LAC-RRT: Constrained Rapidly-Exploring Random Tree With Configuration Transfer Models for Motion Planning

Motion planning is challenging for robotic manipulators. Achieving high efficiency and generalization while considering various constraints simultaneously can be difficult. One common solution is to use iterative projection techniques to acquire feasible manipulator configurations and then search for a valid path to reach the goal state based on those configurations. However, such iterative techniques tend to be computationally expensive and time-consuming. The problem becomes more serious if equality constraints are involved, due to their narrower solution space. In this paper, we propose the Learning-Assisted Constrained Rapidly-Exploring Random Tree (LAC-RRT) algorithm, which employs self-supervised learning to train a model that can directly convert any sampled configuration to a new and valid configuration using feature values constrained by the imposed equality constraints, avoiding the need for iterative optimizations. Unlike other learning-based motion planning techniques, which typically solve the problem by building the constraint manifold based on a fixed set of constraints, LAC-RRT permits better generalization by allowing the equality constraints to be specified at run time. The experimental results show that the proposed LAC-RRT surpasses other approaches in most cases. Specifically, LAC-RRT can significantly reduce computation time by 80-90% for acquiring valid configurations and performing motion planning.