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