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<span>The use of autonomous vehicle/robot has been adopted widely to replace human beings in performing dangerous missions in adverse environments. Keeping this in mind, path planning ensures that the autonomous vehicle must safely arrive to its destination with required criteria like lower computation time, shortest travelled path and completeness. There are few kinds of path planning strategies, such as combinatorial method, sampling based method and bio-inspired method. Among them, combinatorial method can accomplish couple of criteria without further adjustment in conventional algorithm. Configuration space provides detailed information about the position of all points in the system and it is the space for all configurations. Therefore, C-space denotes the actual free space zone for the movement of robot and guarantees that the vehicle or robot must not collide with the obstacle. This paper analyses different C-Space representation techniques under combinatorial method based on the past researches and their findings with different criteria such as optimality, completeness, safety, memory uses, real time and computational time etc. Visibility Graph has optimality which is a unique from other</span>

<span>Unmanned Air Vehicle (UAV) has attracted attention in recent years in conducting missions for longer time with higher levels of autonomy. For the enhanced autonomous characteristic of UAV, path planning is one of the crucial issues. Current researches on the graph search algorithms under combinatorial method are mainly reviewed in this paper by keeping focus on the comprehensive surveys of its properties for path planning. The outcome is a pen picture of their assumptions and drawbacks.</span>

Path planning has been an important aspect in the development of autonomous cars in which path planning is used to find a collision-free path for the car to traverse from a starting point Sp to a target point Tp. The main criteria for a good path planning algorithm include the capability of producing the shortest path with a low computation time. Low computation time makes the autonomous car able to re-plan a new collision-free path to avoid accident. However, the main problem with most path planning methods is their computation time increases as the number of obstacles in the environment increases. In this paper, an algorithm based on visibility graph (VG) is proposed. In the proposed algorithm, which is called Equilateral Space Oriented Visibility Graph (ESOVG), the number of obstacles considered for path planning is reduced by introducing a space in which the obstacles lie. This means the obstacles located outside the space are ignored for path planning. From simulation, the proposed algorithm has an improvement rate of up to 90% when compared to VG. This makes the algorithm is suitable to be applied in real-time and will greatly accelerate the development of autonomous cars in the near future.

This paper analyses an experimental path planning performance between the Iterative Equilateral Space Oriented Visibility Graph (IESOVG) and conventional Visibility Graph (VG) algorithms in terms of computation time and path length for an autonomous vehicle. IESOVG is a path planning algorithm that was proposed to overcome the limitations of VG which is slow in obstacle-rich environment. The performance assessment was done in several identical scenarios through simulation. The results showed that the proposed IESOVG algorithm was much faster in comparison to VG. In terms of path length, IESOVG was found to have almost similar performance with VG.  It was also found that IESOVG was complete as it could find a collision-free path in all scenarios.