IEEE Galveston Bay Section

Inverse kinematics is a fundamental problem in manipulator robotics: a set of joint angles must be calculated so that the robot arm can be manipulated to the corresponding desired end effector position and orientation (also known as "pose"). Traditional solution techniques include analytical kinematics solvers which provide the closed-form expressions for the joint positions as functions of the end effector pose. When analytical inverse kinematics solvers are not possible due to the manipulator structure, numerical methods such as Newton-Ralphson or Jacobian inverse can be used to achieve the task but at a much slower speed due to the iterative nature of the computation. Recent swarm intelligence technology has also contributed to manipulator inverse kinematics solutions. In this presentation, we investigate the use of the Particle Swarm Optimization (PSO) approach in solving the inverse kinematics problem for the general serial robotic manipulators. Many of the reviewed robotic manipulator inverse kinematics solvers using swarm intelligence only deal with the end effector position and not its orientation. Our PSO approach provides the convergence of a complete end-effector pose and will be demonstrated using the Baxter Research Robot which has two seven-joint arms although the method is applicable to any general serial robotic manipulator. Speaker(s): , HASAN Houston, Texas, United States