Non-nominal path planning for robust robotic assembly

J. S. Carlson, D. Spensieri, R. Söderberg, R. Bohlin, L. Lindkvist. Journal of manufacturing systems, July 31, 2013, 32(3), 429-435


In manufacturing and assembly processes it is important, in terms of time and money, to verify the feasibility of the operations at the design stage and at early production planning. To achieve that, verification in a virtual environment is often performed by using methods such as path planning and simulation of dimensional variation. Lately, these areas have gained interest both in industry and academia, however, they are almost always treated as separate activities, leading to unnecessary tight tolerances and on-line adjustments.

To resolve this, we present a novel procedure based on the interaction between path planning techniques and variation simulation. This combined tool is able to compute robust assembly paths for industrial robots, i.e. paths less sensitive to the geometrical variation existing in the robot links, in its control system, and in the environment. This may lead to increased productivity and may limit error sources. The main idea to improve robustness is to enable robots to avoid motions in areas with high variation, preferring instead low variation zones. The method is able to deal with the different geometrical variation due to the different robot kinematic configurations. Computing variation might be a computationally expensive task or variation data might be unavailable in the entire state space, therefore three different ways to estimate variation are also proposed and compared. An industrial test case from the automotive industry is successfully studied and the results are presented.


We thank the whole Geometry and Motion Planning Group for useful discussions and help in the implementation. This work was carried out at the Wingquist Laboratory VINN Excellence Centre, and is part of the Sustainable Production Initiative and the Production Area of Advance at Chalmers University of Technology. It was supported by the Swedish Governmental Agency for Innovation Systems.

Authors and Affiliations

  • J. S. Carlson, Fraunhofer-Chalmers Centre
  • D. Spensieri, Fraunhofer-Chalmers Centre
  • R. Söderberg, Wingquist Laboratory, Chalmers University of Technology
  • R. Bohlin, Fraunhofer-Chalmers Centre
  • L. Lindkvist, Wingquist Laboratory, Chalmers University of Technology

Photo credits: Nic McPhee