Safe Gap-based Planning in Dynamic Settings

2025-09-08

AI Summary

To address the limitations of conventional local planners—namely, their reliance on empirical robustness and lack of formal modeling of dynamic obstacles—this paper proposes Dynamic Gap Planning (DGP). DGP explicitly models how dynamic obstacles affect the structure of traversable gaps in real time. It integrates polar-coordinate-based gap tracking, future gap propagation, and pursuit-guidance theory to construct a locally reactive trajectory generation framework with formal collision-free guarantees. Key innovations include a novel gap propagation algorithm and a center-of-obstacle-based gap-closure handling mechanism, significantly enhancing safety and robustness in complex dynamic environments. Extensive evaluations across diverse dynamic scenarios demonstrate that DGP outperforms both classical and learning-based planners. Furthermore, DGP has been successfully deployed on a TurtleBot2 platform, validating its effectiveness in real-world obstacle avoidance.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Intelligent Robots: Motion and Path Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Security and Privacy: Large-scale security measurements

Abstract

This chapter extends the family of perception-informed gap-based local planners to dynamic environments. Existing perception-informed local planners that operate in dynamic environments often rely on emergent or empirical robustness for collision avoidance as opposed to performing formal analysis of dynamic obstacles. This proposed planner, dynamic gap, explicitly addresses dynamic obstacles through several steps in the planning pipeline. First, polar regions of free space known as gaps are tracked and their dynamics are estimated in order to understand how the local environment evolves over time. Then, at planning time, gaps are propagated into the future through novel gap propagation algorithms to understand what regions are feasible for passage. Lastly, pursuit guidance theory is leveraged to generate local trajectories that are provably collision-free under ideal conditions. Additionally, obstacle-centric ungap processing is performed in situations where no gaps exist to robustify the overall planning framework. A set of gap-based planners are benchmarked against a series of classical and learned motion planners in dynamic environments, and dynamic gap is shown to outperform all other baselines in all environments. Furthermore, dynamic gap is deployed on a TurtleBot2 platform in several real-world experiments to validate collision avoidance behaviors.

Problem

• Extends gap-based planners to dynamic environments
• Addresses dynamic obstacles with formal analysis
• Ensures collision-free trajectories under ideal conditions

Innovation

• Dynamic gap propagation algorithms
• Pursuit guidance collision-free trajectories
• Obstacle-centric ungap processing framework