Common robotic tracking tasks consist of motions along predefined paths. The design of time-optimal path-constrained trajectories for robotic applications is discussed in this paper. To increase industrial applicability, the proposed method accounts for robot kinematics together with actuator velocity, acceleration and jerk limits instead of accounting for the generally more complex dynamic equations of a manipulator with actuator torque and torque-rate limits. Besides actuator constraints also constraints acting on process level are accounted for. The resulting non-convex optimization problem is solved using a cascade of genetic algorithms and Nelder-Mead’s method. Simulations performed on a Puma 560 manipulator model show that for a proper choice of the kinematic constraints results can be obtained that match the quality of those obtained using the more complex dynamic constraint approach.

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