Trajectory Planning Method for Mobile Robots Using an RRT-based Dual-Tree Structure

RRT-based Mobile Robot Trajectory Planning Technology Using Dual Trees for Workspace and State

This technology is an RRT-based trajectory planning method that plans paths for mobile robots using a dual-tree structure combining a workspace tree and a state tree. It generates optimal trajectories considering dynamic constraints by identifying neighboring nodes for newly sampled points and selecting parent nodes with optimized costs.

Existing RRT-based path planning techniques have faced limitations in performance optimization due to high computational costs in environments with differential constraints or the complexity of designing distance metrics for searching the nearest nodes.

This technology proposes a method that manages the tree structure by separating the workspace and state, implements logic for searching optimal parent nodes to calculate minimum path costs when sampling new points, and executes node reconnection procedures. It can be applied to autonomous vehicles and non-holonomic robots, finding optimal trajectories while adhering to dynamic constraints without excessive computational burden.

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Key Features:

• Randomly sampling arbitrary new points in the mobile robot's workspace
• Extracting new workspace nodes and adjacent workspace nodes corresponding to the new points
• Extracting state nodes corresponding to the parent nodes of adjacent nodes from the state tree and registering them as candidate state nodes
• Configured to generate trajectories by selecting the optimal parent node with the minimum path cost among candidate state nodes