with scenarios where there is limited or unknown information about the surroundings. Through simulation trials, the effectiveness of this approach has been empirically demonstrated, enabling robots to autonomously navigate maze environments. The Dijkstra algorithm proposed in this article has been shown to be accurate and efficient in mobile robot path planning. This study has found several problems, one of which is the robot's working space being a two-dimensional grid that has obstacles spanning from unobstructed areas to barriers [19]. To perceive ambient data, the robot is depicted as a single point, while barriers are depicted as linear entities. The technique uses a Cartesian spatial approach to represent the robot's movement. The robotic system is required to make decisions regarding obstacle avoidance through a series of computational steps, including line drawing, intersection detection, intersection identification, point connection, and execution of four distinct operations [19]. The line sensor location problem (SCLP), or traffic surveillance problem, is a challenging task that involves selecting a line to separate demand centers into two groups: origins and destinations [20]. The collection of census information and control of estimated origin/destination (O/D) matrices is of the utmost importance. To simplify the problem, it can be transformed into a set covering problem. The transformation is contingent on enumerating all potential paths between origin-destination pairs, which becomes infeasible in the case of vast networks. The problem of set coverage has been proven to be NP-hard. In the absence of limitations on the best placement of sensors, there are 2n possible configurations. Extensive research has been conducted on the subject of the traffic sensor location problem (TSLP), but limited emphasis has been placed on the associated surveillance issues. The SCLP conducts an investigation to determine the optimal placement and quantity of a specific type of traffic sensor to achieve a pre-established target. The researchers [PERSON], [PERSON], and [PERSON] conducted a study whereby they devised both exact and heuristic algorithms to address the stochastic capacitated location problem (SCLP) as a subproblem of the transportation and location problem (TSLP). These algorithms were specifically designed to handle large-scale transportation networks [20]. The methodology presented for solving the problem is based on the utilization of random priority selection metaheuristics in conjunction with a column-generating mechanism based on the shortestpath algorithm. The proposed framework is the sole method for implementing the precise method as it effectively avoids the need to exhaustively enumerate all potential paths within a network. Transportation networks are crucial in facilitating the identification of optimal routes between pairs of origin and destination points. Operators conduct research on this issue in order to optimize network performance, whereas consumers want the created routes to provide the most efficient means of transportation from their starting points to their intended destinations. The issue of variability in journey time is commonly addressed by simplifying the assumption that a probability density function (PDF) exists for each link. In practical scenarios, it is difficult to accurately anticipate the travel time of each link due to variations.