Solving Roulette Wheel Selection Method using Swarm Intelligence for Trajectory Planning of Intelligent Systems

Intelligent Systems is a defined methodical technique to solving major and somewhat complicated issues that consistently and reliably produces consistent and reliable outcomes. Understanding and benefiting from one's own experiences are two key components of intelligence, as defined by a variety of dictionaries. However, there are alternative connotations, such as the ability to learn and retain knowledge; mental ability; the ability to adapt rapidly and successfully to a new situation, etc. Intelligent systems are difficult to define and are the topic of much discussion. Intelligence may be measured in terms of a system's ability to adapt and learn from its environment, as well as its ability to handle uncertainty and inaccurate information. Using kinematic constraints such as geometric, physical, and temporal restrictions, intelligent systems attempt to design a path across a workspace that is both optimum and collision-free. When compared to motion planning, where dynamics must be taken into account, the primary goal of path planning is to discover the quickest and most efficient route while also accurately modeling the surrounding environment. In this paper, we make use of Swarm Intelligence for solving the problem of path planning in Intelligent Systems. Use of Roulette Wheel Selection algorithm is done to find the optimal path. The efficiency of the model is proved by making use of Swarm Intelligent algorithms. Comparative results are obtained for all the algorithms and it could be claimed that the Firefly algorithm performs better than the other algorithms for our proposed trajectory planning algorithm.