Continuous Path Planning of Kinematically Redundant Manipulator using Particle Swarm Optimization

This paper addresses a problem of a continuous path planning of a redundant manipulator where an end-effector needs to follow a desired path. Based on a geometrical analysis, feasible postures of a self-motion are mapped into an interval so that there will be an angle domain boundary and a redundancy resolution to track the desired path lies within this boundary. To choose a best solution among many possible solutions, meta-heuristic optimizations, namely, a Genetic Algorithm (GA), a Particle Swarm Optimization (PSO), and a Grey Wolf Optimizer (GWO) will be employed with an optimization objective to minimize a joint angle travelling distance. To achieve n-connectivity of sampling points, the angle domain trajectories are modelled using a sinusoidal function generated inside the angle domain boundary. A complex geometrical path obtained from Bezier and algebraic curves are used as the traced path that should be followed by a 3-Degree of Freedom (DOF) arm robot manipulator and a hyper-redundant manipulator. The path from the PSO yields better results than that of the GA and GWO.

@article{Machmudah2018,
  title     = {Continuous Path Planning of Kinematically Redundant Manipulator using Particle Swarm Optimization},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {9},
  number    = {3},
  year      = {2018},
  publisher = {The Science and Information Organization},
  author    = {Affiani Machmudah and Setyamartana Parman and M.B. Baharom},
  doi       = {10.14569/IJACSA.2018.090330},
  url       = {https://doi.org/10.14569/IJACSA.2018.090330}
}