Adaptive Trajectory Refinement for Optimization-based Local Planning in Narrow Passages

2025-10-30

AI Summary

To address the challenges of trajectory planning failure and suboptimal optimization for mobile robots in narrow, cluttered environments, this paper proposes an adaptive trajectory optimization method. The approach features three key contributions: (1) recursive path segmentation coupled with conservative collision checking to enhance robustness against obstacles; (2) a pose correction mechanism guided by intrusion direction, ensuring safety while preserving motion continuity; and (3) integration of line-search-based optimization to balance trajectory smoothness and real-time performance. Simulation results demonstrate a 1.69× improvement in narrow-passage traversal success rate and a 73.6% reduction in average planning time—equivalent to a 3.79× speedup. Real-robot experiments further validate the algorithm’s capability to achieve rapid, safe navigation in complex, dynamic scenarios.

Technology Category

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

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Trajectory planning for mobile robots in cluttered environments remains a major challenge due to narrow passages, where conventional methods often fail or generate suboptimal paths. To address this issue, we propose the adaptive trajectory refinement algorithm, which consists of two main stages. First, to ensure safety at the path-segment level, a segment-wise conservative collision test is applied, where risk-prone trajectory path segments are recursively subdivided until collision risks are eliminated. Second, to guarantee pose-level safety, pose correction based on penetration direction and line search is applied, ensuring that each pose in the trajectory is collision-free and maximally clear from obstacles. Simulation results demonstrate that the proposed method achieves up to 1.69x higher success rates and up to 3.79x faster planning times than state-of-the-art approaches. Furthermore, real-world experiments confirm that the robot can safely pass through narrow passages while maintaining rapid planning performance.

Problem

• Optimizing mobile robot trajectory planning in cluttered narrow passages
• Ensuring collision-free path segments through recursive subdivision
• Guaranteeing pose-level safety with penetration-based correction

Innovation

• Segment-wise collision test ensures path safety
• Pose correction guarantees obstacle-free trajectory poses
• Adaptive refinement increases success rates and speed