Unified Motion Planning Framework for High-Dimensional Robotics
A new framework enhances sampling-based planning by integrating constraints effectively.
This research introduces a motion planning framework that combines equality and inequality constraints into a single geometric formulation for high-dimensional robotic systems. Traditional methods often treat these constraints separately, leading to inefficiencies in exploration.
Riemannian Barrier Metric RRT (RMRRT)
The proposed RMRRT method constructs a unified local geometry for planning on equality-constrained manifolds. It builds an ambient barrier metric from inequality-sensitive terms and induces a tangent-space metric through a (G)-orthogonal projection related to equality constraints.
Key Findings
- RMRRT maintains first-order equality consistency while steering and selecting nearest neighbors.
- It biases exploration away from inequality boundaries, enhancing planning efficiency.
- Experimental results indicate a 100% success rate in various constrained manipulation tasks.
Ablation studies confirm that the new metric improves exploration quality by minimizing rejected samples and reducing path lengths.