Authors:
Wei Luo
and
Peter Eberhard
Affiliation:
Institute of Engineering and Computational Mechanics, University of Stuttgart, Pfaffenwaldring 9, 70569 Stuttgart, Germany
Keyword(s):
Goal Intention Evaluation, Monte-Carlo Sampling, Optimization, Trajectory Prediction, Complementary Progress Constraint, Mobile Robot.
Abstract:
In this paper, an optimization-based trajectory prediction enhanced with goal evaluation for omnidirectional mobile robots is proposed. The proposed approach tries to predict the mobile platform’s trajectory based on its previous positions. A two-stage strategy is introduced. At the first stage, the likely goal of the robot in the scenario is evaluated based on an improved Bayesian framework, which also predicts the possible waypoints in a discrete roadmap based on Monte-Carlo sampling in the future. Then, based on the predicted waypoints, an optimization problem is formulated based on the complementary progress constraints, the system dynamics, and the model constraints. After solving the proposed optimization problem, a more reasonable predicted trajectory can be generated. At the end, an experimental scenario is set up, and it is verified with the experimental data, whether the trajectories can be predicted well.