Time optimal motions of mechanical system with a prescribed trajectory

Obradović, Aleksandar; Vukovic, Josif; Mladenović, N Nataša; Mitrović, Zoran

doi:10.1007/s11012-010-9339-3

Cited by 4 publications

(1 citation statement)

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“…Therefore, dynamic techniques will usually result in a faster TOTP solution, compared to kinematic techniques. Several dynamic techniques for time-optimal control have been proposed in the field of robotics [1][2][3][4]. Most of these techniques use bang-bang control, whereby acceleration switching points are identified along a path, at which actuators switch from maximum torque to minimum or vice-versa.…”

Section: Introductionmentioning

confidence: 99%

Time-Optimal Trajectory Planning of Cable-Driven Parallel Mechanisms for Fully Specified Paths With G1-Discontinuities

Barnett

Gosselin

2015

Journal of Dynamic Systems, Measurement, and Control

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Time-optimal trajectory planning (TOTP) is a well-studied problem in robotics and manufacturing, which involves the minimization of the time required for the operation point of a mechanism to follow a path, subject to a set of constraints. A TOTP technique, designed for fully specified paths that include abrupt changes in direction, was previously introduced by the first author of this paper: an incremental approach called minimum-time trajectory shaping (MTTS) was used. In the current paper, MTTS is converted to a dynamic technique and adapted for use with cable-driven parallel robots, which exhibit cable tension and motor torque constraints. For many applications, cable tensions along a path are verified after trajectory generation, rather than imposed during trajectory generation. For the technique proposed in this paper, the cable-tension constraints are imposed directly and fully integrated with MTTS, during trajectory generation, thus maintaining a time-optimal solution. MTTS is applied to a test system and path, and compared to the bang–bang technique. With the same constraints, the results obtained with both techniques are found to be very close. However, MTTS can be applied to a wider variety of paths, and accepts constraints on jerk and total acceleration that would be difficult to apply using the bang–bang approach.

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Section: Introductionmentioning

confidence: 99%