Evaluating Deep Actor–Critic Methods for Path Planning of Mobile Manipulators Under Wheel–Terrain Interaction
The proposed framework incorporates a slip-aware reward formulation that penalizes discrepancies between commanded and measured robot motion while accounting for longitudinal and lateral slip resulting from wheel–terrain interaction, demonstrating the potential of slip-aware RL for coordinated SSMM navigation.
Christian Camacho Morales, Oscar Camacho, Marco Herrera et al.
· Mathematics · 0 citations