# Real-Time Path Planning for Strategic Missions
## 1 Introduction  
In recent decades, mobile robotics has been successfully used in many applications, such as security, industry and military missions, for instance. To perform these missions, a robot should be able to navigate and explore the environment autonomously. Often, path-planning is one of the problems to be solved, which involves the search of an optimal or sub-optimal route from the initial position to a desired one in its workspace [1].  
In a dynamic environment, such as those characteristic of war scenarios or disaster zones, route changes often have obstructed passages or loss of information about some region. In this case, the decision making by the robot must take place interactively with the environment, and the robot has to update the route to its destination each time a change occurs in the environment. Thus, it disrupts momentously the previous/current computed path and starts following a new one, resulting in a local planning performed upon detecting an obstacle or loosing information about a certain zone by which it should pass.  
From a military point-of-view, the authors of [2] mention that in recent years the US Army has incorporated robots to reduce the number of humans in their strategic actions. As a result, the US Army has invested heavily in research and development of autonomous systems [3]. With a similar purpose, intelligent agents have been used in natural disaster sites to minimize the losses, so that intelligent disaster rescue operations are becoming more and more common [4; 5; 6]. It is important to notice that in both scenarios the environment can change during mission execution, therefore requiringthe incorporation of a path-planning strategy that is able to update the planned path whenever an unforeseen or unexpected event occurs.  
There are several algorithms available in the literature to deal with route optimization, such as Dijkstra algorithm [7], A* algorithm [8] and Lifelong Planning A* (LPA*) algorithm [9]. All of them can solve path-planning problems and find an optimal solution. The main difference among them is the complexity level of the algorithm.  
All of these three algorithms deal with the path-planning problem, finding an optimal route for the robot, with different execution times. When a change in the environment occurs, such as the addition/appearance of an obstacle, if that change affects the previously found path, the Dijkstra and A* algorithms solve the path planning problem again, while the LPA* takes advantage of the already obtained solution for this new problem. Thus, this last approach addresses the problem more quickly and effectively.  
In this context, this paper discusses the application of the LPA* algorithm in a real problem, whose objective is to guide a mobile robot to a target point in an environment that changes along time. These changes are characterized by the addition of obstacles that prevent navigation or the inability to follow a previously computed route, emulating scenarios of recent hazards or disasters.  
After this short introduction, this paper is hereinafter organized as follows: Section 2 presents some related works, and Section 3 introduces the LPA* algorithm and how it is implemented to deal with a navigation problem. The description of the navigation environment and the results obtained with a mobile robot evolving in such an environment, as well the pertinent discussions, are presented in Sections 4 and 5, respectively. Finally, Section 6 highlights the main contributions and concluding remarks of this paper.