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Sensor-Based Trajectory Planning in Dynamic Environments
Linköping University, Department of Electrical Engineering, Automatic Control.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Motion planning is central to the efficient operation and autonomy of robots in the industry. Generally, motion planning of industrial robots is treated in a two-step approach. First, a geometric path between the start and goal position is planned where the objective is to achieve as short path as possible together with avoiding obstacles. Alternatively, a pre-defined geometric path is provided by the end user. Second, the velocity profile along the geometric path is calculated accounting for system dynamics together with other constraints. This approach is computationally efficient, but yield sub-optimal solutions as the system dynamics is not considered in the first step when the geometric path is planned.

In this thesis, an alternative to the two-step approach is investigated and a trajectory planner is designed and implemented which plans both the geometric path and the velocity profile simultaneously. The motion planning problem is formulated as an optimal control problem, which is solved by a direct collocation method where the trajectory is parametrised by splines, and the spline nodes and knots are used as optimization variables.

The implemented trajectory planner is evaluated in simulations, where the planner is applied to a simple planar elbow robot and ABB's SCARA robot IRB 910SC. Trade-off between computation time and optimality is identified and the results indicate that the trajectory planner yields satisfactory solutions. On the other hand, the simulations indicate that it is not possible to apply the proposed method on a real robot in real-time applications without significant modifications in the implementation to decrease the computation time.

Place, publisher, year, edition, pages
2018. , p. 53
Keywords [en]
Control Engineering, Control Architecture, Motion Control, Trajectory Planning, Path Planning, Optimal Control, Motion Planning, Industrial Robots
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-150040ISRN: LiTH-ISY-EX--18/5164--SEOAI: oai:DiVA.org:liu-150040DiVA, id: diva2:1237343
External cooperation
ABB Robotics
Subject / course
Automatic Control
Presentation
2018-06-21, Algoritmen, 10:15 (Swedish)
Supervisors
Examiners
Available from: 2018-08-09 Created: 2018-08-08 Last updated: 2018-08-09Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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Output format
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