1. An optimized approach of intelligent path planning of a robot manipulator using PSO algorithm.
- Author
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Kumar, Ranjan and Kumar, Kaushik
- Subjects
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ROBOTIC path planning , *PARTICLE swarm optimization , *SWARM intelligence , *MANIPULATORS (Machinery) , *SELF-organizing systems , *ALGORITHMS , *MECHANICAL ability - Abstract
The path planning plays an important role in the development of a robotic system. Since, a robotic system has to perform many tedious jobs in a particular trajectory. For avoiding the collision in between the path or trajectory, it is essential to plan the path according to the assigned jobs. In such cases, the introduction of some level of intelligence to the mechanical machines are quite important. However, the path and trajectory generation of robotic system comprises many aspects. The current work focusses on the path planning using cubic spline approach and some level of intelligence has been introduced using the particle swarm optimization (PSO) algorithm. The path planning of a robotic system demands some intelligence and is helpful in planning the for finding the shortest best possible path between the two terminal points via some control points which are responsible for the geometrical and topological shape of the path. The main objective of this work is to propose a path planning technique using the spline function and PSO algorithms and to enlighten the readers with basic aspects of the same. The present study introduces the particle swarm optimization (PSO) algorithm to solve the path planning problem and helps in finding the best possible shortest path between the two terminal points SP1 and SP2. The PSO shows the swarm intelligence behavior which falls under the category of meta-heuristic optimization techniques. The swarm intelligence shows their collaborative behavior of self-organizing and decentralized systems. It provides a kind of framework where the particles or the simple agents interacts each other. Due to having an important key feature of self-organization. Due to having the local interactions among the swarm particles the unordered particles interchange their best experience and there arises a global coordination among the particles in an ordered manner. The particles or agents of the swarm are employed in the search space to perform the richness of the food source. Each particle of the swarm is treated as a 'Candidate solution' and they possess their own memory where the 'Personal Best' experience of each particle is stored. Further, each member or particle interact with each other and share their information about their personal best experiences. So, they finally learn to choose the best experience among all the experiences and we call it 'Global Best'. This work helps in finding the based possible path when a robotic system is subjected to some spherical shaped obstacles in the configuration space. The path planning based on PSO estimates the best cost solution by avoiding the obstacles in the space and generates a collision free path. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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