# Real-Time Hybrid Navigation System-Based Path Planning and Obstacle Avoidance for Mobile Robots
## 1 Introduction
### Related Work  
#### Global Path Planning Approach  
Many global path-planning methods have been studied, each with its own criteria for optimization that depend on the particular environment. It is possible to divide path planning into two categories by decision-making based classification: deterministic (A-heuristic, D algorithm, etc.) and non-deterministic (particle swarm optimization, rapidly exploring, random tree, ant colony optimization, etc.). The artificial potential field [2] is a deterministic algorithm that simulates a magnetic field with a target acting as an attractive field and obstacles acting as the repulsive field. The robot acts as a particle under the effect of these fields. The advance of [2] is to find a suitable path without stacking caused by the local minima of its optimization problem (cul-de-sac case) and reducing the oscillation of the path (zigzag). A remarkable point is the regression search method that looks for nonadjacent points as long as the line between them does not exist an obstacle and remove the remaining points. Based on that, an optimized-length path can be obtained which original APF cannot produce directly. The genetic algorithm is presented in [3] that can be considered as a search heuristic that is inspired by [PERSON]'s theory. The algorithm presented in [3] could not find the shortest path since it uses the Manhattan distance instead of Euclidean distance for fitness value. A major drawback of the genetic algorithm is that it is computationally expensive for finding an optimal path. PSO of Ferguson splines were applied in [4] for path planning. Using the algorithm, the robot can find a trajectory directly. However, with a complex map, finding a solution for the problem requires many particles (particles are used to construct spline segment), i.e., the calculation becomes very difficult without prior sub-targets. The A-heuristic algorithm was introduced in [5; 6] that was used to solve the global path-planning task with the given map. The highlight in [5] is the consideration of unsafe diagonal movements, sharp turns and robot size that the original algorithm does not consider. Turning score was added to the weight of each cell in [6] to improve the optimal motion of the robot. [PERSON], et al., present the interesting control system for the global path planning that can adapt to the presence of the static unforeseen obstacles in [7]. Their algorithm is based on the VFH\({}^{*}\) that directly produces the smooth trajectory. However, in many cases, these trajectories just can be applicable for omnidirectional mobile robots. In [8], [PERSON], et al., modified the VFH method and applied neuron network learning model in order to enhance ability to react with critical situations of robot.  
A-heuristic algorithm is the simple, time-efficient algorithm and its performance is acceptable. Furthermore, special features of this algorithm are easily modified and its result is always existing for the reachable target. However, in mobile-robot path planning, we need to consider the size and kinematics constraint of the robots in order to generate the trajectory for it. We will consider these problems in Section 3.