An Efficient Path Planning Algorithm for Networked Robots using Modified Optimization Algorithm
Niharika Singh1, Manish Prateek2, Piyush Chauhan3
1Niharika Singh*, School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India.
2Manish Prateek, School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India.
3Piyush Chauhan, School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India.
Manuscript received on September 12, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 2743-2748 | Volume-8 Issue-12, October 2019. | Retrieval Number: L25541081219/2019©BEIESP | DOI: 10.35940/ijitee.L2554.1081219
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Abstract: Path planning has played a significant role in major numerous decision-making techniques through an automatic system involved in numerous military applications. In the last century, pathfinding and generation were carried out by multiple intelligent approaches. It is very difficult in pathfinding to reduce energy. Besides suggesting the shortest path, it has been found that optimal path planning. This paper introduces an efficient path planning algorithm for networked robots using modified optimization algorithms in combination with the η3 -splines. A new method has employed a cuckoo optimization algorithm to handle the mobile robot path planning problem. At first, η3 – splines are combined so an irregular set of points can be included alongside the kinematic parameters chosen to relate with the development and the control of mobile robots. The proposed algorithm comprises of adaptive random fluctuations (ARFs), which help to deal with the very much manageable neighborhood convergence. This algorithm carries out the process of accurate object identification along with analyzing the influence of different design choice by developing a 3D CNN architecture to determine its performance. Besides offering classification in real-time applications, the proposed algorithm outperforms the performance of state of the art in different benchmarks.
Keywords: Cuckoo Optimization Algorithm, Modified Optimization Algorithms, Networked Robots, Path Planning
Scope of the Article: Algorithm Engineering