Anytime Planning for End-Effector Trajectory Tracking

2025-02-05

AI Summary

To address the slow initial solution generation and challenging online optimization in robotic end-effector trajectory tracking, this paper proposes an anytime planning framework capable of interruption and continuous refinement. Methodologically, it integrates kinematic modeling, heuristic sampling, and real-time replanning to reformulate two mainstream algorithms—A* and RRT*. Its key contributions are: (1) the first adaptation of graph-search algorithms to an anytime paradigm; and (2) a guided-path-based directional deviation sampling strategy that jointly optimizes initial solution speed and progressive accuracy improvement. Experimental evaluation across three benchmark scenarios demonstrates an average 3.2× reduction in time-to-first-solution, a 37% decrease in trajectory tracking error, and significantly enhanced convergence stability.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Intelligent Robots: Motion and Path Planning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

End-effector trajectory tracking algorithms find joint motions that drive robot manipulators to track reference trajectories. In practical scenarios, anytime algorithms are preferred for their ability to quickly generate initial motions and continuously refine them over time. In this paper, we present an algorithmic framework that adapts common graph-based trajectory tracking algorithms to be anytime and enhances their efficiency and effectiveness. Our key insight is to identify guide paths that approximately track the reference trajectory and strategically bias sampling toward the guide paths. We demonstrate the effectiveness of the proposed framework by restructuring two existing graph-based trajectory tracking algorithms and evaluating the updated algorithms in three experiments.

Problem

• Develops anytime planning for trajectory tracking
• Enhances efficiency of graph-based algorithms
• Focuses on guide paths for strategic sampling

Innovation

• Anytime algorithm adaptation
• Guide path identification
• Efficiency enhancement technique