[Paper Review] Finding Shortest Path for Developed Cognitive Map Using Medial Axis

Hazim A. Farhan, Hussein H. Owaied+1 more|arXiv (Cornell University)|Mar 17, 2011

Spatial Cognition and Navigation10 references3 citations

TL;DR

This paper proposes an enhanced medial axis algorithm to compute the shortest path on a developed cognitive map derived from architectural blueprints, using central path pixels to model navigable routes. By filtering out narrow, impractical paths and modeling turns as nodes, the method enables intelligent robots to efficiently navigate buildings with reduced computational overhead and optimized pathfinding performance.

ABSTRACT

this paper presents an enhancement of the medial axis algorithm to be used for finding the optimal shortest path for developed cognitive map. The cognitive map has been developed, based on the architectural blueprint maps. The idea for using the medial-axis is to find main path central pixels; each center pixel represents the center distance between two side boarder pixels. The need for these pixels in the algorithm comes from the need of building a network of nodes for the path, where each node represents a turning in the real world (left, right, critical left, critical right...). The algorithm also ignores from finding the center pixels paths that are too small for intelligent robot navigation. The Idea of this algorithm is to find the possible shortest path between start and end points. The goal of this research is to extract a simple, robust representation of the shape of the cognitive map together with the optimal shortest path between start and end points. The intelligent robot will use this algorithm in order to decrease the time that is needed for sweeping the targeted building.

Motivation & Objective

• To develop a robust, simplified representation of building layouts for intelligent robot navigation.
• To address the challenge of finding optimal shortest paths in complex indoor environments using cognitive maps.
• To reduce pathfinding computation time by filtering out narrow, impractical corridors unsuitable for robot movement.
• To model turning points (e.g., left, right, critical turns) as discrete nodes in a navigational network.
• To integrate medial axis transformation with cognitive mapping for real-time, efficient robot path planning.

Proposed method

• The medial axis of the building's floor plan is computed to identify central path pixels, representing the geometric center of navigable corridors.
• Only paths with sufficient width are retained, excluding narrow corridors that are infeasible for robot navigation.
• Central path pixels are used to construct a network of nodes, where each node corresponds to a turning point in the environment.
• The algorithm models directional changes (left, right, critical turns) as transitions between nodes in the path network.
• A shortest path is computed between start and end points using graph traversal on the constructed node network.
• The method integrates architectural blueprint data into a cognitive map representation suitable for robot path planning.

Experimental results

Research questions

• RQ1 How can the medial axis transform be adapted to extract meaningful navigable paths from architectural blueprints for robot use?
• RQ2 What criteria can be used to filter out narrow, non-navigable corridors from the path network?
• RQ3 How can turning points in a building be effectively modeled as discrete nodes in a path-finding graph?
• RQ4 What is the impact of simplifying the cognitive map representation on pathfinding efficiency and accuracy?
• RQ5 Can the medial axis-based approach outperform traditional pathfinding methods in terms of computational speed and route optimality?

Key findings

• The medial axis transformation successfully identifies central, navigable paths in building floor plans, forming the backbone of the cognitive map.
• The algorithm effectively filters out narrow corridors that are unsuitable for robot movement, improving path feasibility.
• The constructed node network accurately represents turning points, enabling precise path representation and navigation.
• The method reduces computational overhead by focusing only on relevant, wide corridors, enhancing pathfinding speed.
• The approach enables efficient shortest path computation between start and end points, supporting real-time robot navigation in indoor environments.