Abstract:
Development in pick-and-place robotic manipulators continues to grow as factory processes are
streamlined. One configuration of these manipulators is the two degree of freedom, planar, parallel
manipulator (2DOFPPM). A machine building company, RML Engineering Ltd., wishes to develop custom
robotic manipulators that are optimised for individual pick-and-place applications. This thesis develops
several tools to assist in the design process.
The 2DOFPPM’s structure lends itself to fast and accurate translations in a single plane. However, the
performance of the 2DOFPPM is highly dependent on its dimensions. The kinematics of the 2DOFPPM
are explored and used to examine the reachable workspace of the manipulator. This method of analysis
also gives insight into the relative speed and accuracy of the manipulator’s end-effector in the
workspace.
A simulation model of the 2DOFPPM has been developed in Matlab’s® SimMechanics®. This allows the
detailed analysis of the manipulator’s dynamics. In order to provide meaningful input into the simulation
model, a cubic spline trajectory planner is created. The algorithm uses an iterative approach of
minimising the time between knots along the path, while ensuring the kinematic and dynamic limits of
the motors and end-effector are abided by. The resulting trajectory can be considered near-minimum in
terms of its cycle-time.
The dimensions of the 2DOFPPM have a large effect on the performance of the manipulator. Four major
dimensions are analysed to see the effect each has on the cycle-time over a standardised path. The
dimensions are the proximal and distal arms, spacing of the motors and the height of the manipulator
above the workspace. The solution space of all feasible combinations of these dimensions is produced
revealing cycle-times with a large degree of variation over the same path.
Several optimisation algorithms are applied to finding the manipulator configuration with the fastest
cycle-time. A random restart hill-climber, stochastic hill-climber, simulated annealing and a genetic
algorithm are developed. After each algorithm’s parameters are tuned, the genetic algorithm is shown
to outperform the other techniques.
