Data-driven multi-stage multi-objective motion planning of mobile robots, application to near minimum power fuzzy parking

In this paper, the problem of multi-objective trajectory planning is studied for a wheeled mobile robot (WMR) in a crowded environment using a hybrid data-driven neuro-fuzzy system. This approach is composed of two basic stages involving a hard computing through a state space detailed modeling of the system and then a data driven evolutionary neuro-fuzzy system is used for online planning. Typically, a first pre-processing step involves an offline planning generating a large dataset of multi-objective trajectories, optimizing a time-power criterion while including robot, task, and workspace constraints. The discrete augmented Lagrangian is implemented on a decoupled form of the robot dynamics to solve for the resulting non-linear multi-objective optimal control problem. The final state constraint is satisfied with a gradient projection technique. The outcomes of this pre-processing step allow building a genetic neuro-fuzzy inference system to learn and capture the robot multi-objective dynamic behavior. Once this system is trained and optimized, it is used in a generalization phase to achieve online planning. The approach was also applied to the near optimal power fuzzy parking problem. Simulation results showing the effectiveness of the proposal are presented and discussed.

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