# Real-Time Path Planning for Strategic Missions
## 2 Related Works  
Several strategies have been proposed to autonomously guide robots in dynamic environments. Some of them, related to this work, are commented below.  
An intelligent motion planning with dynamic obstacle avoidance is proposed in [10]. Two Sugeno fuzzy logic controllers work separately to handle collision avoidance tasks. Four behavior controllers are responsible to guide the navigation. They are goal-reaching, goal searching, speed control and obstacle avoidance. This last one is split in two others, to separately deal with static and dynamic obstacles.  
[PERSON] and colleagues contributed with a new navigation strategy called Dynamic Planning Navigation Algorithm optimized with genetic algorithm (DPNA-GA) in [11], which ensures that the robot reaches the goal in an unknown environment containing static and dynamic obstacles. In summary, the robot follows a path composed by location goals and the destination.  
[PERSON], in [12], uses the optimized firefly algorithm, and applies it to mobile robot navigation. For this, a zone was made around the agent representing the light intensity of a firefly and from that the algorithm is able to detect and avoid any object within that safe zone in a static environment. In [13], a genetic algorithm improves robot decisions and enables navigation in dynamic environments.  
A new path planning methodology based on Ant Colony Optimization is presented in [14] for environments with static obstacles. The authors consider the obstacles in the straight segment that connect the starting point with the goal point, as critical ones, and use such an information to improve the optimization process. Then, they introduce the Ant Colony Optimization with the Influence of Critical obstacle, or simply ACOIC.  
In [15], the authors deal with the path planning problem for platforms with limited sensor and processing capabilities. They assume that a mapping strategy is available, however the robot has no prior information about the environment. As the robot navigates towards the target, it stores information that enables it to generate sub-optimal routes using the A* searching algorithm. The more information the robot has about the environment, the closer to the optimal route the obtained solution is. Now, considering a known dynamic environment, the work of [PERSON] [16] uses a search algorithm to find an optimal way to get to the desired position, and during task execution the algorithm is able to deviate and recompute routes, thus avoiding collisions with dynamic obstacles. In addition, the hybrid navigation algorithm proves to be more efficient than others known in the literature and presents excellent performance in environments with many dynamic obstacles.  
In turn, in [17], an improved genetic algorithm finds an optimal path in a dynamic environment. This work considers that the entire environment is known by the algorithm and the algorithm considers that the robot is a particle, ignoring its dimensions. The work guarantees that the algorithm finds the optimal path, better than A* and D* (which commonly find sub-optimal paths).  
All the works listed in this section apply some optimization algorithm to perform a path planning. Some of them for static and other for dynamic environments. Thus, they give theoretical support to the strategy proposed in this paper.