# Reactive Obstacle-Avoidance Systems for Wheeled Mobile Robots Based on Artificial Intelligence
## 2 Artificial Intelligence, Obstacle Avoidance, and Navigation
_Obstacle avoidance and navigation._ It is one of the main tasks performed by mobile robots; it consists of avoiding a set of obstacles through the path to reach the goal in the environment. There are two techniques for obstacle avoidance on path planning: _global_ and _local._ A global type requires full knowledge of the static environment to search the target from an initial place. The local strategy consists of detecting the robot's position and obstacles close to it. In addition, for obstacle avoidance, two aspects need to be considered; the first one consists of detecting different obstacles through sensors. Second, the mobile robot will choose a proper path to reach the target without a crash. The knowledge of the environment is an essential aspect to avoid obstacles. There are dynamic and static types of obstacles, which are obstacles moving and without changes, respectively. A set of parameters determines the robot's position in space. Considering the parameters, a configuration is needed; this means that a system state at the environment space covered in \(n\) dimensions. Therefore, a vector space can be expressed by \([q_{1},...,q_{n}]^{T}\), where \(q\) is a point in the dimensional space \(Q\)[25]. In addition, robot navigation means finding its location and building a path to reach a goal, avoiding different environmental situations such as obstacles, radiation, weather, among others. Several approaches have been proposed to solve the autonomous navigation problem, such as methods based on fuzzy logic, neural networks, genetic algorithms, fuzzy-PSO, FLC-Expert systems, among others. For example, in [26], the authors proposed a method based on a combination of different algorithms, including fuzzy-PSO and genetic algorithms, to identify the optimal navigation in complex environments. According to their results, the proposal is suitable for navigation with and without obstacle avoidance in different environments. Different techniques can represent the environment, such as graph representation, cell decomposition, and road-map, among others. For example, the environment representation can usually be translated into a graph containing all mobile robot stages and the area covered by obstacles. This graph is called a state transition graph when all intermediate configurations and transitions are considered. Reactive methods need an accuracy system based on sensors that can avoid obstacles in the environment. One manner of performing is path planning, which is about tracking a path to guide the robot from an initial location to an objective. Usually, when path planning is performed, a map is used, and memory contains the data employed to build it. The main steps performed during path planning are: _self location, path planning, map building,_ and _map interpretation_[27]. A useful path consists of a set of actions that guides the robot through two points. According to the problem's target, an optimal path needs to satisfy different conditions such as smooth motion, constraints related to the problem, time used to reach the goal, and obstacle avoidance. In addition, motion planning is the process of selecting inputs and motions satisfying all constraints such as risk and obstacle avoidance. This task is performed through different models and environments [27].