Table II. Performance of different path planning technique in Phase I of evaluation Sl. No Factors Conventional Technique Soft Computing Techniques Artificial Potential Field [89] Vector Field Histogram [90] Road Map Graph Searching Technique [93] Neural Network [94] Genetic Algorithm [95] Fuzzy Logic [96] Visibility Graph [91] Voronoi Diagram [92] 1 Shortest Path distance (SPD) (m) 35.43 99.45 41.67 40.21 37.22 34.65 34.11 35.32 2 Obstacle Avoidance (OA) (mm) 3.5 3.2 3.45 3.107 2.73 4 3.99 3.83 3 Elapsed Time (ET) (min) 3 9.55 10.78 9.32 10.47 8.21 8.2 8.99 Table III. Performance of different path planning technique in Phase II of evaluation Sl. No Factors Conventional Techniques Soft Computing Techniques Hybrid Techniques Artificial Potential Field [97] Road Map Genetic Algorithm [100] Swarm Optimization [101] Ant Colony Optimization [102] Ant Colony + Genetic Algorithm [103] Ant Colony + Logic [104] Visibility [98] Voronoi Diagram [99] 1 Shortest Path Distance (SPD) (m) 39.21 42.34 39.59 36.25 35.51 34.98 36.92 37.97 2 Obstacle Avoidance (OA) (mm) 3.36 2.91 2.87 3.89 3.91 4.01 4.1 4.07 3 Elapsed Time (ET) (min) 37.54 38.62 38.83 36.69 35.92 35.027 36.309 37.1
To Summarize, the factors involved in I shows better results for path planning in the single autonomous mobile robot. Each Factor is applied for each Algorithm to gain a solution which shows Genetic Algorithm is suitable for finding a Shortest Path Distance which is calculated in meters, for Obstacles Avoidance Neural Network plays a major role which is measured in Millimeters and for measuring a time in Minutes the genetic Algorithm is good for reaching a target in a few minutes compared to other techniques. Thus the required solution shown in Phase I represents Soft Computing is better for Path Planning by an autonomous Single Mobile robot. In Phase II the factors are involved in calculating path planning in Multiple Robots. The Shortest Path Distance factor is applied in Conventional and Soft computing techniques, but better result shown in Ant Colony Optimization which is a soft computing technique. In the process of finding an Obstacles, here also Ant Colony Optimization playing a major role. In the third factor, Ant colony Optimization (ACO) takes less time for reaching a target compared to other techniques. The Hybrid Algorithm is also more effective in finding a better solution like Computing method, but less complex for merging two or more Algorithm. From the Phase I and Phase II overall view Soft computing Techniques shows better results for finding a shortest path, Obstacle Avoidance and Elapsed time where Conventional Techniques also involve in finding these factors but it shows only less effective results. 5. CONCLUSION The different path planning algorithm for an autonomous mobile robot has been endured with various techniques. Each technique has its own advantages and disadvantages. Each section shows the different approaches used for Path Planning in Autonomous Mobile Robot in both Static and Dynamic Environment. This work showed complete survey on various mobile robot path planning techniques that can optimize the robot path. Simulation results justify that softcomputing algorithm especially ACO based path planning outperform traditional methods, both in the perspective of single and multi robot path planning, based on the factors such as shortest path distance, obstacle avoidance, elapsed time and high computational problems. This survey can offer better assistance in the understanding of the path planning techniques and also guide researchers to formulate novel techniques for improved path planning in both single and multi robot environments.