An Occupancy Information Grid Model for Path Planning of Intelligent Robots

Abstract

Commonly used robot map models include occupancy grid maps, topological maps, and semantic maps. Among these, an occupancy grid map is mainly represented as a quadrilateral grid. This paper proposes an occupancy information grid for intelligent robots by exploiting the advantages of the occupancy grid map and spatial information grid. In terms of geometric structure, a regular hexagonal grid is used instead of a regular quadrilateral grid. In terms of attribute structure, the single obstacle attribute is replaced by the grid terrain characteristics, grid element attributes, and grid edge attributes. Thus, the occupancy information grid model is transformed into a new data structure describing the spatial environment, and it can be effectively applied to map construction and path planning of intelligent robots. For the map construction application of intelligent robots, this paper describes the basic process of laser sensor-based grid model construction. For the path planning application of intelligent robots, this paper extends the A* algorithm based on a regular hexagonal grid. Additionally, map construction and path planning applications for intelligent robots are experimentally verified. Several experimental results were obtained. First, the experimental results confirmed the theoretical conclusion that the minimum sampling density of the hexagonal structure was 13.4% lower than that of the quadrilateral structure. Second, the regular hexagonal grid is clearly more advantageous in describing environmental scenes, which can ameliorate the "undercompleteness" phenomenon. Third, there were large differences in the planning paths based on two types of grids, as shown by the fact that the distance of the planning paths obtained by the regular hexagonal grid was reduced by at least 10.8% and at most 15.6% compared with the regular quadrilateral grid.

A multi-AGV fast path planning method based on improved CBS algorithm in workshops

“…The modeling methods include grid-based map modeling, topological map modeling, semantic map modeling and geometric map modeling. [6][7][8][9] Among these, grid-based map modeling and topological map modeling are the most commonly used. Topological map uses nodes and edges to describe abstractly the connectivity and topology of the spatial environment.…”

“…The map modeling methods include gridbased map modeling, topological map modeling, semantic map modeling and geometric map modeling. [6][7][8][9] Among them, the grid-based and topological map models are typical. The grid-based map, which is widely used in warehouse systems, discretizes the spatial environment into equally sized grids and then gives each grid a specific attribute.…”

Bidirectional Jump Point Search Path-Planning Algorithm Based on Electricity-Guided Navigation Behavior of Electric Eels and Map Preprocessing

“…The method of building grid maps based on sensor data was originally proposed by Moravec and Elfes [ 27 ], which is widely used in the field of robotics. The method constructs a grid map through the following steps, including acquiring sensor data, constructing the original grid map, correcting the map, optimizing the map, and storing the map [ 28 ]. The method can effectively construct a grid map based on sensor data to support robot navigation and autonomous control.…”

A Navigation-Based Evaluation Metric for Probabilistic Occupancy Grids: Pathfinding Cost Mean Squared Error

“…Only points far away from obstacles are evaluated, and the close-toobstacle ones are purposely ignored, even though they could be critical for navigation. Not relying on a unique topological graph, [17] compared the length of trajectories generated using the A* pathfinding algorithm in order to compare the navigation behavior between hexagonal tiled OGs and squared tiled OGs. While topological approaches are relevant for navigation-based metrics, they are designed for indoor binary OGs, which are different from the probabilistic egocentered use-case.…”