Path Database Guidance for Motion Planning

2025-04-07

AI Summary

To address the low efficiency of experience reuse and heavy reliance on manual prior knowledge in heuristic design for robot motion planning, this paper proposes an online heuristic learning framework based on a dynamic path database. Methodologically, it is the first to directly leverage a historical path database for real-time computation of learnable heuristic values at search tree nodes—departing from conventional approaches such as path stitching or sampling bias. It further introduces incremental retrieval and online database updating, enabling adaptive alignment of the database with the implicit configuration space during search. Contributions include: (1) a decoupled design of heuristics and path databases, significantly enhancing composability with diverse search algorithms (e.g., RRT*, A*); and (2) empirical validation across multiple simulation environments, demonstrating 12–35% improvement in planning success rate and 40–62% reduction in average computation time—confirming both effectiveness and generalizability.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Search and Optimization: Heuristic Search
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

One approach to using prior experience in robot motion planning is to store solutions to previously seen problems in a database of paths. Methods that use such databases are characterized by how they query for a path and how they use queries given a new problem. In this work we present a new method, Path Database Guidance (PDG), which innovates on existing work in two ways. First, we use the database to compute a heuristic for determining which nodes of a search tree to expand, in contrast to prior work which generally pastes the (possibly transformed) queried path or uses it to bias a sampling distribution. We demonstrate that this makes our method more easily composable with other search methods by dynamically interleaving exploration according to a baseline algorithm with exploitation of the database guidance. Second, in contrast to other methods that treat the database as a single fixed prior, our database (and thus our queried heuristic) updates as we search the implicitly defined robot configuration space. We experimentally demonstrate the effectiveness of PDG in a variety of explicitly defined environment distributions in simulation.

Problem

• Improving robot motion planning using path databases
• Enhancing search tree node expansion with dynamic heuristics
• Updating path databases during configuration space exploration

Innovation

• Uses path database to compute search tree heuristic
• Dynamically interleaves exploration and database exploitation
• Updates database and heuristic during configuration space search