Robot trajectory planning using a genetic algorithm

Daniel J. Pack; Gregory J. Toussaint; Randy L. Haupt

doi:10.1117/12.258129

11 November 1996 Robot trajectory planning using a genetic algorithm

Daniel J. Pack, Gregory J. Toussaint, Randy L. Haupt

Proceedings Volume 2824, Adaptive Computing: Mathematical and Physical Methods for Complex Environments; (1996) https://doi.org/10.1117/12.258129
Event: SPIE's 1996 International Symposium on Optical Science, Engineering, and Instrumentation, 1996, Denver, CO, United States

Abstract

In this paper, we studied the trajectory generation problem for a two-degrees-of-freedom robot in a workspace with obstacles. To generate the robot's trajectories, we developed a genetic algorithm to search for valid solutions in the configuration space. Our results present a novel perspective on the problem not seen in the conventional robot trajectory planners. The genetic algorithm approach is beneficial because it may be extended to plan trajectories for robots with more degrees of freedom. The evolutionary search process may allow the user to solve the trajectory problem in an n-dimensional space where the 'curse of dimensionality' inevitably stalls conventional methods. We demonstrate the algorithm with some examples and discuss the possible extension to higher order problems.

Citation Download Citation

Daniel J. Pack, Gregory J. Toussaint, and Randy L. Haupt "Robot trajectory planning using a genetic algorithm", Proc. SPIE 2824, Adaptive Computing: Mathematical and Physical Methods for Complex Environments, (11 November 1996); https://doi.org/10.1117/12.258129

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